add train.py

form dataset for current date
add exception catching

constants.py

@@ -0,0 +1,13 @@
+import os
+from datetime import datetime
+
+from dto.annotationClass import AnnotationClass
+
+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')
+annotation_classes = AnnotationClass.read_json()
+
+
+prefix = 'zombobase-'
+today_dataset = os.path.join('datasets', f'{prefix}{datetime.now():%Y-%m-%d}')

preprocessing.py

@@ -3,15 +3,11 @@ import time
 from pathlib import Path
 import albumentations as A
 import cv2
-from dto.annotationClass import AnnotationClass
+from constants import current_images_dir, current_labels_dir, annotation_classes
 from dto.imageLabel import ImageLabel
 
 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')
-annotation_classes = AnnotationClass.read_json()
 
 
 def image_processing(img_ann: ImageLabel) -> [ImageLabel]:
@@ -37,16 +33,19 @@ def image_processing(img_ann: ImageLabel) -> [ImageLabel]:
 
     results = []
     for i, transform in enumerate(transforms):
-        res = transform(image=img_ann.image, bboxes=img_ann.labels)
+        try:
-        path = Path(img_ann.image_path)
+            res = transform(image=img_ann.image, bboxes=img_ann.labels)
-        name = f'{path.stem}_{i+1}'
+            path = Path(img_ann.image_path)
-        img = ImageLabel(
+            name = f'{path.stem}_{i+1}'
-            image=res['image'],
+            img = ImageLabel(
-            labels=res['bboxes'],
+                image=res['image'],
-            image_path=os.path.join(current_images_dir, f'{name}{path.suffix}'),
+                labels=res['bboxes'],
-            labels_path=os.path.join(current_labels_dir, f'{name}.txt')
+                image_path=os.path.join(current_images_dir, f'{name}{path.suffix}'),
-        )
+                labels_path=os.path.join(current_labels_dir, f'{name}.txt')
-        results.append(img)
+            )
+            results.append(img)
+        except Exception as e:
+            print(f'Error during transformtation: {e}')
     return results
 
 
@@ -74,7 +73,7 @@ def read_labels(labels_path) -> [[]]:
         for row in rows:
             str_coordinates = row.split(' ')
             class_num = str_coordinates.pop(0)
-            coordinates = [float(n) for n in str_coordinates]
+            coordinates = [float(n.replace(',', '.')) for n in str_coordinates]
             coordinates.append(class_num)
             arr.append(coordinates)
         return arr
@@ -111,8 +110,18 @@ def main():
                     labels_path=labels_path,
                     labels=read_labels(labels_path)
                 ))
-            except FileNotFoundError:
+            except Exception as e:
-                print(f'No labels file {labels_path} found')
+                print(f'Error appeared {e}')
+
+                try:
+                    os.remove(image_path)
+                except OSError:
+                    pass
+
+                try:
+                    os.remove(labels_path)
+                except OSError:
+                    pass
 
 
 if __name__ == '__main__':

tests/imagelabel_visualize_test.py

@@ -1,19 +1,16 @@
-from pathlib import Path
 import cv2
-import os.path
+
 
 from dto.annotationClass import AnnotationClass
 from dto.imageLabel import ImageLabel
 from preprocessing import read_labels
 
-images_dir = '../images'
-labels_dir = '../labels'
 annotation_classes = AnnotationClass.read_json()
 
+images_dir = ''
 
-image = os.listdir(images_dir)[0]
+image_path = 'test01.jpg'
-image_path = os.path.join(images_dir, image)
+labels_path = 'test01.txt'
-labels_path = os.path.join(labels_dir, f'{Path(image_path).stem}.txt')
 
 img = ImageLabel(
     image_path=image_path,

tests/test01.txt

@@ -0,0 +1 @@
+0 0.3809 0.49269 0.21636 0.39129

train.py

@@ -1,4 +1,86 @@
 import os
+import shutil
+from datetime import datetime
+from pathlib import Path
+
+from ultralytics import YOLO
+from constants import current_images_dir, current_labels_dir, annotation_classes, today_dataset, prefix
+
+yaml_name = 'data.yaml'
+yaml_path = os.path.join(today_dataset, yaml_name)
+train_set = 70
+valid_set = 20
+test_set = 10
 
