add checkpoints and config system

convert from bbox oriented and pascal xml
fixes

config.py

@@ -0,0 +1,22 @@
+import yaml
+
+config_file = 'config.yaml'
+
+
+class Config:
+
+    def __init__(self):
+        with open(config_file, 'r') as f:
+            c = yaml.safe_load(f)
+            self.checkpoint = c['checkpoint']
+            self.images_dir = c['images_dir']
+            self.labels_dir = c['labels_dir']
+            f.close()
+
+    def write(self):
+        with open(config_file, 'w') as f:
+            d = dict(checkpoint=self.checkpoint,
+                     images_dir=self.images_dir,
+                     labels_dir=self.labels_dir)
+            yaml.safe_dump(d, f)
+            f.close()

config.yaml

@@ -0,0 +1,3 @@
+checkpoint: 2024-06-18 19:14:02.080664
+images_dir: E:\images
+labels_dir: E:\labels

constants.py

@@ -1,9 +1,9 @@
 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-'
+date_format = '%Y-%m-%d'

convert-annotations.py

@@ -2,6 +2,7 @@ import os
 import shutil
 import xml.etree.cElementTree as et
 from pathlib import Path
+import cv2
 
 labels_dir = 'labels'
 images_dir = 'images'
@@ -13,36 +14,50 @@ tag_bndbox = 'bndbox'
 name_class_map = {'Truck': 1, 'Car': 2, 'Taxi': 2}  # 1 Вантажівка, 2 Машина легкова
 forbidden_classes = ['Motorcycle']
 default_class = 1
+image_extensions = ['jpg', 'png', 'jpeg']
 
 
-def convert_xml(folder):
+def convert(folder, read_annotations, ann_format):
     os.makedirs(images_dir, exist_ok=True)
     os.makedirs(labels_dir, exist_ok=True)
 
     for f in os.listdir(folder):
-        if not f.endswith('.jpg'):
+        if not f[-3:] in image_extensions:
             continue
-        label = f'{Path(f).stem}.xml'
-        lines = read_xml(folder, label)
-        if not lines:
-            print(f'Image {f} has only forbidden classes in annotations')
+        im = cv2.imread(os.path.join(folder, f))
+        height = im.shape[0]
+        width = im.shape[1]
+
+        label = f'{Path(f).stem}.{ann_format}'
+        try:
+            with open(os.path.join(folder, label), 'r') as label_file:
+                text = label_file.read()
+                lines = read_annotations(width, height, text)
+        except ValueError as val_err:
+            print(f'Image {f} annotations could not be converted. Error: {val_err}')
             continue
+        except Exception as e:
+            print(f'Error conversion for {f}. Error: {e}')
 
         shutil.copy(os.path.join(folder, f), os.path.join(images_dir, f))
-        with open(os.path.join(labels_dir, f'{Path(label).stem}.txt'), 'w') as label_file:
-            label_file.writelines(lines)
-            label_file.close()
+        with open(os.path.join(labels_dir, f'{Path(label).stem}.txt'), 'w') as new_label_file:
+            new_label_file.writelines(lines)
+            new_label_file.close()
         print(f'Image {f} has been processed successfully')
 
 
-def read_xml(folder, label):
-    tree = et.parse(os.path.join(folder, label))
-    root = tree.getroot()
+def minmax2yolo(width, height, xmin, xmax, ymin, ymax):
+    c_w = (xmax - xmin) / width
+    c_h = (ymax - ymin) / height
+    c_x = xmin / width + c_w / 2
+    c_y = ymin / height + c_h / 2
+    return round(c_x, 5), round(c_y, 5), round(c_w, 5), round(c_h, 5)
+
+
+def read_pascal_voc(width, height, s):
+    root = et.fromstring(s)
     lines = []
-    size_dict = {size_ch.tag: size_ch.text for size_ch in root.findall(f'{tag_size}/*')}
-    width = int(size_dict['width'])
-    height = int(size_dict['height'])
-    for node_object in tree.findall(tag_object):
+    for node_object in root.findall(tag_object):
         class_num = default_class
         c_x = c_y = c_w = c_h = 0
         for node_object_ch in node_object:
@@ -58,20 +73,47 @@ def read_xml(folder, label):
                         class_num = default_class
             if node_object_ch.tag == tag_bndbox:
                 bbox_dict = {bbox_ch.tag: bbox_ch.text for bbox_ch in node_object_ch}
-                xmin = int(bbox_dict['xmin'])
-                xmax = int(bbox_dict['xmax'])
-                ymin = int(bbox_dict['ymin'])
-                ymax = int(bbox_dict['ymax'])
-                c_w = (xmax - xmin) / width
-                c_h = (ymax - ymin) / height
-                c_x = xmin / width + c_w / 2
-                c_y = ymin / height + c_h / 2
+                c_x, c_y, c_w, c_h = minmax2yolo(width, height,
+                                                 int(bbox_dict['xmin']),
+                                                 int(bbox_dict['xmax']),
+                                                 int(bbox_dict['ymin']),
+                                                 int(bbox_dict['ymax']))
         if class_num == -1:
             continue
+        if c_x > 1 or c_y > 1 or c_w > 1 or c_h > 1:
+            print('Values are out of bounds')
+        else:
             if c_x != 0 and c_y != 0 and c_w != 0 and c_h != 0:
-            lines.append(f'{class_num} {round(c_x, 5)} {round(c_y, 5)} {round(c_w, 5)} {round(c_h, 5)}\n')
+                lines.append(f'{class_num} {c_x} {c_y} {c_w} {c_h}\n')
     return lines
 
