YOLOv8 OBB and YOLOv8 for Instance Segmentation¶
Oriented object detection goes a step further than object detection and introduces an extra angle to more accurately locate objects in an image.
The output of an oriented object detector is a set of rotated bounding boxes that precisely enclose the objects in the image, along with class labels and confidence scores for each box. Object detection is a good choice when you need to identify objects of interest in a scene, but you don’t need to know exactly where the object is or its exact shape.
Overview of OBB (Oriented Bounding Box) Datasets¶
Training an accurate oriented bounding box (OBB) object detection model requires a comprehensive dataset. This guide explains the various OBB dataset formats supported by Ultralytics YOLO models, providing information on their structure, application, and format conversion methods.
Supported OBB dataset formats¶
YOLO OBB Format
The YOLO OBB format designates bounding boxes by their four corner points with normalized coordinates between 0 and 1. It follows this format:
Internally, YOLO processes the losses and outputs the xywhr which represents the center point (xy), width, height and rotation of the bounding box.
An example of a *.txt for the image above, which contains a class 0 object in OBB format, might look like this:
Models¶
YOLOv8 pre-trained OBB models are shown here, which are pre-trained on the DOTAv1 dataset.
The models are automatically downloaded from the latest version of Ultralytics on first use.
Supported Datasets¶
The following oriented bounding box datasets are currently supported:
DOTA v2: DOTA (A Large-scale Dataset for Object Detection in Aerial Images) version 2 emphasizes detection from aerial perspectives and contains oriented bounding boxes with 1.7 million instances and 11,268 images.
DOTA8: A small subset of 8 images from the full DOTA dataset suitable for testing continuous integration (CI) workflows and verifications of OBB training in the ultralytics repository.
We start by installing the necessary libraries:
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!pip install rasterio
!pip install ultralytics
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Installing collected packages: snuggs, affine, rasterio
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Installing collected packages: thop, ultralytics
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Now, let's connect to Drive and import the functions we're going to use:
In [ ]:
from google.colab import drive
drive.mount('/content/drive')
Mounted at /content/drive
In [ ]:
import rasterio
import geopandas as gpd
import numpy as np
from matplotlib import pyplot as plt
import cv2
from rasterio.features import rasterize
from rasterio.windows import Window
from rasterio.plot import show
import os
from shapely.geometry import box
from shapely.ops import unary_union
import json
from shapely.geometry import shape
from shapely.geometry import Polygon
from shapely.geometry import MultiPolygon
from shapely.ops import unary_union
import shapely
import math
import pandas as pd
from skimage import io
from skimage.io import imsave
from sklearn import model_selection
import os
import shutil
import json
import ast
import numpy as np
from tqdm import tqdm
import pandas as pd
import seaborn as sns
import fastai.vision as vision
import xml.etree.ElementTree as ET
import glob
from shapely.geometry import Polygon
The next step is to set the image path, open it with rasterio and plot it:
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path_img = '/content/drive/MyDrive/Datasets/TL_UAV_Mosaic/Lagoa1.tif'
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src_img = rasterio.open(path_img)
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img = src_img.read()
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img.shape
Out[ ]:
(4, 24885, 27011)
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img = img.transpose([1,2,0])
In [ ]:
plt.figure(figsize=[16,16])
plt.imshow(img)
plt.axis('off')
Out[ ]:
(-0.5, 27010.5, 24884.5, -0.5)
Let's split the image into several image patches with a size of 1024 x 1024:
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if not os.path.isdir('/content/Predict'):
os.mkdir('/content/Predict')
In [ ]:
qtd = 0
out_meta = src_img.meta.copy()
for n in range((src_img.meta['width']//1024)):
for m in range((src_img.meta['height']//1024)):
x = (n*1024)
y = (m*1024)
window = Window(x,y,1024,1024)
win_transform = src_img.window_transform(window)
arr_win = src_img.read(window=window)
arr_win = arr_win[0:3,:,:]
if (arr_win.max() != 0) and (arr_win.shape[1] == 1024) and (arr_win.shape[2] == 1024):
qtd = qtd + 1
path_exp = '/content/Predict/img_' + str(qtd) + '.tif'
out_meta.update({"driver": "GTiff","height": 1024,"width": 1024, "transform":win_transform})
with rasterio.open(path_exp, 'w', **out_meta) as dst:
for i, layer in enumerate(arr_win, start=1):
dst.write_band(i, layer.reshape(-1, layer.shape[-1]))
del arr_win
In [ ]:
print(qtd)
624
Then we convert the tiff image to jpg:
In [ ]:
if not os.path.isdir('/content/Predict_jpg'):
os.mkdir('/content/Predict_jpg')
path_data_pred = '/content/Predict_jpg'
imgs_to_pred = os.listdir('/content/Predict')
for images in imgs_to_pred:
src = rasterio.open('/content/Predict/' + images)
raster = src.read()
raster = raster.transpose([1,2,0])
raster = raster[:,:,0:3]
imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
<ipython-input-14-448461b5bf97>:11: UserWarning: /content/Predict_jpg/img_313.jpg is a low contrast image
imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
<ipython-input-14-448461b5bf97>:11: UserWarning: /content/Predict_jpg/img_607.jpg is a low contrast image
imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
<ipython-input-14-448461b5bf97>:11: UserWarning: /content/Predict_jpg/img_216.jpg is a low contrast image
imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
<ipython-input-14-448461b5bf97>:11: UserWarning: /content/Predict_jpg/img_254.jpg is a low contrast image
imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
<ipython-input-14-448461b5bf97>:11: UserWarning: /content/Predict_jpg/img_507.jpg is a low contrast image
imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
<ipython-input-14-448461b5bf97>:11: UserWarning: /content/Predict_jpg/img_457.jpg is a low contrast image
imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
<ipython-input-14-448461b5bf97>:11: UserWarning: /content/Predict_jpg/img_552.jpg is a low contrast image
imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
<ipython-input-14-448461b5bf97>:11: UserWarning: /content/Predict_jpg/img_277.jpg is a low contrast image
imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
<ipython-input-14-448461b5bf97>:11: UserWarning: /content/Predict_jpg/img_574.jpg is a low contrast image
imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
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imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
Let's import YOLO and use the pre-trained weights from the YOLOv8 OBB:
In [ ]:
from ultralytics import YOLO
In [ ]:
model = YOLO('/content/drive/MyDrive/Datasets/TL_UAV_Mosaic/yolov8x-obb.pt')
In [ ]:
model.predict('/content/Predict_jpg', save=True,save_txt=True, show_conf=False, show_labels=False, imgsz=1024, conf=0.4)
image 1/624 /content/Predict_jpg/img_1.jpg: 1024x1024 (no detections), 16948.1ms
image 2/624 /content/Predict_jpg/img_10.jpg: 1024x1024 (no detections), 11912.8ms
image 3/624 /content/Predict_jpg/img_100.jpg: 1024x1024 (no detections), 14892.3ms
image 4/624 /content/Predict_jpg/img_101.jpg: 1024x1024 (no detections), 12698.2ms
image 5/624 /content/Predict_jpg/img_102.jpg: 1024x1024 12150.1ms
image 6/624 /content/Predict_jpg/img_103.jpg: 1024x1024 12237.4ms
image 7/624 /content/Predict_jpg/img_104.jpg: 1024x1024 (no detections), 11044.8ms
image 8/624 /content/Predict_jpg/img_105.jpg: 1024x1024 (no detections), 12037.0ms
image 9/624 /content/Predict_jpg/img_106.jpg: 1024x1024 (no detections), 13253.3ms
image 10/624 /content/Predict_jpg/img_107.jpg: 1024x1024 (no detections), 12750.9ms
image 11/624 /content/Predict_jpg/img_108.jpg: 1024x1024 12329.1ms
image 12/624 /content/Predict_jpg/img_109.jpg: 1024x1024 (no detections), 11940.9ms
image 13/624 /content/Predict_jpg/img_11.jpg: 1024x1024 (no detections), 12440.0ms
image 14/624 /content/Predict_jpg/img_110.jpg: 1024x1024 (no detections), 12401.4ms
image 15/624 /content/Predict_jpg/img_111.jpg: 1024x1024 11092.4ms
image 16/624 /content/Predict_jpg/img_112.jpg: 1024x1024 11622.2ms
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image 525/624 /content/Predict_jpg/img_571.jpg: 1024x1024 12168.6ms
image 526/624 /content/Predict_jpg/img_572.jpg: 1024x1024 (no detections), 11649.8ms
image 527/624 /content/Predict_jpg/img_573.jpg: 1024x1024 (no detections), 10777.4ms
image 528/624 /content/Predict_jpg/img_574.jpg: 1024x1024 (no detections), 11754.3ms
image 529/624 /content/Predict_jpg/img_575.jpg: 1024x1024 (no detections), 12283.5ms
image 530/624 /content/Predict_jpg/img_576.jpg: 1024x1024 (no detections), 11704.4ms
image 531/624 /content/Predict_jpg/img_577.jpg: 1024x1024 (no detections), 11821.4ms
image 532/624 /content/Predict_jpg/img_578.jpg: 1024x1024 (no detections), 11779.8ms
image 533/624 /content/Predict_jpg/img_579.jpg: 1024x1024 (no detections), 11725.7ms
image 534/624 /content/Predict_jpg/img_58.jpg: 1024x1024 (no detections), 10663.9ms
image 535/624 /content/Predict_jpg/img_580.jpg: 1024x1024 (no detections), 11779.8ms
image 536/624 /content/Predict_jpg/img_581.jpg: 1024x1024 (no detections), 11819.5ms
image 537/624 /content/Predict_jpg/img_582.jpg: 1024x1024 (no detections), 11899.9ms
image 538/624 /content/Predict_jpg/img_583.jpg: 1024x1024 (no detections), 12173.3ms
image 539/624 /content/Predict_jpg/img_584.jpg: 1024x1024 (no detections), 12186.9ms
image 540/624 /content/Predict_jpg/img_585.jpg: 1024x1024 (no detections), 12041.8ms
image 541/624 /content/Predict_jpg/img_586.jpg: 1024x1024 11028.9ms
image 542/624 /content/Predict_jpg/img_587.jpg: 1024x1024 (no detections), 11067.0ms
image 543/624 /content/Predict_jpg/img_588.jpg: 1024x1024 11842.6ms
image 544/624 /content/Predict_jpg/img_589.jpg: 1024x1024 12334.6ms
image 545/624 /content/Predict_jpg/img_59.jpg: 1024x1024 (no detections), 12012.2ms
image 546/624 /content/Predict_jpg/img_590.jpg: 1024x1024 11819.5ms
image 547/624 /content/Predict_jpg/img_591.jpg: 1024x1024 12636.4ms
image 548/624 /content/Predict_jpg/img_592.jpg: 1024x1024 (no detections), 12468.0ms
image 549/624 /content/Predict_jpg/img_593.jpg: 1024x1024 (no detections), 10864.1ms
image 550/624 /content/Predict_jpg/img_594.jpg: 1024x1024 (no detections), 11599.1ms
image 551/624 /content/Predict_jpg/img_595.jpg: 1024x1024 (no detections), 12003.5ms
image 552/624 /content/Predict_jpg/img_596.jpg: 1024x1024 (no detections), 11733.6ms
image 553/624 /content/Predict_jpg/img_597.jpg: 1024x1024 (no detections), 11734.9ms
image 554/624 /content/Predict_jpg/img_598.jpg: 1024x1024 (no detections), 11733.5ms
image 555/624 /content/Predict_jpg/img_599.jpg: 1024x1024 (no detections), 11237.7ms
image 556/624 /content/Predict_jpg/img_6.jpg: 1024x1024 (no detections), 11135.5ms
image 557/624 /content/Predict_jpg/img_60.jpg: 1024x1024 12019.9ms
image 558/624 /content/Predict_jpg/img_600.jpg: 1024x1024 (no detections), 12166.0ms
image 559/624 /content/Predict_jpg/img_601.jpg: 1024x1024 (no detections), 12038.6ms
image 560/624 /content/Predict_jpg/img_602.jpg: 1024x1024 (no detections), 11962.4ms
image 561/624 /content/Predict_jpg/img_603.jpg: 1024x1024 (no detections), 12025.1ms
image 562/624 /content/Predict_jpg/img_604.jpg: 1024x1024 (no detections), 11583.8ms
image 563/624 /content/Predict_jpg/img_605.jpg: 1024x1024 (no detections), 10980.7ms
image 564/624 /content/Predict_jpg/img_606.jpg: 1024x1024 (no detections), 11782.3ms
image 565/624 /content/Predict_jpg/img_607.jpg: 1024x1024 (no detections), 12050.3ms
image 566/624 /content/Predict_jpg/img_608.jpg: 1024x1024 (no detections), 11845.7ms
image 567/624 /content/Predict_jpg/img_609.jpg: 1024x1024 (no detections), 11746.6ms
image 568/624 /content/Predict_jpg/img_61.jpg: 1024x1024 (no detections), 12044.4ms
image 569/624 /content/Predict_jpg/img_610.jpg: 1024x1024 11641.4ms
image 570/624 /content/Predict_jpg/img_611.jpg: 1024x1024 (no detections), 10897.3ms
image 571/624 /content/Predict_jpg/img_612.jpg: 1024x1024 (no detections), 11858.1ms
image 572/624 /content/Predict_jpg/img_613.jpg: 1024x1024 12043.2ms
image 573/624 /content/Predict_jpg/img_614.jpg: 1024x1024 11958.2ms
image 574/624 /content/Predict_jpg/img_615.jpg: 1024x1024 11895.0ms
image 575/624 /content/Predict_jpg/img_616.jpg: 1024x1024 11803.3ms
image 576/624 /content/Predict_jpg/img_617.jpg: 1024x1024 (no detections), 11287.3ms
image 577/624 /content/Predict_jpg/img_618.jpg: 1024x1024 (no detections), 10919.0ms
image 578/624 /content/Predict_jpg/img_619.jpg: 1024x1024 (no detections), 11803.0ms
image 579/624 /content/Predict_jpg/img_62.jpg: 1024x1024 11973.7ms
image 580/624 /content/Predict_jpg/img_620.jpg: 1024x1024 (no detections), 11933.8ms
image 581/624 /content/Predict_jpg/img_621.jpg: 1024x1024 (no detections), 12103.6ms
image 582/624 /content/Predict_jpg/img_622.jpg: 1024x1024 (no detections), 12378.1ms
image 583/624 /content/Predict_jpg/img_623.jpg: 1024x1024 (no detections), 12200.9ms
image 584/624 /content/Predict_jpg/img_624.jpg: 1024x1024 (no detections), 11151.0ms
image 585/624 /content/Predict_jpg/img_63.jpg: 1024x1024 11707.0ms
image 586/624 /content/Predict_jpg/img_64.jpg: 1024x1024 (no detections), 11997.1ms
image 587/624 /content/Predict_jpg/img_65.jpg: 1024x1024 (no detections), 12487.5ms
image 588/624 /content/Predict_jpg/img_66.jpg: 1024x1024 (no detections), 12135.2ms
image 589/624 /content/Predict_jpg/img_67.jpg: 1024x1024 (no detections), 12317.3ms
image 590/624 /content/Predict_jpg/img_68.jpg: 1024x1024 (no detections), 12579.9ms
image 591/624 /content/Predict_jpg/img_69.jpg: 1024x1024 (no detections), 11305.5ms
image 592/624 /content/Predict_jpg/img_7.jpg: 1024x1024 (no detections), 10969.5ms
image 593/624 /content/Predict_jpg/img_70.jpg: 1024x1024 (no detections), 11781.6ms
image 594/624 /content/Predict_jpg/img_71.jpg: 1024x1024 (no detections), 11765.0ms
image 595/624 /content/Predict_jpg/img_72.jpg: 1024x1024 (no detections), 11777.7ms
image 596/624 /content/Predict_jpg/img_73.jpg: 1024x1024 (no detections), 11977.2ms
image 597/624 /content/Predict_jpg/img_74.jpg: 1024x1024 (no detections), 11741.1ms
image 598/624 /content/Predict_jpg/img_75.jpg: 1024x1024 (no detections), 10729.8ms
image 599/624 /content/Predict_jpg/img_76.jpg: 1024x1024 (no detections), 11242.2ms
image 600/624 /content/Predict_jpg/img_77.jpg: 1024x1024 (no detections), 11679.5ms
image 601/624 /content/Predict_jpg/img_78.jpg: 1024x1024 (no detections), 11720.0ms
image 602/624 /content/Predict_jpg/img_79.jpg: 1024x1024 11941.1ms
image 603/624 /content/Predict_jpg/img_8.jpg: 1024x1024 (no detections), 11859.2ms
image 604/624 /content/Predict_jpg/img_80.jpg: 1024x1024 (no detections), 11549.1ms
image 605/624 /content/Predict_jpg/img_81.jpg: 1024x1024 (no detections), 10989.8ms
image 606/624 /content/Predict_jpg/img_82.jpg: 1024x1024 (no detections), 11675.3ms
image 607/624 /content/Predict_jpg/img_83.jpg: 1024x1024 (no detections), 11857.6ms
image 608/624 /content/Predict_jpg/img_84.jpg: 1024x1024 12210.0ms
image 609/624 /content/Predict_jpg/img_85.jpg: 1024x1024 11898.2ms
image 610/624 /content/Predict_jpg/img_86.jpg: 1024x1024 11880.0ms
image 611/624 /content/Predict_jpg/img_87.jpg: 1024x1024 (no detections), 11554.8ms
image 612/624 /content/Predict_jpg/img_88.jpg: 1024x1024 10925.0ms
image 613/624 /content/Predict_jpg/img_89.jpg: 1024x1024 11567.8ms
image 614/624 /content/Predict_jpg/img_9.jpg: 1024x1024 (no detections), 12277.5ms
image 615/624 /content/Predict_jpg/img_90.jpg: 1024x1024 (no detections), 12049.1ms
image 616/624 /content/Predict_jpg/img_91.jpg: 1024x1024 (no detections), 11794.6ms
image 617/624 /content/Predict_jpg/img_92.jpg: 1024x1024 (no detections), 11972.7ms
image 618/624 /content/Predict_jpg/img_93.jpg: 1024x1024 (no detections), 11864.3ms
image 619/624 /content/Predict_jpg/img_94.jpg: 1024x1024 (no detections), 12121.4ms
image 620/624 /content/Predict_jpg/img_95.jpg: 1024x1024 (no detections), 11625.9ms
image 621/624 /content/Predict_jpg/img_96.jpg: 1024x1024 (no detections), 11983.6ms
image 622/624 /content/Predict_jpg/img_97.jpg: 1024x1024 (no detections), 12216.7ms
image 623/624 /content/Predict_jpg/img_98.jpg: 1024x1024 (no detections), 12154.5ms
image 624/624 /content/Predict_jpg/img_99.jpg: 1024x1024 (no detections), 11970.2ms
Speed: 7.4ms preprocess, 11979.0ms inference, 1.4ms postprocess per image at shape (1, 3, 1024, 1024)
Results saved to runs/obb/predict
140 labels saved to runs/obb/predict/labels
Out[ ]:
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keypoints: None
masks: None
names: {0: 'plane', 1: 'ship', 2: 'storage tank', 3: 'baseball diamond', 4: 'tennis court', 5: 'basketball court', 6: 'ground track field', 7: 'harbor', 8: 'bridge', 9: 'large vehicle', 10: 'small vehicle', 11: 'helicopter', 12: 'roundabout', 13: 'soccer ball field', 14: 'swimming pool'}
obb: ultralytics.engine.results.OBB object
orig_img: array([[[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
...,
[255, 255, 255],
[255, 255, 255],
[255, 255, 255]],
[[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
...,
[255, 255, 255],
[255, 255, 255],
[255, 255, 255]],
[[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
...,
[255, 255, 255],
[255, 255, 255],
[255, 255, 255]],
...,
[[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
...,
[255, 255, 255],
[255, 255, 255],
[255, 255, 255]],
[[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
...,
[255, 255, 255],
[255, 255, 255],
[255, 255, 255]],
[[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
...,
[255, 255, 255],
[255, 255, 255],
[255, 255, 255]]], dtype=uint8)
orig_shape: (1024, 1024)
path: '/content/Predict_jpg/img_99.jpg'
probs: None
save_dir: 'runs/obb/predict'
speed: {'preprocess': 5.995035171508789, 'inference': 11970.226287841797, 'postprocess': 0.9756088256835938}]
In [ ]:
from IPython.display import Image, display
for images in glob.glob('/content/runs/obb/predict/*.jpg')[316:320]:
display(Image(filename=images))
With the generated results, we will obtain the coordinates of the bounding boxes and convert them into a polygon with geographic coordinates:
In [ ]:
ls_poly = []
ls_class = []
imgs_to_pred = [f for f in os.listdir('/content/runs/obb/predict/labels/') if f.endswith('.txt')]
for images in imgs_to_pred:
filename = images.split('.')[0]
src = rasterio.open('/content/Predict/' + filename + '.tif')
path = f'/content/runs/obb/predict/labels/'+filename+'.txt'
cols = ['class', 'x1', 'y1', 'x2', 'y2', 'x3', 'y3', 'x4', 'y4']
df = pd.read_csv(path, sep=" ", header=None)
df.columns = cols
df['x1'] = np.round(df['x1'] * 1024)
df['y1'] = np.round(df['y1'] * 1024)
df['x2'] = np.round(df['x2'] * 1024)
df['y2'] = np.round(df['y2'] * 1024)
df['x3'] = np.round(df['x3'] * 1024)
df['y3'] = np.round(df['y3'] * 1024)
df['x4'] = np.round(df['x4'] * 1024)
df['y4'] = np.round(df['y4'] * 1024)
for i,row in df.iterrows():
xs1, ys1 = rasterio.transform.xy(src.transform, row['y1'], row['x1'])
xs2, ys2 = rasterio.transform.xy(src.transform, row['y2'], row['x2'])
xs3, ys3 = rasterio.transform.xy(src.transform, row['y3'], row['x3'])
xs4, ys4 = rasterio.transform.xy(src.transform, row['y4'], row['x4'])
ls_poly.append(Polygon([[xs1, ys1], [xs2,ys2], [xs3, ys3], [xs4,ys4]]))
ls_class.append(row['class'])
Then we create a GeoDataFrame with the polygons:
In [ ]:
gdf = gpd.GeoDataFrame(ls_class, geometry=ls_poly, crs=src.crs)
In [ ]:
gdf.rename(columns={0:'class'}, inplace=True)
In [ ]:
gdf
Out[ ]:
| class | geometry | |
|---|---|---|
| 0 | 11.0 | POLYGON ((-51.71160 -20.78338, -51.71160 -20.7... |
| 1 | 9.0 | POLYGON ((-51.71505 -20.78658, -51.71504 -20.7... |
| 2 | 10.0 | POLYGON ((-51.71170 -20.78445, -51.71168 -20.7... |
| 3 | 10.0 | POLYGON ((-51.71138 -20.78416, -51.71137 -20.7... |
| 4 | 1.0 | POLYGON ((-51.71185 -20.78462, -51.71184 -20.7... |
| ... | ... | ... |
| 348 | 14.0 | POLYGON ((-51.71353 -20.77929, -51.71351 -20.7... |
| 349 | 10.0 | POLYGON ((-51.71566 -20.77914, -51.71565 -20.7... |
| 350 | 9.0 | POLYGON ((-51.71553 -20.77928, -51.71552 -20.7... |
| 351 | 10.0 | POLYGON ((-51.71133 -20.78359, -51.71132 -20.7... |
| 352 | 10.0 | POLYGON ((-51.71118 -20.78351, -51.71118 -20.7... |
353 rows × 2 columns
Finally we can save it in a .shp or .json:
In [ ]:
gdf.to_file('objects.json')
We open the file in QGIS to see the results along with the image:
Instance segmentation¶
Instance segmentation is a computer vision task that involves identifying and delineating individual objects in an image. Unlike semantic segmentation, which classifies each pixel into predefined categories, instance segmentation aims to differentiate and separate object instances from one another.
In instance segmentation, the goal is not only to classify each pixel, but also to assign a unique label or identifier to each distinct object instance. This means that objects of the same class are treated as separate entities. For example, if there are multiple instances of cars in an image, instance segmentation algorithms will assign a unique label to each car, allowing for accurate identification and differentiation.
Why use YOLOv8 instance segmentation?¶
YOLOv8 is a state-of-the-art object detection algorithm known for its high accuracy and real-time performance. It is particularly effective when it comes to instance segmentation, which involves identifying and outlining individual objects in an image. YOLOv8 provides accurate bounding boxes and masks, making it an excellent choice for tasks that require pixel-level analysis.
Segmenting orange trees with YOLOv8¶
Let's start by installing rasterio and importing the necessary libraries:
In [ ]:
!pip install rasterio
Collecting rasterio
Downloading rasterio-1.3.10-cp310-cp310-manylinux2014_x86_64.whl (21.5 MB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 21.5/21.5 MB 34.1 MB/s eta 0:00:00
Collecting affine (from rasterio)
Downloading affine-2.4.0-py3-none-any.whl (15 kB)
Requirement already satisfied: attrs in /usr/local/lib/python3.10/dist-packages (from rasterio) (23.2.0)
Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from rasterio) (2024.2.2)
Requirement already satisfied: click>=4.0 in /usr/local/lib/python3.10/dist-packages (from rasterio) (8.1.7)
Requirement already satisfied: cligj>=0.5 in /usr/local/lib/python3.10/dist-packages (from rasterio) (0.7.2)
Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from rasterio) (1.25.2)
Collecting snuggs>=1.4.1 (from rasterio)
Downloading snuggs-1.4.7-py3-none-any.whl (5.4 kB)
Requirement already satisfied: click-plugins in /usr/local/lib/python3.10/dist-packages (from rasterio) (1.1.1)
Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from rasterio) (67.7.2)
Requirement already satisfied: pyparsing>=2.1.6 in /usr/local/lib/python3.10/dist-packages (from snuggs>=1.4.1->rasterio) (3.1.2)
Installing collected packages: snuggs, affine, rasterio
Successfully installed affine-2.4.0 rasterio-1.3.10 snuggs-1.4.7
In [ ]:
import rasterio
import geopandas as gpd
import numpy as np
from matplotlib import pyplot as plt
import cv2
from rasterio.features import rasterize
from rasterio.windows import Window
import os
from shapely.geometry import box
import pandas as pd
from skimage.io import imsave
from sklearn import model_selection
import os
import shutil
import json
import ast
import numpy as np
from tqdm import tqdm
import pandas as pd
import seaborn as sns
import fastai.vision as vision
import glob
from shapely.geometry import Polygon
Now we connect the Drive and set the paths of the files that will be used:
In [ ]:
from google.colab import drive
drive.mount('/content/drive')
Mounted at /content/drive
In [ ]:
path_img = '/content/drive/MyDrive/Datasets/orange_trees/Orange_trees.tif'
path_shp = '/content/drive/MyDrive/Datasets/orange_trees/orange_trees.shp'
We can open the image and use matplotlib to visualize it:
In [ ]:
label = gpd.read_file(path_shp)
In [ ]:
src = rasterio.open(path_img)
In [ ]:
img = src.read()
In [ ]:
img.shape
Out[ ]:
(3, 5106, 15360)
In [ ]:
img = img.transpose([1,2,0])
In [ ]:
plt.figure(figsize=[16,16])
plt.imshow(img)
plt.axis('off')
Out[ ]:
(-0.5, 15359.5, 5105.5, -0.5)
Let's divide the image into 1024x1024 patches:
In [ ]:
qtd = 0
out_meta = src.meta.copy()
for n in range((src.meta['width']//1024)):
for m in range((src.meta['height']//1024)):
x = (512+(n*1024))
y = (512+(m*1024))
window = Window(x,y,1024,1024)
win_transform = src.window_transform(window)
arr_win = src.read(window=window)
arr_win = arr_win[0:3,:,:]
qtd = qtd + 1
path_exp = '/content/drive/MyDrive/Datasets/orange_trees/data/img_' + str(qtd) + '.tif'
out_meta.update({"driver": "GTiff","height": 1024,"width": 1024, "transform":win_transform})
with rasterio.open(path_exp, 'w', **out_meta) as dst:
for i, layer in enumerate(arr_win, start=1):
dst.write_band(i, layer.reshape(-1, layer.shape[-1]))
del arr_win
In [ ]:
path_data = '/content/drive/MyDrive/Datasets/orange_trees/data/'
In [ ]:
images_files = [f for f in os.listdir(path_data)]
Let's split the training and testing images:
In [ ]:
images_files_train, images_files_valid= model_selection.train_test_split(
images_files,
test_size=0.1,
random_state=42,
shuffle=True,
)
print(len(images_files_train))
print(len(images_files_valid))
54 6
We will get jpg versions of the .tiff images to feed to YOLO
In [ ]:
destination_1 = 'train'
destination_2 = 'validation'
if not os.path.isdir(destination_1):
os.mkdir(destination_1)
if not os.path.isdir(destination_2):
os.mkdir(destination_2)
In [ ]:
path_data_new = '/content/train'
for images in images_files_train:
src = rasterio.open(os.path.join(path_data,images))
raster = src.read()
raster = raster.transpose([1,2,0])
imsave(os.path.join(path_data_new,images.split('.')[0] + '.jpg'), raster)
In [ ]:
path_data_new = '/content/validation'
for images in images_files_valid:
src = rasterio.open(os.path.join(path_data,images))
raster = src.read()
raster = raster.transpose([1,2,0])
imsave(os.path.join(path_data_new,images.split('.')[0] + '.jpg'), raster)
Now let's work with Labels. First we check if the image and the vector have the same coordinate system:
In [ ]:
src.crs
Out[ ]:
CRS.from_epsg(31982)
In [ ]:
label = label.to_crs(src.crs)
In [ ]:
label.crs
Out[ ]:
<Projected CRS: EPSG:31982> Name: SIRGAS 2000 / UTM zone 22S Axis Info [cartesian]: - [east]: Easting (metre) - [north]: Northing (metre) Area of Use: - undefined Coordinate Operation: - name: UTM zone 22S - method: Transverse Mercator Datum: Sistema de Referencia Geocentrico para las AmericaS 2000 - Ellipsoid: GRS 1980 - Prime Meridian: Greenwich
Let's plot the labels:
In [ ]:
fig, ax = plt.subplots(1, 1, figsize=(15, 15))
label.plot(ax = ax)
Out[ ]:
<Axes: >
Now let's create the training and validation datasets with the polygons that intersect each of the images:
In [ ]:
poly_geometry_train = []
img_id_train = []
for fp1 in images_files_train:
src1 = rasterio.open(os.path.join(path_data,fp1))
bounds1 = src1.bounds
df1 = gpd.GeoDataFrame({"id":1,"geometry":[box(*bounds1)]})
df1 = df1.set_crs(epsg=31982)
for i,row in label.iterrows():
intersects = df1['geometry'][0].intersection(row['geometry'])
if (intersects.is_empty == False):
poly_geometry_train.append(intersects)
img_id_train.append(fp1)
In [ ]:
poly_geometry_val = []
img_id_val = []
for fp2 in images_files_valid:
src2 = rasterio.open(os.path.join(path_data,fp2))
bounds2 = src2.bounds
df2 = gpd.GeoDataFrame({"id":1,"geometry":[box(*bounds2)]})
df2 = df2.set_crs(epsg=31982)
for i,row in label.iterrows():
intersects = df2['geometry'][0].intersection(row['geometry'])
if (intersects.is_empty == False):
poly_geometry_val.append(intersects)
img_id_val.append(fp2)
In [ ]:
dataset_train = gpd.GeoDataFrame(geometry=poly_geometry_train)
dataset_val = gpd.GeoDataFrame(geometry=poly_geometry_val)
In [ ]:
dataset_train['ImageId'] = img_id_train
dataset_val['ImageId'] = img_id_val
So we have the dataframe with the geometry of the polygon and the id of the image to which it belongs:
In [ ]:
dataset_val
Out[ ]:
| geometry | ImageId | |
|---|---|---|
| 0 | POLYGON ((621429.317 7741234.534, 621429.441 7... | img_17.tif |
| 1 | POLYGON ((621431.886 7741231.723, 621431.776 7... | img_17.tif |
| 2 | POLYGON ((621436.050 7741230.603, 621435.668 7... | img_17.tif |
| 3 | POLYGON ((621438.937 7741229.644, 621438.970 7... | img_17.tif |
| 4 | POLYGON ((621444.528 7741229.644, 621443.032 7... | img_17.tif |
| ... | ... | ... |
| 58 | POLYGON ((621406.293 7741224.685, 621406.554 7... | img_10.tif |
| 59 | POLYGON ((621409.384 7741222.229, 621409.156 7... | img_10.tif |
| 60 | POLYGON ((621414.105 7741220.207, 621413.972 7... | img_10.tif |
| 61 | POLYGON ((621410.568 7741229.644, 621410.482 7... | img_10.tif |
| 62 | POLYGON ((621414.105 7741229.644, 621414.105 7... | img_10.tif |
63 rows × 2 columns
The next step is to convert the coordinates into x and y values.
In [ ]:
df_train = []
Id_train = []
for i,row in dataset_train.iterrows():
ImageID = row['ImageId'].split('.')[0] + '.jpg'
src1 = rasterio.open(os.path.join(path_data,row['ImageId']))
poly = []
if row.geometry.geom_type == 'Polygon':
for point in list(row.geometry.exterior.coords):
x = point[0]
y = point[1]
row, col = src1.index(x,y)
poly.append(col)
poly.append(row)
Id_train.append(ImageID)
df_train.append(poly)
In [ ]:
df_val = []
Id_val = []
for i,row in dataset_val.iterrows():
ImageID = row['ImageId'].split('.')[0] + '.jpg'
src2 = rasterio.open(os.path.join(path_data,row['ImageId']))
poly = []
if row.geometry.geom_type == 'Polygon':
for point in list(row.geometry.exterior.coords):
x = point[0]
y = point[1]
row, col = src2.index(x,y)
poly.append(col)
poly.append(row)
Id_val.append(ImageID)
df_val.append(poly)
In [ ]:
train_set = pd.DataFrame([])
valid_set = pd.DataFrame([])
In [ ]:
train_set['ImageId'] = Id_train
valid_set['ImageId'] = Id_val
In [ ]:
train_set['geometry'] = df_train
valid_set['geometry'] = df_val
Since we only have the orange tree class, let's configure it.
In [ ]:
train_set['class'] = 0
train_set['class_name'] = 'orange_tree'
valid_set['class'] = 0
valid_set['class_name'] = 'orange_tree'
In [ ]:
train_set
Out[ ]:
| ImageId | geometry | class | class_name | |
|---|---|---|---|---|
| 0 | img_50.jpg | [1024, 1023, 966, 1023, 965, 1022, 939, 1008, ... | 0 | orange_tree |
| 1 | img_50.jpg | [748, 1023, 738, 995, 741, 968, 754, 959, 762,... | 0 | orange_tree |
| 2 | img_50.jpg | [1024, 571, 1019, 575, 1006, 569, 984, 554, 96... | 0 | orange_tree |
| 3 | img_50.jpg | [0, 1023, 0, 1014, 7, 1019, 10, 1023, 0, 1023] | 0 | orange_tree |
| 4 | img_50.jpg | [524, 916, 514, 883, 495, 887, 479, 857, 457, ... | 0 | orange_tree |
| ... | ... | ... | ... | ... |
| 469 | img_55.jpg | [782, 386, 798, 393, 819, 392, 822, 403, 818, ... | 0 | orange_tree |
| 470 | img_55.jpg | [439, 349, 424, 327, 431, 298, 441, 270, 462, ... | 0 | orange_tree |
| 471 | img_55.jpg | [219, 254, 225, 227, 207, 218, 199, 208, 201, ... | 0 | orange_tree |
| 472 | img_55.jpg | [0, 0, 121, 0, 124, 24, 126, 64, 120, 87, 107,... | 0 | orange_tree |
| 473 | img_55.jpg | [1024, 0, 1024, 33, 1008, 41, 986, 53, 954, 41... | 0 | orange_tree |
474 rows × 4 columns
After that we create the .csv files to use in YOLOv5:
In [ ]:
def convert(data, data_type):
df = data.groupby('ImageId')['geometry'].apply(list).reset_index(name='bboxes')
df['classes'] = data.groupby('ImageId')['class'].apply(list).reset_index(drop=True)
df.to_csv(data_type + '.csv', index=False)
print(data_type)
print(df.shape)
print(df.head())
df_train = convert(train_set, '/content/train')
df_valid = convert(valid_set, '/content/validation')
/content/train
(52, 3)
ImageId bboxes \
0 img_1.jpg [[32, 1024, 37, 1014, 58, 947, 77, 931, 99, 92...
1 img_11.jpg [[0, 789, 19, 781, 44, 782, 57, 807, 79, 809, ...
2 img_12.jpg [[0, 894, 1, 889, 34, 864, 54, 836, 77, 835, 1...
3 img_13.jpg [[0, 1024, 0, 968, 0, 968, 11, 990, 16, 1002, ...
4 img_14.jpg [[0, 1023, 0, 1006, 25, 997, 46, 991, 98, 1000...
classes
0 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
1 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
2 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
3 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
4 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
/content/validation
(6, 3)
ImageId bboxes \
0 img_10.jpg [[0, 787, 17, 785, 51, 817, 62, 878, 59, 899, ...
1 img_17.jpg [[0, 694, 8, 699, 0, 708, 0, 694], [172, 884, ...
2 img_2.jpg [[0, 922, 27, 935, 44, 993, 29, 1004, 0, 1000,...
3 img_22.jpg [[0, 457, 11, 457, 25, 486, 52, 476, 72, 485, ...
4 img_30.jpg [[0, 578, 7, 576, 37, 589, 55, 573, 105, 564, ...
classes
0 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
1 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
2 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
3 [0, 0, 0, 0, 0, 0, 0, 0, 0]
4 [0, 0, 0, 0, 0, 0, 0]
Now is the time to prepare the environment to use YOLOv8. We will install the Ultralytics library and import YOLO
In [ ]:
!pip install ultralytics
from IPython import display
import ultralytics
ultralytics.checks()
Ultralytics YOLOv8.1.47 🚀 Python-3.10.12 torch-2.2.1+cu121 CPU (Intel Xeon 2.20GHz) Setup complete ✅ (2 CPUs, 12.7 GB RAM, 29.4/225.8 GB disk)
In [ ]:
from ultralytics import YOLO
In [ ]:
import os
HOME = os.getcwd()
print(HOME)
/content
Inside the yolo folder, we create the plant_data folder to store our data.
In [ ]:
!mkdir plant_data
%cd plant_data
/content/plant_data
In [ ]:
!mkdir images
!mkdir labels
%cd images
!mkdir train
!mkdir validation
%cd ..
%cd labels
!mkdir train
!mkdir validation
%cd ..
%cd ..