 
-current_dataset_dir = os.path.join('datasets', 'zombobase-current')
+def form_dataset():
+    os.makedirs(today_dataset, exist_ok=True)
+    images = os.listdir(current_images_dir)
+
+    train_size = int(len(images) * train_set / 100.0)
+    valid_size = int(len(images) * valid_set / 100.0)
+
+    move_annotations(images[:train_size], 'train')
+    move_annotations(images[train_size:train_size + valid_size], 'valid')
+    move_annotations(images[train_size + valid_size:], 'test')
+
+    create_yaml()
+
+
+def move_annotations(images, folder):
+    destination_images = os.path.join(today_dataset, folder, 'images')
+    os.makedirs(destination_images, exist_ok=True)
+    destination_labels = os.path.join(today_dataset, folder, 'labels')
+    os.makedirs(destination_labels, exist_ok=True)
+    for image_name in images:
+        image_path = os.path.join(current_images_dir, image_name)
+        label_name = f'{Path(image_name).stem}.txt'
+        label_path = os.path.join(current_labels_dir, label_name)
+        os.replace(image_path, os.path.join(destination_images, image_name))
+        os.replace(label_path, os.path.join(destination_labels, label_name))
+
+
+def create_yaml():
+    lines = ['names:']
+    for c in annotation_classes:
+        lines.append(f'- {annotation_classes[c].name}')
+    lines.append(f'nc: {len(annotation_classes)}')
+    lines.append(f'test: test/images')
+    lines.append(f'train: train/images')
+    lines.append(f'val: valid/images')
+    lines.append('')
+
+    with open(yaml_path, 'w', encoding='utf-8') as f:
+        f.writelines([f'{line}\n' for line in lines])
+
+
+def get_recent_model():
+    date_sets = []
+    datasets = [next((file for file in os.listdir(os.path.join('datasets', d)) if file.endswith('pt')), None)
+                for d in os.listdir('datasets')]
+
+    # date_str = d.replace(prefix, '')
+    # if date_str == 'current' or date_str == f'{datetime.now():%Y-%m-%d}':
+    #     continue
+    # if len(date_sets) == 0:
+    #     return None
+
+    recent = max(date_sets)
+    return os.path.join('datasets', f'{prefix}{recent}', f'{prefix}{recent}.pt')
+
+
+def retrain():
+    model = YOLO(get_recent_model() or 'yolov10x.yaml')
+    model.train(data=yaml_path, save=True, cache=True)
+
+
+def revert_to_current(date):
+    def revert_dir(dir):
+        os.listdir(os.path.join(current_images_dir, 'images'))
+
+    date_dataset = f'{prefix}{date}'
+    revert_dir(os.path.join(date_dataset, 'test'))
+
+form_dataset()
+create_yaml()
+retrain()

yolov10x.yaml

@@ -0,0 +1,40 @@
+# Parameters
+nc: 50 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
+  # [depth, width, max_channels]
+  x: [1.00, 1.25, 512]
+
+# YOLOv8.0n backbone
+backbone:
+  # [from, repeats, module, args]
+  - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+  - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+  - [-1, 3, C2f, [128, True]]
+  - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+  - [-1, 6, C2f, [256, True]]
+  - [-1, 1, SCDown, [512, 3, 2]] # 5-P4/16
+  - [-1, 6, C2fCIB, [512, True]]
+  - [-1, 1, SCDown, [1024, 3, 2]] # 7-P5/32
+  - [-1, 3, C2fCIB, [1024, True]]
+  - [-1, 1, SPPF, [1024, 5]] # 9
+  - [-1, 1, PSA, [1024]] # 10
+
+# YOLOv8.0n head
+head:
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+  - [-1, 3, C2fCIB, [512, True]] # 13
+
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+  - [-1, 3, C2f, [256]] # 16 (P3/8-small)
+
+  - [-1, 1, Conv, [256, 3, 2]]
+  - [[-1, 13], 1, Concat, [1]] # cat head P4
+  - [-1, 3, C2fCIB, [512, True]] # 19 (P4/16-medium)
+
+  - [-1, 1, SCDown, [512, 3, 2]]
+  - [[-1, 10], 1, Concat, [1]] # cat head P5
+  - [-1, 3, C2fCIB, [1024, True]] # 22 (P5/32-large)
+
+  - [[16, 19, 22], 1, v10Detect, [nc]] # Detect(P3, P4, P5)