Made some changes

breeding_date_set.py

@@ -1,17 +1,14 @@
+import math
+import datetime
 import cv2
 import albumentations as A
 import numpy as np
 import os
-file_txt = []
+def file_magnification(folder_path):
-file_jpg = []
+    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':
@@ -19,13 +16,12 @@ for foldername, subfolders, filenames in os.walk(folder_path):
             elif f[-1] == 'jpg':
                 file_jpg.append(filename)
 
-for k in range(len(file_jpg)):
+    for k in range(len(file_jpg)):
-    image = cv2.imread(f'D:\\train\\images\\{file_jpg[k]}')
+        image = cv2.imread(f'{folder_path}\\{file_jpg[k]}')
         annotations = []
-    with open(f'D:\\train\\labels\\{file_txt[k]}', 'r') as file:
+        with open(f'{folder_path}\\{file_txt[k]}', 'r') as file:
             lines = file.readlines()
             for line in lines:
-            print(line)
                 annotations.append(line)
 
 
@@ -35,9 +31,7 @@ for k in range(len(file_jpg)):
         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:
@@ -46,16 +40,9 @@ for k in range(len(file_jpg)):
             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
 
@@ -70,9 +57,63 @@ for k in range(len(file_jpg)):
             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)
+            cv2.imwrite(f'{folder_path}\\{o}_{file_jpg[k]}', transformed_image)
-        with open(f'D:\\python\\prodgect_2\\pythonProject1\\op\\1\\{o}_{file_txt[k]}', 'w') as f:
+            with open(f'{folder_path}\\{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")
+file_txt_1 = []
+file_jpg_1 = []
+file_start = 'Zombobase-'+str(datetime.date.today())
+folder_path = ('train')
+for foldername, subfolders, filenames in os.walk(folder_path):
+    for subfolder in subfolders:
+        folder_path = (f'train\\{subfolder}')
+        for foldername, subfolders, filenames in os.walk(folder_path):
+            for filename in filenames:
+
+                f = filename.split('.')
+
+                if f[-1] == 'txt':
+                    file_txt_1.append(filename)
+                elif f[-1] == 'jpg':
+                    file_jpg_1.append(filename)
+
+annotations = []
+os.makedirs(file_start)
+file = ['test', 'train']
+percent_fille = [0.20,0.10]
+for fi, p_f in zip(file, percent_fille):
+    os.makedirs(f'{file_start}\\{fi}')
+
+    for i in range(math.ceil(len(file_txt_1)* p_f)):
+        image = cv2.imread(f'D:\\train\\images\\{file_jpg_1[i]}')
+        with open(f'D:\\train\\labels\\{file_txt_1[i]}', 'r') as file:
+            lines = file.readlines()
+            for line in lines:
+                annotations.append(line)
+        cv2.imwrite(f'{file_start}\\{fi}\\{file_jpg_1[i]}', image)
+        with open(f'{file_start}\\{fi}\\{file_txt_1[i]}', 'w') as f:
+            for iii in range(len(annotations)):
+                f.write(annotations[iii])
+
+        annotations = []
+        del file_txt_1[i]
+        del file_jpg_1[i]
+
+os.makedirs(f'{file_start}\\validation')
+for a, j in zip(file_txt_1, file_jpg_1):
+    image = cv2.imread(f'D:\\train\\images\\{j}')
+    with open(f'D:\\train\\labels\\{a}', 'r') as file:
+        lines = file.readlines()
+        for line in lines:
+            annotations.append(line)
+    cv2.imwrite(f'{file_start}\\Validation\\{j}', image)
+    with open(f'{file_start}\\Validation\\{a}', 'w') as f:
+        for iii in range(len(annotations)):
+            f.write(annotations[iii])
+    annotations = []
+file = ['test', 'train','validation']
+for i in file:
+    file_magnification(f'{file_start}\\{i}')