 
+def read_bbox_oriented(width, height, s):
+    yolo_lines = []
+    lines = s.split('\n', )
+    for line in lines:
+        if line == '':
+            continue
+        vals = line.split(' ')
+        if len(vals) != 14:
+            raise ValueError('wrong format')
+        xmin = min(int(vals[6]), int(vals[7]), int(vals[8]), int(vals[9]))
+        xmax = max(int(vals[6]), int(vals[7]), int(vals[8]), int(vals[9]))
+        ymin = min(int(vals[10]), int(vals[11]), int(vals[12]), int(vals[13]))
+        ymax = max(int(vals[10]), int(vals[11]), int(vals[12]), int(vals[13]))
+        c_x, c_y, c_w, c_h = minmax2yolo(width, height, xmin, xmax, ymin, ymax)
+        if c_x > 1 or c_y > 1 or c_w > 1 or c_h > 1:
+            print('Values are out of bounds')
+        else:
+            yolo_lines.append(f'1 {c_x} {c_y} {c_w} {c_h}\n')
+    return yolo_lines
+
+
+def rename_images(folder):
+    for f in os.listdir(folder):
+        shutil.move(os.path.join(folder, f), os.path.join(folder, f[:-7] + '.png'))
+
+
 if __name__ == '__main__':
-    convert_xml('datasets/others/UAVimages')
+    convert('datasets/others/UAVHeightImages', read_bbox_oriented, 'txt')
+    convert('datasets/others/UAVimages', read_pascal_voc, 'xml')

preprocessing.py

@@ -1,13 +1,18 @@
 import os.path
 import time
+from datetime import datetime
 from pathlib import Path
 import albumentations as A
 import cv2
-from constants import current_images_dir, current_labels_dir, annotation_classes
+import numpy as np
+from dateutil.relativedelta import relativedelta
+
+from config import Config
+from constants import current_images_dir, current_labels_dir, annotation_classes, prefix, date_format, \
+    current_dataset_dir
 from dto.imageLabel import ImageLabel
 
-labels_dir = 'labels'
-images_dir = 'images'
+config = Config()
 
 
 def image_processing(img_ann: ImageLabel) -> [ImageLabel]:
@@ -22,7 +27,7 @@ def image_processing(img_ann: ImageLabel) -> [ImageLabel]:
                    A.RandomBrightnessContrast(always_apply=True)],
                   bbox_params=A.BboxParams(format='yolo')),
         A.Compose([A.ShiftScaleRotate(scale_limit=0.2, always_apply=True),
-                   A.VerticalFlip(always_apply=True),],
+                   A.VerticalFlip(always_apply=True), ],
                   bbox_params=A.BboxParams(format='yolo')),
         A.Compose([A.ShiftScaleRotate(scale_limit=0.2, always_apply=True)],
                   bbox_params=A.BboxParams(format='yolo')),
@@ -36,7 +41,7 @@ def image_processing(img_ann: ImageLabel) -> [ImageLabel]:
         try:
             res = transform(image=img_ann.image, bboxes=img_ann.labels)
             path = Path(img_ann.image_path)
-            name = f'{path.stem}_{i+1}'
+            name = f'{path.stem}_{i + 1}'
             img = ImageLabel(
                 image=res['image'],
                 labels=res['bboxes'],
@@ -56,11 +61,12 @@ def write_result(img_ann: ImageLabel, show_image=False):
     if show_image:
         img_ann.visualize(annotation_classes)
 
