delete breeding

add proper preprocessing. not so tested yet

preprocessing.py

@@ -1,60 +1,83 @@
 import os.path
 import time
-
-import cv2
-import albumentations as alb
-from os import listdir
-from os.path import isfile, join
+from array import *
 from pathlib import Path
+import albumentations as A
+import cv2
+
 
 labels_dir = 'labels'
 images_dir = 'images'
 current_dataset_dir = os.path.join('datasets', 'zombobase-current')
+current_images_dir = os.path.join(current_dataset_dir, 'images')
+current_labels_dir = os.path.join(current_dataset_dir, 'labels')
 
-class ImageAnnotation:
 
-    def read_annotations(self) -> [[]]:
-        with open(self.annotation_path, 'r') as f:
-            rows = f.readlines()
-            arr = []
-            for row in rows:
-                str_coordinates = row.split(' ')
-                class_num = str_coordinates.pop(0)
-                coordinates = [float(n) for n in str_coordinates]
-                coordinates.append(class_num)
-                arr.append(coordinates)
-            return arr
+class ImageLabel:
 
-    def __init__(self, image_path):
+    def __init__(self, image_path, image, labels_path, labels):
         self.image_path = image_path
-        self.image_name = Path(image_path).stem
-        self.dataset_image_path = os.path.join(current_dataset_dir, images_dir, self.image_name, os.path.basename(image_path))
-        self.image = cv2.imread(image_path)
+        self.image = image
+        self.labels_path = labels_path
+        self.labels = labels
 
-        self.annotation_path = os.path.join(labels_dir, self.image_name, '.txt')
-        self.dataset_annotation_path = os.path.join(current_dataset_dir, labels_dir, self.image_name, '.txt')
-        self.annotations = self.read_annotations()
 
-def image_processing(img_ann: ImageAnnotation) -> [ImageAnnotation]:
-    # return structure example:
-    # utilize transform albumentations here
-    return [ImageAnnotation(f'{img_ann.image_name}1', image1, bboxes1 ),
-            ImageAnnotation(f'{img_ann.image_name}2', image2, bboxes2),
-            ...
-                ]
+def image_processing(img_ann: ImageLabel) -> [ImageLabel]:
+    transforms = [
+        A.Compose([A.HorizontalFlip(always_apply=True)], bbox_params=A.BboxParams(format='yolo')),
+        A.Compose([A.RandomBrightnessContrast(always_apply=True)], bbox_params=A.BboxParams(format='yolo')),
+        A.Compose([A.ShiftScaleRotate(shift_limit=0.1, scale_limit=0.2, rotate_limit=15, always_apply=True)],
+                  bbox_params=A.BboxParams(format='yolo'))
+    ]
 
-def write_results(img_ann: ImageAnnotation):
-    # write image cv2.imwrite(, image) dataset_image_path
-    # write img_ann.annotations into new file with name dataset_annotation_path
+    results = []
+    for i, transform in enumerate(transforms):
+        res = transform(image=img_ann.image, bboxes=img_ann.labels)
+        path = Path(img_ann.image_path)
+        name = f'{path.stem}_{i+1}'
+        results.append(ImageLabel(
+            image=res['image'],
+            labels=res['bboxes'],
+            image_path=os.path.join(current_images_dir, f'{name}{path.suffix}'),
+            labels_path=os.path.join(current_labels_dir, f'{name}.txt')
+        ))
+    return results
+
+
+def write_result(img_ann: ImageLabel):
+    cv2.imwrite(img_ann.image_path, img_ann.image)
+    with open(img_ann.labels_path, 'w') as f:
+        lines = [f'{ann[4]} {round(ann[0], 5)} {round(ann[1], 5)} {round(ann[2], 5)} {round(ann[3], 5)}\n' for ann in img_ann.labels]
+        f.writelines(lines)
+        f.close()
+
+
+def read_labels(labels_path) -> [[]]:
+    with open(labels_path, 'r') as f:
+        rows = f.readlines()
+        arr = []
+        for row in rows:
+            str_coordinates = row.split(' ')
+            class_num = str_coordinates.pop(0)
+            coordinates = [float(n) for n in str_coordinates]
+            coordinates.append(class_num)
+            arr.append(coordinates)
+        return arr
 
 
 def process_image(img_ann):
     results = image_processing(img_ann)
     for res_ann in results:
-        write_results(res_ann)
-    write_results(img_ann)
+        write_result(res_ann)
+
+    write_result(ImageLabel(
+        image=img_ann.image,
+        labels=img_ann.labels,
+        image_path=os.path.join(current_images_dir, Path(img_ann.image_path).name),
+        labels_path=os.path.join(current_labels_dir, Path(img_ann.labels_path).name)
+    ))
     os.remove(img_ann.image_path)
-    os.remove(img_ann.annotation_path)
+    os.remove(img_ann.labels_path)
 
 
 def main():
@@ -66,7 +89,14 @@ def main():
 
         for image in images:
             image_path = os.path.join(images_dir, image)
-            process_image(ImageAnnotation(image_path))
+            labels_path = os.path.join(labels_dir, f'{Path(image_path).stem}.txt')
+            process_image(ImageLabel(
+                image_path=image_path,
+                image=cv2.imread(image_path),
+                labels_path=labels_path,
+                labels=read_labels(labels_path)
+            ))
+
 
 if __name__ == '__main__':
     main()