/content/plant_data/images /content/plant_data /content/plant_data/labels /content/plant_data /content
Let's copy the images and create a .txt for each image with boundaries inside the folder of our project that we just created:
In [ ]:
INPUT_PATH = '/content/'
OUTPUT_PATH = '/content/plant_data'
def process_data(data, data_type='train'):
for _, row in tqdm(data.iterrows(), total = len(data)):
image_name = row['ImageId'].split('.')[0]
bounding_boxes = row['bboxes']
classes = row['classes']
yolo_data = []
for bbox, Class in zip(bounding_boxes, classes):
norm_bbox = [x/1024 for x in bbox]
yolo_data.append([Class]+ norm_bbox)
lst_fmt = ['%f'] * len(norm_bbox)
fmt_full = ['%d'] + lst_fmt
with open(os.path.join(OUTPUT_PATH, f"labels/{data_type}/{image_name}.txt"), "w") as f:
for row in yolo_data:
for el in row:
f.write("%s" %el + ' ')
f.write('\n')
f.close()
shutil.copyfile(
os.path.join(INPUT_PATH, f"{data_type}/{image_name}.jpg"),
os.path.join(OUTPUT_PATH, f"images/{data_type}/{image_name}.jpg")
)
df_train = pd.read_csv('/content/train.csv')
df_train.bboxes = df_train.bboxes.apply(ast.literal_eval)
df_train.classes = df_train.classes.apply(ast.literal_eval)
df_valid = pd.read_csv('/content/validation.csv')
df_valid.bboxes = df_valid.bboxes.apply(ast.literal_eval)
df_valid.classes = df_valid.classes.apply(ast.literal_eval)
process_data(df_train, data_type='train')
process_data(df_valid, data_type='validation')
100%|██████████| 52/52 [00:00<00:00, 238.22it/s] 100%|██████████| 6/6 [00:00<00:00, 258.42it/s]
Here we can check if the .txt was created correctly:
In [ ]:
f = open('/content/plant_data/labels/train/'+os.listdir("/content/plant_data/labels/train/")[10])
print(f.name)
for l in f:
print(l)
/content/plant_data/labels/train/img_26.txt 0 0.0 0.78125 0.01171875 0.775390625 0.03515625 0.7607421875 0.0634765625 0.7490234375 0.0712890625 0.7646484375 0.08984375 0.78125 0.1005859375 0.787109375 0.1435546875 0.7568359375 0.150390625 0.7666015625 0.1318359375 0.7919921875 0.1298828125 0.8115234375 0.166015625 0.818359375 0.173828125 0.8564453125 0.1845703125 0.8740234375 0.1513671875 0.8876953125 0.1689453125 0.9072265625 0.1787109375 0.921875 0.150390625 0.9365234375 0.1142578125 0.958984375 0.0712890625 0.9619140625 0.029296875 0.9375 0.01171875 0.90625 0.0 0.888671875 0.0 0.8271484375 0.0068359375 0.8212890625 0.0 0.8173828125 0.0 0.78125 0 0.25390625 0.9990234375 0.25 0.9892578125 0.2373046875 0.966796875 0.2392578125 0.9453125 0.234375 0.927734375 0.2294921875 0.8994140625 0.2314453125 0.8662109375 0.2490234375 0.853515625 0.2783203125 0.8525390625 0.2861328125 0.8681640625 0.291015625 0.8662109375 0.3076171875 0.859375 0.3408203125 0.8681640625 0.3525390625 0.884765625 0.3828125 0.8876953125 0.4208984375 0.9111328125 0.408203125 0.9599609375 0.3857421875 0.9873046875 0.3662109375 0.9990234375 0.25390625 0.9990234375 0 0.513671875 0.9990234375 0.53125 0.9677734375 0.5625 0.958984375 0.583984375 0.9619140625 0.59765625 0.974609375 0.640625 0.95703125 0.6591796875 0.9716796875 0.66015625 0.9873046875 0.65234375 0.9951171875 0.630859375 0.994140625 0.630859375 0.9990234375 0.513671875 0.9990234375 0 0.0 0.212890625 0.00390625 0.2099609375 0.0166015625 0.2236328125 0.0166015625 0.2431640625 0.0361328125 0.240234375 0.0576171875 0.2666015625 0.080078125 0.2802734375 0.099609375 0.298828125 0.07421875 0.341796875 0.052734375 0.3359375 0.046875 0.3642578125 0.029296875 0.3935546875 0.0 0.3955078125 0.0 0.212890625 0 0.2451171875 0.4873046875 0.21875 0.4892578125 0.2060546875 0.4716796875 0.21875 0.45703125 0.1943359375 0.453125 0.19921875 0.4189453125 0.2001953125 0.404296875 0.2138671875 0.392578125 0.220703125 0.3701171875 0.220703125 0.3486328125 0.21875 0.3134765625 0.2392578125 0.3134765625 0.26953125 0.3154296875 0.2958984375 0.3408203125 0.3125 0.35546875 0.33203125 0.3642578125 0.3349609375 0.376953125 0.3603515625 0.3857421875 0.35546875 0.4052734375 0.3408203125 0.44140625 0.33203125 0.43359375 0.32421875 0.4501953125 0.3193359375 0.4755859375 0.302734375 0.482421875 0.2880859375 0.5 0.251953125 0.490234375 0.2451171875 0.4873046875 0 0.4931640625 0.5615234375 0.4677734375 0.537109375 0.4365234375 0.5048828125 0.443359375 0.4873046875 0.4609375 0.4453125 0.4912109375 0.4365234375 0.53515625 0.4140625 0.5625 0.40234375 0.5771484375 0.416015625 0.55859375 0.4345703125 0.5927734375 0.4345703125 0.62109375 0.4296875 0.6494140625 0.4619140625 0.6669921875 0.4736328125 0.654296875 0.49609375 0.634765625 0.5458984375 0.6005859375 0.5498046875 0.6025390625 0.58203125 0.5771484375 0.595703125 0.5361328125 0.5673828125 0.4931640625 0.5615234375 0 0.7529296875 0.6103515625 0.7421875 0.5859375 0.716796875 0.6044921875 0.7099609375 0.6005859375 0.7109375 0.587890625 0.7265625 0.564453125 0.69140625 0.5615234375 0.6904296875 0.5390625 0.7197265625 0.53125 0.7294921875 0.5224609375 0.7294921875 0.50390625 0.7255859375 0.478515625 0.7392578125 0.46875 0.7783203125 0.462890625 0.79296875 0.484375 0.8173828125 0.484375 0.8359375 0.486328125 0.8564453125 0.4775390625 0.8681640625 0.5078125 0.861328125 0.5205078125 0.8857421875 0.5537109375 0.8837890625 0.576171875 0.857421875 0.6064453125 0.8076171875 0.625 0.7705078125 0.6279296875 0.7529296875 0.6103515625 0 1.0 0.6923828125 0.962890625 0.6826171875 0.951171875 0.65234375 0.9521484375 0.6318359375 0.96484375 0.583984375 0.98828125 0.5693359375 1.0 0.564453125 1.0 0.6923828125 0 0.6572265625 0.0 0.6328125 0.0244140625 0.662109375 0.0341796875 0.66796875 0.0458984375 0.658203125 0.0625 0.623046875 0.046875 0.609375 0.0615234375 0.576171875 0.056640625 0.5673828125 0.03515625 0.5361328125 0.037109375 0.5 0.029296875 0.4921875 0.0 0.6572265625 0.0 0 0.890625 0.0 0.8916015625 0.0048828125 0.91796875 0.03515625 0.9580078125 0.0517578125 0.951171875 0.076171875 0.9248046875 0.0908203125 0.9052734375 0.125 0.87890625 0.1259765625 0.837890625 0.1337890625 0.7900390625 0.1220703125 0.759765625 0.08203125 0.71875 0.09765625 0.7177734375 0.0673828125 0.7421875 0.064453125 0.734375 0.0390625 0.7236328125 0.0087890625 0.7431640625 0.0 0.890625 0.0 0 1.0 0.12109375 0.9990234375 0.1201171875 0.9970703125 0.0791015625 0.99609375 0.0537109375 1.0 0.0556640625 1.0 0.12109375
Let's visualize an image and the mask with the instances:
In [ ]:
from typing import Tuple, List
import cv2
import numpy as np
import matplotlib.pyplot as plt
from pathlib import Path
from time import time
from rich import print
In [ ]:
def read_image_label(path_to_img: str, path_to_txt: str, normalize: bool = False) -> Tuple[np.array, np.array]:
# read image
image = cv2.imread(path_to_img)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
img_h, img_w = image.shape[:2]
# read .txt file for this image
with open(path_to_txt, "r") as f:
txt_file = f.readlines()
polygon_list = []
for line in txt_file:
line_split = line.split()
cls_idx = line_split[0]
coords = line_split[1:]
polygon = np.array([[eval(x), eval(y)] for x, y in zip(coords[0::2], coords[1::2])]) # convert list of coordinates to numpy massive
# Convert normilized coordinates of polygons to coordinates of image
if normalize:
polygon[:,0] = polygon[:,0]*img_w
polygon[:,1] = polygon[:,1]*img_h
polygon_list.append(polygon.astype(np.int32))
return image, polygon_list
def show_image_mask(img: np.array, polygon_list: np.array, alpha: float = 0.7):
# Create zero array for mask
mask = np.zeros((img.shape[0], img.shape[1]), dtype=np.uint8)
overlay = img.copy()
# Draw polygon on the image and mask
for polygon in polygon_list:
cv2.fillPoly(mask, pts=[polygon], color=(255, 255, 255))
cv2.fillPoly(img, pts=[polygon], color=(255, 0, 0))
# Plot image with mask
fig = plt.figure(figsize=(22,18))
axes = fig.subplots(nrows=1, ncols=2)
axes[0].imshow(img)
axes[1].imshow(mask, cmap="Greys_r")
axes[0].set_title("Imagen original con la máscara")
axes[1].set_title("Máscara")
plt.show()
In [ ]:
image, polygon = read_image_label(path_to_img='/content/plant_data/images/train/img_11.jpg',
path_to_txt='/content/plant_data/labels/train/img_11.txt',
normalize=True)
In [ ]:
show_image_mask(img=image, polygon_list=polygon)
Finally, we will create the yaml with our project information and run the train.py file.