-    cv2.imwrite(img_ann.image_path, img_ann.image)
+    cv2.imencode('.jpg', img_ann.image)[1].tofile(img_ann.image_path)
     print(f'{img_ann.image_path} written')
 
     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]
+        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()
     print(f'{img_ann.labels_path} written')
@@ -89,40 +95,69 @@ def process_image(img_ann):
         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.labels_path)
+    # os.remove(img_ann.image_path)
+    # os.remove(img_ann.labels_path)
+
+
+def get_checkpoint():
+    if config.checkpoint is not None:
+        return config.checkpoint
+
+    dates = []
+    for directory in os.listdir('models'):
+        try:
+            dates.append(datetime.strptime(directory[len(prefix):], date_format))
+        except:
+            continue
+    if len(dates) == 0:
+        return datetime.now() - relativedelta(years=1)
+    else:
+        return max(dates)
 
 
 def main():
+    last_date = checkpoint = get_checkpoint()
     while True:
-        images = os.listdir(images_dir)
-        if len(images) == 0:
-            time.sleep(5)
+        images = []
+        with os.scandir(config.images_dir) as imd:
+            for image_file in imd:
+                if not image_file.is_file():
                     continue
+                mod_time = datetime.fromtimestamp(image_file.stat().st_mtime)
+                if mod_time > checkpoint:
+                    images.append(image_file)
+                    last_date = max(last_date, mod_time)
 
-        for image in images:
+        for image_file in images:
             try:
-                image_path = os.path.join(images_dir, image)
-                labels_path = os.path.join(labels_dir, f'{Path(image_path).stem}.txt')
+                image_path = os.path.join(config.images_dir, image_file.name)
+                labels_path = os.path.join(config.labels_dir, f'{Path(image_path).stem}.txt')
+                image = cv2.imdecode(np.fromfile(image_path, dtype=np.uint8), cv2.IMREAD_UNCHANGED)
                 process_image(ImageLabel(
                     image_path=image_path,
-                    image=cv2.imread(image_path),
+                    image=image,
                     labels_path=labels_path,
                     labels=read_labels(labels_path)
                 ))
             except Exception as e:
                 print(f'Error appeared {e}')
+        if last_date != checkpoint:
+            checkpoint = config.checkpoint = last_date
+            config.write()
+        time.sleep(5)
 
-                try:
-                    os.remove(image_path)
-                except OSError:
-                    pass
 
-                try:
-                    os.remove(labels_path)
-                except OSError:
-                    pass
+def check_labels():
+    for label in os.listdir(os.path.join(current_dataset_dir, 'labels')):
+        with open(os.path.join(current_dataset_dir, 'labels', label), 'r') as f:
+            lines = f.readlines()
+            for line in lines:
+                list_c = line.split(' ')[1:]
+                for l in list_c:
+                    if float(l) > 1:
+                        print('Error!')
 
 
 if __name__ == '__main__':
-    main()
+    check_labels()
+    # main()

train.py

@@ -4,11 +4,10 @@ import shutil
 from datetime import datetime
 from pathlib import Path
 from ultralytics import YOLOv10
-from constants import current_images_dir, current_labels_dir, annotation_classes
+from constants import current_images_dir, current_labels_dir, annotation_classes, prefix, date_format
 
-prefix = 'zombobase-'
 latest_model = f'models/{prefix}latest.pt'
-today_folder = f'{prefix}{datetime.now():%Y-%m-%d}'
+today_folder = f'{prefix}{datetime.now():{date_format}}'
 train_set = 70
 valid_set = 20
 test_set = 10
@@ -38,13 +37,35 @@ def move_annotations(images, folder):
         image_path = path.join(current_images_dir, image_name)
         label_name = f'{Path(image_name).stem}.txt'
         label_path = path.join(current_labels_dir, label_name)
-        if not path.exists(label_path):
+        if not check_label(label_path):
             remove(image_path)
         else:
             replace(image_path, path.join(destination_images, image_name))
             replace(label_path, path.join(destination_labels, label_name))
 
 
+def check_label(label_path):
+    lines_edited = False
+    if not path.exists(label_path):
+        return False
+    with open(label_path, 'r') as f:
+        lines = f.readlines()
+        for line in lines:
+            for val in line.split(' ')[1:]:
+                if float(val) > 1:
+                    lines.remove(line)
+                    lines_edited = True
+    if len(lines) == 0:
+        return False
+    if not lines_edited:
+        return True
+
+    with open(label_path, 'w') as label_write:
+        label_write.writelines(lines)
+        label_write.close()
+    return True
+
+
 def create_yaml():
     lines = ['names:']
     for c in annotation_classes: