add-segment-data-offline-aug

data-offline-aug/readme.md

@@ -7,6 +7,12 @@
 ### 1. object_detection_data_aug.py
 
     目标检测数据集yolo格式离线数据增强脚本.
+    视频教程链接:https://www.bilibili.com/video/BV1bT421k7iq/
+
+### 2. segment_data_aug.py
+
+    语义分割离线数据增强脚本.
+    视频教程链接:
 
 # Reference
 https://github.com/albumentations-team/albumentations  

data-offline-aug/segment_data_aug.py

@@ -0,0 +1,126 @@
+import warnings
+warnings.filterwarnings('ignore')
+import os, shutil, cv2, tqdm
+import numpy as np
+np.random.seed(0)
+import albumentations as A
+from PIL import Image
+from multiprocessing import Pool
+from typing import Callable, Dict, List, Union
+
+# https://github.com/albumentations-team/albumentations
+
+def generate_color_map(num_classes):
+    hsv_colors = [(i * 180 // num_classes, 255, 255) for i in range(num_classes)]
+    rgb_colors = [[0, 0, 0]] + [cv2.cvtColor(np.uint8([[color]]), cv2.COLOR_HSV2BGR)[0][0] for color in hsv_colors]
+    return np.array(rgb_colors, dtype=np.uint8)
+
+IMAGE_PATH = 'dataset/segment/images'
+LABEL_PATH = 'dataset/segment/labels'
+AUG_IMAGE_PATH = 'dataset/segment/images_aug'
+AUG_LABEL_PATH = 'dataset/segment/labels_aug'
+SHOW_SAVE_PATH = 'results'
+COLORS = generate_color_map(20)
+
+ENHANCEMENT_LOOP = 1
+ENHANCEMENT_STRATEGY = A.Compose([
+    A.Compose([
+        A.Affine(scale=[0.5, 1.5], translate_percent=[0.0, 0.3], rotate=[-360, 360], shear=[-45, 45], keep_ratio=True, cval_mask=0, p=0.5), # Augmentation to apply affine transformations to images.
+        A.BBoxSafeRandomCrop(erosion_rate=0.2, p=0.1), # Crop a random part of the input without loss of bboxes.
+        A.D4(p=0.1), # Applies one of the eight possible D4 dihedral group transformations to a square-shaped input, maintaining the square shape. These transformations correspond to the symmetries of a square, including rotations and reflections.
+        A.ElasticTransform(p=0.1), # Elastic deformation of images as described in [Simard2003]_ (with modifications).
+        A.Flip(p=0.1), # Flip the input either horizontally, vertically or both horizontally and vertically.
+        A.GridDistortion(p=0.1), # Applies grid distortion augmentation to images, masks, and bounding boxes. This technique involves dividing the image into a grid of cells and randomly displacing the intersection points of the grid, resulting in localized distortions.
+        A.Perspective(p=0.1), # Perform a random four point perspective transform of the input.
+    ], p=1.0),
+
+    A.Compose([
+        A.GaussNoise(p=0.1), # Apply Gaussian noise to the input image.
+        A.ISONoise(p=0.1), # Apply camera sensor noise.
+        A.ImageCompression(quality_lower=50, quality_upper=100, p=0.1), # Decreases image quality by Jpeg, WebP compression of an image.
+        A.RandomBrightnessContrast(p=0.1), # Randomly change brightness and contrast of the input image.
+        A.RandomFog(p=0.1), # Simulates fog for the image.
+        A.RandomRain(p=0.1), # Adds rain effects to an image.
+        A.RandomSnow(p=0.1), # Bleach out some pixel values imitating snow.
+        A.RandomShadow(p=0.1), # Simulates shadows for the image
+        A.RandomSunFlare(p=0.1), # Simulates Sun Flare for the image
+        A.ToGray(p=0.1), # Convert the input RGB image to grayscale
+    ], p=1.0)
+
+    # A.OneOf([
+    #     A.GaussNoise(p=1.0), # Apply Gaussian noise to the input image.
+    #     A.ISONoise(p=1.0), # Apply camera sensor noise.
+    #     A.ImageCompression(quality_lower=50, quality_upper=100, p=1.0), # Decreases image quality by Jpeg, WebP compression of an image.
+    #     A.RandomBrightnessContrast(p=1.0), # Randomly change brightness and contrast of the input image.
+    #     A.RandomFog(p=1.0), # Simulates fog for the image.
+    #     A.RandomRain(p=1.0), # Adds rain effects to an image.
+    #     A.RandomSnow(p=1.0), # Bleach out some pixel values imitating snow.
+    #     A.RandomShadow(p=1.0), # Simulates shadows for the image
+    #     A.RandomSunFlare(p=1.0), # Simulates Sun Flare for the image
+    #     A.ToGray(p=1.0), # Convert the input RGB image to grayscale
+    # ], p=1.0),
+], is_check_shapes=False)
+
+def draw_segments(image, mask):
+    blended_image = cv2.addWeighted(image, 0.7, COLORS[mask], 0.3, 0)
+    return blended_image
+
+def show_labels(images_base_path, labels_base_path):
+    if os.path.exists(SHOW_SAVE_PATH):
+        shutil.rmtree(SHOW_SAVE_PATH)
+    os.makedirs(SHOW_SAVE_PATH, exist_ok=True)
+
+    for images_name in tqdm.tqdm(os.listdir(images_base_path)):
+        file_heads, _ = os.path.splitext(images_name)
+        images_path = f'{images_base_path}/{images_name}'
+        labels_path = f'{labels_base_path}/{file_heads}.png'
+        if os.path.exists(labels_path):
+            images = cv2.imread(images_path)
+            masks = np.array(Image.open(labels_path))
+            print(np.unique(masks))
+            images = draw_segments(images, masks)
+            cv2.imwrite(f'{SHOW_SAVE_PATH}/{images_name}', images)
+            print(f'{SHOW_SAVE_PATH}/{images_name} save success...')
+        else:
+            print(f'{labels_path} label file not found...')
+
+def data_aug_single(images_name):
+    file_heads, postfix = os.path.splitext(images_name)
+    images_path = f'{IMAGE_PATH}/{images_name}'
+    labels_path = f'{LABEL_PATH}/{file_heads}.jpg'
+    if os.path.exists(labels_path):
+        images = Image.open(images_path)
+        masks = np.array(Image.open(labels_path))
+        for i in range(ENHANCEMENT_LOOP):
+            new_images_name = f'{AUG_IMAGE_PATH}/{file_heads}_{i:0>3}{postfix}'
+            new_labels_name = f'{AUG_LABEL_PATH}/{file_heads}_{i:0>3}.png'
+            try:
+                transformed = ENHANCEMENT_STRATEGY(image=np.array(images), masks=[masks])
+            except:
+                continue
+            transformed_image = transformed['image']
+            transformed_masks = transformed['masks'][0]
+
+            cv2.imwrite(new_images_name, cv2.cvtColor(transformed_image, cv2.COLOR_RGB2BGR))
+            Image.fromarray(np.array(transformed_masks)).save(new_labels_name)
+            print(f'{new_images_name} and {new_labels_name} save success...')
+    else:
+        print(f'{labels_path} label file not found...')
+
+def data_aug():
+    if os.path.exists(AUG_IMAGE_PATH):
+        shutil.rmtree(AUG_IMAGE_PATH)
+    if os.path.exists(AUG_LABEL_PATH):
+        shutil.rmtree(AUG_LABEL_PATH)
+
+    os.makedirs(AUG_IMAGE_PATH, exist_ok=True)
+    os.makedirs(AUG_LABEL_PATH, exist_ok=True)
+
+    for images_name in tqdm.tqdm(os.listdir(IMAGE_PATH)):
+        data_aug_single(images_name)
+
+if __name__ == '__main__':
+    show_labels(IMAGE_PATH, LABEL_PATH)
+    # show_labels(AUG_IMAGE_PATH, AUG_LABEL_PATH)
+
+    # data_aug()

yolo-improve/yolov8v10-project.md

@@ -1346,4 +1346,5 @@
     3. 新增Wavelet Convolutions for Large Receptive Fields中的WTConv改进C2f.
     4. 新增UBRFC-Net中的Adaptive Fine-Grained Channel Attention.
     5. 更新使用教程.
-    6. 百度云视频增加20240720更新说明.
+    6. 百度云视频增加20240720更新说明.
+    7. 增加v10多个改进、主要是上下采样系列.