Add breeding_date_set.py

breeding_date_set.py

@@ -0,0 +1,78 @@
+import cv2
+import albumentations as A
+import numpy as np
+import os
+file_txt = []
+file_jpg = []
+
+folder_path = ('D:\\train')
+for foldername, subfolders, filenames in os.walk(folder_path):
+    for subfolder in subfolders:
+        folder_path = (f'D:\\train\\{subfolder}')
+        for foldername, subfolders, filenames in os.walk(folder_path):
+            for filename in filenames:
+
+                f = filename.split('.')
+
+                if f[-1] == 'txt':
+                    file_txt.append(filename)
+                elif f[-1] == 'jpg':
+                    file_jpg.append(filename)
+
+for k in range(len(file_jpg)):
+    image = cv2.imread(f'D:\\train\\images\\{file_jpg[k]}')
+    annotations = []
+    with open(f'D:\\train\\labels\\{file_txt[k]}', 'r') as file:
+        lines = file.readlines()
+        for line in lines:
+            print(line)
+            annotations.append(line)
+
+
+    main_fillet_yolo_conversion = []
+    fillet_yolo_bboxes = []
+    fillet_yolo_class = []
+    print(annotations)
+    for ii in range(len(annotations)):
+        a = annotations[ii].split(' ')
+
+        for i in range(len(a)):
+
+            try:
+                main_fillet_yolo_conversion.append(int(a[i]))
+            except ValueError:
+                main_fillet_yolo_conversion.append(float(a[i]))
+            print(main_fillet_yolo_conversion)
+        fillet_yolo_class.append(main_fillet_yolo_conversion[0])
+        del main_fillet_yolo_conversion[0]
+        fillet_yolo_bboxes.append(main_fillet_yolo_conversion)
+        print(fillet_yolo_bboxes)
+        print(fillet_yolo_class)
+        main_fillet_yolo_conversion = []
+
+    for o in range(10):
+        if image is None:
+            raise ValueError("Image not found or the path is incorrect")
+        if not isinstance(image, np.ndarray):
+            raise TypeError("Image must be a numpy array")
+
+        bboxes = fillet_yolo_bboxes
+        category_ids = fillet_yolo_class
+
+        transform = A.Compose([
+            A.HorizontalFlip(p=0.5),
+            A.RandomBrightnessContrast(p=0.2),
+            A.ShiftScaleRotate(shift_limit=0.1, scale_limit=0.2, rotate_limit=15, p=0.5),
+        ], bbox_params=A.BboxParams(format='yolo', label_fields=['category_ids']))
+
+        transformed = transform(image=image, bboxes=bboxes, category_ids=category_ids)
+        transformed_image = transformed['image']
+        transformed_bboxes = transformed['bboxes']
+        transformed_category_ids = transformed['category_ids']
+
+        cv2.imwrite(f'D:\\python\\prodgect_2\\pythonProject1\\op\\1\\{o}_{file_jpg[k]}', transformed_image)
+        with open(f'D:\\python\\prodgect_2\\pythonProject1\\op\\1\\{o}_{file_txt[k]}', 'w') as f:
+            for bbox, category_id in zip(transformed_bboxes, transformed_category_ids):
+                x_center, y_center, width, height = bbox
+                cla = category_id
+                f.write(f"{cla} {x_center} {y_center} {width} {height}\n")