In [ ]:
%%writefile orange_tree.yaml
train: /content/plant_data/images/train
val: /content/plant_data/images/validation
nc: 1
names: ['Orange Tree']
Writing orange_tree.yaml
In [ ]:
%cd {HOME}
!yolo task=segment mode=train model=yolov8s-seg.pt data=/content/orange_tree.yaml epochs=25 imgsz=1024
/content Downloading https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8s-seg.pt to 'yolov8s-seg.pt'... 100% 22.8M/22.8M [00:00<00:00, 325MB/s] Ultralytics YOLOv8.1.9 🚀 Python-3.10.12 torch-2.1.0+cu121 CUDA:0 (Tesla T4, 15102MiB) engine/trainer: task=segment, mode=train, model=yolov8s-seg.pt, data=/content/orange_tree.yaml, epochs=25, time=None, patience=50, batch=16, imgsz=1024, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs/segment/train Downloading https://ultralytics.com/assets/Arial.ttf to '/root/.config/Ultralytics/Arial.ttf'... 100% 755k/755k [00:00<00:00, 103MB/s] 2024-02-03 16:19:21.816673: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered 2024-02-03 16:19:21.816740: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered 2024-02-03 16:19:21.818374: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered Overriding model.yaml nc=80 with nc=1 from n params module arguments 0 -1 1 928 ultralytics.nn.modules.conv.Conv [3, 32, 3, 2] 1 -1 1 18560 ultralytics.nn.modules.conv.Conv [32, 64, 3, 2] 2 -1 1 29056 ultralytics.nn.modules.block.C2f [64, 64, 1, True] 3 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2] 4 -1 2 197632 ultralytics.nn.modules.block.C2f [128, 128, 2, True] 5 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2] 6 -1 2 788480 ultralytics.nn.modules.block.C2f [256, 256, 2, True] 7 -1 1 1180672 ultralytics.nn.modules.conv.Conv [256, 512, 3, 2] 8 -1 1 1838080 ultralytics.nn.modules.block.C2f [512, 512, 1, True] 9 -1 1 656896 ultralytics.nn.modules.block.SPPF [512, 512, 5] 10 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 11 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] 12 -1 1 591360 ultralytics.nn.modules.block.C2f [768, 256, 1] 13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 14 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] 15 -1 1 148224 ultralytics.nn.modules.block.C2f [384, 128, 1] 16 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2] 17 [-1, 12] 1 0 ultralytics.nn.modules.conv.Concat [1] 18 -1 1 493056 ultralytics.nn.modules.block.C2f [384, 256, 1] 19 -1 1 590336 ultralytics.nn.modules.conv.Conv [256, 256, 3, 2] 20 [-1, 9] 1 0 ultralytics.nn.modules.conv.Concat [1] 21 -1 1 1969152 ultralytics.nn.modules.block.C2f [768, 512, 1] 22 [15, 18, 21] 1 2770931 ultralytics.nn.modules.head.Segment [1, 32, 128, [128, 256, 512]] YOLOv8s-seg summary: 261 layers, 11790483 parameters, 11790467 gradients, 42.7 GFLOPs Transferred 411/417 items from pretrained weights TensorBoard: Start with 'tensorboard --logdir runs/segment/train', view at http://localhost:6006/ Freezing layer 'model.22.dfl.conv.weight' AMP: running Automatic Mixed Precision (AMP) checks with YOLOv8n... Downloading https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8n.pt to 'yolov8n.pt'... 100% 6.23M/6.23M [00:00<00:00, 287MB/s] AMP: checks passed ✅ train: Scanning /content/plant_data/labels/train... 52 images, 0 backgrounds, 0 corrupt: 100% 52/52 [00:00<00:00, 788.50it/s] train: New cache created: /content/plant_data/labels/train.cache albumentations: Blur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8)) val: Scanning /content/plant_data/labels/validation... 6 images, 0 backgrounds, 0 corrupt: 100% 6/6 [00:00<00:00, 426.89it/s] val: New cache created: /content/plant_data/labels/validation.cache Plotting labels to runs/segment/train/labels.jpg... optimizer: 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... optimizer: AdamW(lr=0.002, momentum=0.9) with parameter groups 66 weight(decay=0.0), 77 weight(decay=0.0005), 76 bias(decay=0.0) TensorBoard: model graph visualization added ✅ Image sizes 1024 train, 1024 val Using 8 dataloader workers Logging results to runs/segment/train Starting training for 25 epochs... Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size 1/25 12.6G 1.539 4.007 3.005 1.579 39 1024: 100% 4/4 [00:08<00:00, 2.19s/it] Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 1/1 [00:01<00:00, 1.18s/it] all 6 58 0.764 0.672 0.754 0.549 0.764 0.672 0.754 0.527 Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size 2/25 12.7G 1.486 3.406 2.836 1.532 51 1024: 100% 4/4 [00:03<00:00, 1.22it/s] Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 1.97it/s] all 6 58 0.988 0.81 0.871 0.598 0.988 0.81 0.867 0.603 Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size 3/25 12.7G 1.179 2.243 1.694 1.376 46 1024: 100% 4/4 [00:03<00:00, 1.30it/s] Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 2.74it/s] all 6 58 0.895 0.586 0.723 0.549 0.895 0.586 0.717 0.524 Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size 4/25 13.5G 1.069 1.789 1.345 1.214 69 1024: 100% 4/4 [00:03<00:00, 1.11it/s] Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 3.96it/s] all 6 58 0.667 0.793 0.664 0.44 0.667 0.793 0.663 0.454 Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size 5/25 13.1G 1.015 1.843 1.096 1.108 58 1024: 100% 4/4 [00:03<00:00, 1.25it/s] Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 3.67it/s] all 6 58 0.803 0.845 0.808 0.605 0.803 0.845 0.794 0.56 Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size 6/25 13.5G 1.009 1.675 1.083 1.128 49 1024: 100% 4/4 [00:03<00:00, 1.25it/s] Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 4.62it/s] all 6 58 0.797 0.948 0.831 0.555 0.783 0.931 0.8 0.56 Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size 7/25 13G 0.9327 1.547 0.8792 1.096 63 1024: 100% 4/4 [00:02<00:00, 1.33it/s] Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 4.39it/s] all 6 58 0.667 0.862 0.692 0.489 0.667 0.862 0.692 0.486 Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size 8/25 12.8G 0.9161 1.51 0.8808 1.075 53 1024: 100% 4/4 [00:03<00:00, 1.27it/s] Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 2.60it/s] all 6 58 1 0.862 0.975 0.692 1 0.862 0.975 0.695 Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size 9/25 12.8G 0.9987 1.697 0.8892 1.109 58 1024: 100% 4/4 [00:03<00:00, 1.31it/s] Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 3.47it/s] all 6 58 0.902 0.966 0.967 0.759 0.902 0.966 0.967 0.719 Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size 10/25 13.2G 0.9401 1.559 0.8152 1.091 54 1024: 100% 4/4 [00:02<00:00, 1.37it/s] Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 5.14it/s] all 6 58 0.938 0.966 0.976 0.737 0.938 0.966 0.975 0.73 Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size 11/25 13.4G 0.9101 1.517 0.7124 1.055 56 1024: 100% 4/4 [00:03<00:00, 1.31it/s] Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 4.33it/s] all 6 58 0.964 0.966 0.976 0.738 0.964 0.966 0.976 0.721 Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size 12/25 12.8G 0.9257 1.606 0.764 1.083 59 1024: 100% 4/4 [00:02<00:00, 1.38it/s] Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 2.47it/s] all 6 58 0.967 0.948 0.973 0.739 0.967 0.948 0.973 0.713 Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size 13/25 13.6G 0.9728 1.697 0.7117 1.134 41 1024: 100% 4/4 [00:03<00:00, 1.26it/s] Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 5.10it/s] all 6 58 0.932 0.948 0.965 0.731 0.949 0.966 0.973 0.735 Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size 14/25 13.5G 1.001 1.673 0.7173 1.111 63 1024: 100% 4/4 [00:03<00:00, 1.32it/s] Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 3.91it/s] all 6 58 0.982 0.93 0.98 0.724 0.982 0.93 0.973 0.725 Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size 15/25 13G 0.9212 1.64 0.6394 1.079 66 1024: 100% 4/4 [00:03<00:00, 1.29it/s] Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 4.59it/s] all 6 58 0.917 0.959 0.979 0.715 0.917 0.959 0.958 0.698 Closing dataloader mosaic albumentations: Blur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8)) Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size 16/25 12.3G 0.8627 1.43 0.9406 1.08 38 1024: 100% 4/4 [00:05<00:00, 1.33s/it] Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 1.33it/s] all 6 58 0.946 0.948 0.985 0.736 0.946 0.948 0.97 0.706 Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size 17/25 12.4G 0.9163 1.5 0.7949 1.11 37 1024: 100% 4/4 [00:02<00:00, 1.51it/s] Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 5.18it/s] all 6 58 0.992 0.948 0.989 0.744 0.992 0.948 0.968 0.709 Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size 18/25 12.4G 0.8982 1.532 0.7079 1.112 27 1024: 100% 4/4 [00:02<00:00, 1.50it/s] Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 5.29it/s] all 6 58 1 0.961 0.991 0.763 0.996 0.948 0.974 0.754 Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size 19/25 12.6G 0.899 1.472 0.714 1.074 35 1024: 100% 4/4 [00:02<00:00, 1.52it/s] Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 5.22it/s] all 6 58 1 0.946 0.987 0.78 0.996 0.931 0.974 0.757 Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size 20/25 12.6G 0.7926 1.385 0.6322 1.032 32 1024: 100% 4/4 [00:02<00:00, 1.53it/s] Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 5.24it/s] all 6 58 0.997 0.931 0.984 0.787 0.997 0.931 0.981 0.762 Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size 21/25 12.5G 0.8438 1.525 0.6871 1.044 32 1024: 100% 4/4 [00:02<00:00, 1.49it/s] Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 5.28it/s] all 6 58 0.979 0.948 0.981 0.797 0.979 0.948 0.985 0.765 Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size 22/25 12.6G 0.81 1.359 0.5854 1.014 35 1024: 100% 4/4 [00:02<00:00, 1.49it/s] Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 5.07it/s] all 6 58 0.98 0.948 0.982 0.799 0.98 0.948 0.988 0.757 Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size 23/25 12.5G 0.8035 1.372 0.6286 1.029 29 1024: 100% 4/4 [00:02<00:00, 1.50it/s] Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 5.23it/s] all 6 58 0.979 0.948 0.984 0.791 0.979 0.948 0.987 0.748 Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size 24/25 12.6G 0.7782 1.345 0.5861 1.015 35 1024: 100% 4/4 [00:02<00:00, 1.47it/s] Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 5.37it/s] all 6 58 0.982 0.948 0.987 0.795 0.982 0.948 0.987 0.762 Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size 25/25 12.5G 0.8061 1.436 0.5767 1.015 36 1024: 100% 4/4 [00:02<00:00, 1.51it/s] Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 5.41it/s] all 6 58 0.982 0.946 0.989 0.791 0.982 0.946 0.989 0.771 25 epochs completed in 0.033 hours. Optimizer stripped from runs/segment/train/weights/last.pt, 23.9MB Optimizer stripped from runs/segment/train/weights/best.pt, 23.9MB Validating runs/segment/train/weights/best.pt... Ultralytics YOLOv8.1.9 🚀 Python-3.10.12 torch-2.1.0+cu121 CUDA:0 (Tesla T4, 15102MiB) YOLOv8s-seg summary (fused): 195 layers, 11779987 parameters, 0 gradients, 42.4 GFLOPs Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 5.81it/s] all 6 58 0.982 0.946 0.989 0.79 0.982 0.946 0.989 0.77 Speed: 0.5ms preprocess, 14.3ms inference, 0.0ms loss, 0.9ms postprocess per image Results saved to runs/segment/train 💡 Learn more at https://docs.ultralytics.com/modes/train
After finishing the training, we can observe the statistics:
In [ ]:
from IPython.display import display, Image
Image(filename=f'{HOME}/runs/segment/train/confusion_matrix.png', width=600)
Out[ ]:
In [ ]:
Image(filename=f'{HOME}/runs/segment/train/results.png', width=600)
Out[ ]:
So, let's detect orange trees in our validation images:
In [ ]:
!yolo task=segment mode=predict model=/content/runs/segment/train/weights/best.pt conf=0.50 source= /content/plant_data/images/validation save=true save_txt=true
Ultralytics YOLOv8.1.9 🚀 Python-3.10.12 torch-2.1.0+cu121 CUDA:0 (Tesla T4, 15102MiB)
YOLOv8s-seg summary (fused): 195 layers, 11779987 parameters, 0 gradients, 42.4 GFLOPs
image 1/6 /content/plant_data/images/validation/img_1.jpg: 1024x1024 11 Orange Trees, 43.2ms
image 2/6 /content/plant_data/images/validation/img_14.jpg: 1024x1024 11 Orange Trees, 43.1ms
image 3/6 /content/plant_data/images/validation/img_38.jpg: 1024x1024 11 Orange Trees, 43.1ms
image 4/6 /content/plant_data/images/validation/img_47.jpg: 1024x1024 8 Orange Trees, 42.5ms
image 5/6 /content/plant_data/images/validation/img_55.jpg: 1024x1024 8 Orange Trees, 40.7ms
image 6/6 /content/plant_data/images/validation/img_6.jpg: 1024x1024 8 Orange Trees, 40.7ms
Speed: 3.7ms preprocess, 42.2ms inference, 86.9ms postprocess per image at shape (1, 3, 1024, 1024)
Results saved to runs/segment/predict
6 labels saved to runs/segment/predict/labels
💡 Learn more at https://docs.ultralytics.com/modes/predict
In [ ]:
for image_path in glob.glob(f'{HOME}/runs/segment/predict/*.jpg')[:5]:
display(Image(filename=image_path, height=600))
print("\n")
We can also apply the trained model to the entire image. Let's divide the image into several parts and apply the trained model:
In [ ]:
if not os.path.isdir('/content/Predict'):
os.mkdir('/content/Predict')
In [ ]:
src = rasterio.open(path_img)
In [ ]:
qtd = 0
out_meta = src.meta.copy()
for n in range((src.meta['width']//1024)):
for m in range((src.meta['height']//1024)):
x = (n*1024)
y = (m*1024)
window = Window(x,y,1024,1024)
win_transform = src.window_transform(window)
arr_win = src.read(window=window)
arr_win = arr_win[0:3,:,:]
qtd = qtd + 1
path_exp = '/content/Predict/img_' + str(qtd) + '.tif'
out_meta.update({"driver": "GTiff","height": 1024,"width": 1024, "transform":win_transform})
with rasterio.open(path_exp, 'w', **out_meta) as dst:
for i, layer in enumerate(arr_win, start=1):
dst.write_band(i, layer.reshape(-1, layer.shape[-1]))
del arr_win
In [ ]:
if not os.path.isdir('/content/Predict_jpg'):
os.mkdir('/content/Predict_jpg')
path_data_pred = '/content/Predict_jpg'
imgs_to_pred = os.listdir('/content/Predict')
for images in imgs_to_pred:
src = rasterio.open('/content/Predict/' + images)
raster = src.read()
raster = raster.transpose([1,2,0])
raster = raster[:,:,0:3]
imsave(os.path.join(path_data_pred,images.split('.')[0] + '.jpg'), raster)
In [ ]:
!yolo task=segment mode=predict model=/content/runs/segment/train/weights/best.pt conf=0.30 source= /content/Predict_jpg save=true save_txt=true
Ultralytics YOLOv8.1.9 🚀 Python-3.10.12 torch-2.1.0+cu121 CUDA:0 (Tesla T4, 15102MiB)
YOLOv8s-seg summary (fused): 195 layers, 11779987 parameters, 0 gradients, 42.4 GFLOPs
image 1/60 /content/Predict_jpg/img_1.jpg: 1024x1024 11 Orange Trees, 43.1ms
image 2/60 /content/Predict_jpg/img_10.jpg: 1024x1024 9 Orange Trees, 40.6ms
image 3/60 /content/Predict_jpg/img_11.jpg: 1024x1024 10 Orange Trees, 40.6ms
image 4/60 /content/Predict_jpg/img_12.jpg: 1024x1024 8 Orange Trees, 35.9ms
image 5/60 /content/Predict_jpg/img_13.jpg: 1024x1024 11 Orange Trees, 35.9ms
image 6/60 /content/Predict_jpg/img_14.jpg: 1024x1024 10 Orange Trees, 33.9ms
image 7/60 /content/Predict_jpg/img_15.jpg: 1024x1024 11 Orange Trees, 34.0ms
image 8/60 /content/Predict_jpg/img_16.jpg: 1024x1024 11 Orange Trees, 33.9ms
image 9/60 /content/Predict_jpg/img_17.jpg: 1024x1024 11 Orange Trees, 32.1ms
image 10/60 /content/Predict_jpg/img_18.jpg: 1024x1024 9 Orange Trees, 32.1ms
image 11/60 /content/Predict_jpg/img_19.jpg: 1024x1024 9 Orange Trees, 32.3ms
image 12/60 /content/Predict_jpg/img_2.jpg: 1024x1024 10 Orange Trees, 31.3ms
image 13/60 /content/Predict_jpg/img_20.jpg: 1024x1024 8 Orange Trees, 31.3ms
image 14/60 /content/Predict_jpg/img_21.jpg: 1024x1024 11 Orange Trees, 31.4ms
image 15/60 /content/Predict_jpg/img_22.jpg: 1024x1024 12 Orange Trees, 31.3ms
image 16/60 /content/Predict_jpg/img_23.jpg: 1024x1024 9 Orange Trees, 31.4ms
image 17/60 /content/Predict_jpg/img_24.jpg: 1024x1024 11 Orange Trees, 31.4ms
image 18/60 /content/Predict_jpg/img_25.jpg: 1024x1024 10 Orange Trees, 31.3ms
image 19/60 /content/Predict_jpg/img_26.jpg: 1024x1024 10 Orange Trees, 31.3ms
image 20/60 /content/Predict_jpg/img_27.jpg: 1024x1024 9 Orange Trees, 30.9ms
image 21/60 /content/Predict_jpg/img_28.jpg: 1024x1024 8 Orange Trees, 31.0ms
image 22/60 /content/Predict_jpg/img_29.jpg: 1024x1024 9 Orange Trees, 30.9ms
image 23/60 /content/Predict_jpg/img_3.jpg: 1024x1024 9 Orange Trees, 31.0ms
image 24/60 /content/Predict_jpg/img_30.jpg: 1024x1024 10 Orange Trees, 30.9ms
image 25/60 /content/Predict_jpg/img_31.jpg: 1024x1024 10 Orange Trees, 30.9ms
image 26/60 /content/Predict_jpg/img_32.jpg: 1024x1024 7 Orange Trees, 30.9ms
image 27/60 /content/Predict_jpg/img_33.jpg: 1024x1024 10 Orange Trees, 30.9ms
image 28/60 /content/Predict_jpg/img_34.jpg: 1024x1024 8 Orange Trees, 30.9ms
image 29/60 /content/Predict_jpg/img_35.jpg: 1024x1024 9 Orange Trees, 30.9ms
image 30/60 /content/Predict_jpg/img_36.jpg: 1024x1024 9 Orange Trees, 31.0ms
image 31/60 /content/Predict_jpg/img_37.jpg: 1024x1024 9 Orange Trees, 30.9ms
image 32/60 /content/Predict_jpg/img_38.jpg: 1024x1024 9 Orange Trees, 31.1ms
image 33/60 /content/Predict_jpg/img_39.jpg: 1024x1024 8 Orange Trees, 31.0ms
image 34/60 /content/Predict_jpg/img_4.jpg: 1024x1024 6 Orange Trees, 30.9ms
image 35/60 /content/Predict_jpg/img_40.jpg: 1024x1024 11 Orange Trees, 30.9ms
image 36/60 /content/Predict_jpg/img_41.jpg: 1024x1024 11 Orange Trees, 32.0ms
image 37/60 /content/Predict_jpg/img_42.jpg: 1024x1024 11 Orange Trees, 32.1ms
image 38/60 /content/Predict_jpg/img_43.jpg: 1024x1024 8 Orange Trees, 30.6ms
image 39/60 /content/Predict_jpg/img_44.jpg: 1024x1024 8 Orange Trees, 30.5ms
image 40/60 /content/Predict_jpg/img_45.jpg: 1024x1024 10 Orange Trees, 30.6ms
image 41/60 /content/Predict_jpg/img_46.jpg: 1024x1024 11 Orange Trees, 30.6ms
image 42/60 /content/Predict_jpg/img_47.jpg: 1024x1024 10 Orange Trees, 30.6ms
image 43/60 /content/Predict_jpg/img_48.jpg: 1024x1024 10 Orange Trees, 30.6ms
image 44/60 /content/Predict_jpg/img_49.jpg: 1024x1024 11 Orange Trees, 30.5ms
image 45/60 /content/Predict_jpg/img_5.jpg: 1024x1024 12 Orange Trees, 30.6ms
image 46/60 /content/Predict_jpg/img_50.jpg: 1024x1024 12 Orange Trees, 30.6ms
image 47/60 /content/Predict_jpg/img_51.jpg: 1024x1024 12 Orange Trees, 30.6ms
image 48/60 /content/Predict_jpg/img_52.jpg: 1024x1024 7 Orange Trees, 30.5ms
image 49/60 /content/Predict_jpg/img_53.jpg: 1024x1024 11 Orange Trees, 30.6ms
image 50/60 /content/Predict_jpg/img_54.jpg: 1024x1024 10 Orange Trees, 30.6ms
image 51/60 /content/Predict_jpg/img_55.jpg: 1024x1024 10 Orange Trees, 30.6ms
image 52/60 /content/Predict_jpg/img_56.jpg: 1024x1024 4 Orange Trees, 30.6ms
image 53/60 /content/Predict_jpg/img_57.jpg: 1024x1024 9 Orange Trees, 30.6ms
image 54/60 /content/Predict_jpg/img_58.jpg: 1024x1024 12 Orange Trees, 30.9ms
image 55/60 /content/Predict_jpg/img_59.jpg: 1024x1024 10 Orange Trees, 30.9ms
image 56/60 /content/Predict_jpg/img_6.jpg: 1024x1024 9 Orange Trees, 30.9ms
image 57/60 /content/Predict_jpg/img_60.jpg: 1024x1024 2 Orange Trees, 31.2ms
image 58/60 /content/Predict_jpg/img_7.jpg: 1024x1024 13 Orange Trees, 31.3ms
image 59/60 /content/Predict_jpg/img_8.jpg: 1024x1024 11 Orange Trees, 31.7ms
image 60/60 /content/Predict_jpg/img_9.jpg: 1024x1024 10 Orange Trees, 31.7ms
Speed: 3.6ms preprocess, 31.9ms inference, 10.7ms postprocess per image at shape (1, 3, 1024, 1024)
Results saved to runs/segment/predict2
60 labels saved to runs/segment/predict2/labels
💡 Learn more at https://docs.ultralytics.com/modes/predict
Since we saved a .txt for each image, we can now use them together with the tiff image to obtain the coordinates that form the polygons.
In [ ]:
ls_poly = []
ls_class = []
imgs_to_pred = [f for f in os.listdir('/content/runs/segment/predict2/labels/') if f.endswith('.txt')]
for images in imgs_to_pred:
filename = images.split('.')[0]
src = rasterio.open('/content/Predict/' + filename + '.tif')
path = f'/content/runs/segment/predict2/labels/'+filename+'.txt'
with open(path, "r") as f:
txt_file = f.readlines()
for line in txt_file:
line_split = line.split()
cls_idx = line_split[0]
coords = line_split[1:]
polygon = np.array([[eval(x), eval(y)] for x, y in zip(coords[0::2], coords[1::2])]) # convert list of coordinates to numpy massive
polygon[:,0] = polygon[:,0]*1024
polygon[:,1] = polygon[:,1]*1024
for i in range(len(polygon)):
polygon[i,0], polygon[i,1] = rasterio.transform.xy(src.transform, polygon[i,1], polygon[i,0])
polygon = Polygon(polygon)
ls_poly.append(polygon)
ls_class.append(cls_idx)
In [ ]:
ls_poly
Out[ ]:
[<POLYGON ((621553.034 7741205.096, 621553.019 7741205.081, 621552.975 774120...>, <POLYGON ((621556.718 7741203.017, 621556.703 7741203.002, 621556.599 774120...>, <POLYGON ((621549.128 7741197.966, 621549.113 7741197.951, 621549.068 774119...>, <POLYGON ((621550.227 7741206.582, 621550.212 7741206.567, 621550.167 774120...>, <POLYGON ((621544.003 7741200.506, 621543.988 7741200.491, 621543.973 774120...>, <POLYGON ((621544.389 7741206.76, 621544.359 7741206.73, 621544.374 7741206....>, <POLYGON ((621546.78 7741198.917, 621546.766 7741198.902, 621546.691 7741198...>, <POLYGON ((621444.506 7741230.884, 621444.491 7741230.869, 621444.461 774123...>, <POLYGON ((621447.982 7741229.74, 621447.967 7741229.725, 621447.952 7741229...>, <POLYGON ((621443.882 7741237.182, 621443.867 7741237.168, 621443.837 774123...>, <POLYGON ((621451.681 7741228.507, 621451.666 7741228.492, 621451.636 774122...>, <POLYGON ((621440.629 7741223.516, 621440.614 7741223.501, 621440.554 774122...>, <POLYGON ((621439.217 7741231.656, 621439.203 7741231.642, 621439.173 774123...>, <POLYGON ((621436.93 7741224.586, 621436.93 7741222.045, 621438.133 7741222....>, <POLYGON ((621449.111 7741237.242, 621449.111 7741237.227, 621449.081 774123...>, <POLYGON ((621440.034 7741237.242, 621440.034 7741237.064, 621440.049 774123...>, <POLYGON ((621444.209 7741222.729, 621444.194 7741222.714, 621444.164 774122...>, <POLYGON ((621452.037 7741236.157, 621452.037 7741236.143, 621452.022 774123...>, <POLYGON ((621437.019 7741232.132, 621437.004 7741232.117, 621437.004 774123...>, <POLYGON ((621479.533 7741227.497, 621479.518 7741227.482, 621479.444 774122...>, <POLYGON ((621472.67 7741230.527, 621472.655 7741230.513, 621472.596 7741230...>, <POLYGON ((621477.587 7741235.994, 621477.572 7741235.979, 621477.528 774123...>, <POLYGON ((621468.778 7741237.182, 621468.763 7741237.168, 621468.734 774123...>, <POLYGON ((621468.704 7741231.478, 621468.689 7741231.463, 621468.615 774123...>, <POLYGON ((621475.834 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7741214.93, 621414.588 7741214...>, <POLYGON ((621409.998 7741215.732, 621409.983 7741215.717, 621409.894 774121...>, <POLYGON ((621419.149 7741213.652, 621419.134 7741213.638, 621419.045 774121...>, <POLYGON ((621407.503 7741208.364, 621407.488 7741208.349, 621407.399 774120...>, <POLYGON ((621411.187 7741207.77, 621411.172 7741207.755, 621411.127 7741207...>, <POLYGON ((621409.597 7741222.031, 621409.597 7741221.941, 621409.612 774122...>, <POLYGON ((621406.775 7741221.971, 621406.745 7741221.941, 621406.745 774122...>, <POLYGON ((621421.615 7741220.486, 621421.6 7741220.471, 621421.54 7741220.4...>, <POLYGON ((621536.962 7741204.561, 621536.947 7741204.547, 621536.887 774120...>, <POLYGON ((621528.494 7741206.76, 621528.479 7741206.745, 621528.45 7741206....>, <POLYGON ((621530.5 7741199.377, 621530.485 7741199.362, 621530.47 7741199.3...>, <POLYGON ((621540.541 7741202.006, 621540.527 7741201.992, 621540.452 774120...>, <POLYGON ((621539.606 7741194.104, 621539.591 7741194.089, 621539.531 774119...>, <POLYGON ((621536.635 7741195.827, 621536.62 7741195.812, 621536.59 7741195....>, <POLYGON ((621534.496 7741197.788, 621534.481 7741197.773, 621534.406 774119...>, <POLYGON ((621532.966 7741205.616, 621532.951 7741205.601, 621532.862 774120...>, <POLYGON ((621543.245 7741192.544, 621543.23 7741192.529, 621543.067 7741192...>, <POLYGON ((621543.319 7741200.283, 621543.304 7741200.268, 621543.26 7741200...>]
Finally, we create a GeoDataFrame with the polygons and save it in a .json file.
In [ ]:
gdf = gpd.GeoDataFrame(ls_class, geometry=ls_poly, crs=src.crs)
In [ ]:
gdf.rename(columns={0:'class'}, inplace=True)
In [ ]:
gdf.reset_index(inplace=True)
In [ ]:
gdf
Out[ ]:
| index | class | geometry | |
|---|---|---|---|
| 0 | 0 | 0 | POLYGON ((621553.034 7741205.096, 621553.019 7... |
| 1 | 1 | 0 | POLYGON ((621556.718 7741203.017, 621556.703 7... |
| 2 | 2 | 0 | POLYGON ((621549.128 7741197.966, 621549.113 7... |
| 3 | 3 | 0 | POLYGON ((621550.227 7741206.582, 621550.212 7... |
| 4 | 4 | 0 | POLYGON ((621544.003 7741200.506, 621543.988 7... |
| ... | ... | ... | ... |
| 571 | 571 | 0 | POLYGON ((621536.635 7741195.827, 621536.620 7... |
| 572 | 572 | 0 | POLYGON ((621534.496 7741197.788, 621534.481 7... |
| 573 | 573 | 0 | POLYGON ((621532.966 7741205.616, 621532.951 7... |
| 574 | 574 | 0 | POLYGON ((621543.245 7741192.544, 621543.230 7... |
| 575 | 575 | 0 | POLYGON ((621543.319 7741200.283, 621543.304 7... |
576 rows × 3 columns
In [ ]:
gdf.to_file('orange_trees_pred.json', driver='GeoJSON')
In [ ]:
gdf.plot()
Out[ ]:
<Axes: >
