Initial commit

ML/Pytorch/Basics/albumentations_tutorial/classification.py

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+import cv2
+import albumentations as A
+import numpy as np
+from utils import plot_examples
+from PIL import Image
+
+image = Image.open("images/elon.jpeg")
+
+transform = A.Compose(
+    [
+        A.Resize(width=1920, height=1080),
+        A.RandomCrop(width=1280, height=720),
+        A.Rotate(limit=40, p=0.9, border_mode=cv2.BORDER_CONSTANT),
+        A.HorizontalFlip(p=0.5),
+        A.VerticalFlip(p=0.1),
+        A.RGBShift(r_shift_limit=25, g_shift_limit=25, b_shift_limit=25, p=0.9),
+        A.OneOf([
+            A.Blur(blur_limit=3, p=0.5),
+            A.ColorJitter(p=0.5),
+        ], p=1.0),
+    ]
+)
+
+images_list = [image]
+image = np.array(image)
+for i in range(15):
+    augmentations = transform(image=image)
+    augmented_img = augmentations["image"]
+    images_list.append(augmented_img)
+plot_examples(images_list)
+

ML/Pytorch/Basics/albumentations_tutorial/detection.py

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+import cv2
+import albumentations as A
+import numpy as np
+from utils import plot_examples
+from PIL import Image
+
+image = cv2.imread("images/cat.jpg")
+image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+bboxes = [[13, 170, 224, 410]]
+
+# Pascal_voc (x_min, y_min, x_max, y_max), YOLO, COCO
+
+transform = A.Compose(
+    [
+        A.Resize(width=1920, height=1080),
+        A.RandomCrop(width=1280, height=720),
+        A.Rotate(limit=40, p=0.9, border_mode=cv2.BORDER_CONSTANT),
+        A.HorizontalFlip(p=0.5),
+        A.VerticalFlip(p=0.1),
+        A.RGBShift(r_shift_limit=25, g_shift_limit=25, b_shift_limit=25, p=0.9),
+        A.OneOf([
+            A.Blur(blur_limit=3, p=0.5),
+            A.ColorJitter(p=0.5),
+        ], p=1.0),
+    ], bbox_params=A.BboxParams(format="pascal_voc", min_area=2048,
+                                min_visibility=0.3, label_fields=[])
+)
+
+images_list = [image]
+saved_bboxes = [bboxes[0]]
+for i in range(15):
+    augmentations = transform(image=image, bboxes=bboxes)
+    augmented_img = augmentations["image"]
+
+    if len(augmentations["bboxes"]) == 0:
+        continue
+
+    images_list.append(augmented_img)
+    saved_bboxes.append(augmentations["bboxes"][0])
+
+plot_examples(images_list, saved_bboxes)

ML/Pytorch/Basics/albumentations_tutorial/full_pytorch_example.py

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+import torch
+import numpy as np
+import cv2
+from PIL import Image
+import torch.nn as nn
+import albumentations as A
+from albumentations.pytorch import ToTensorV2
+from torch.utils.data import Dataset
+import os
+
+class ImageFolder(Dataset):
+    def __init__(self, root_dir, transform=None):
+        super(ImageFolder, self).__init__()
+        self.data = []
+        self.root_dir = root_dir
+        self.transform = transform
+        self.class_names = os.listdir(root_dir)
+
+        for index, name in enumerate(self.class_names):
+            files = os.listdir(os.path.join(root_dir, name))
+            self.data += list(zip(files, [index]*len(files)))
+
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, index):
+        img_file, label = self.data[index]
+        root_and_dir = os.path.join(self.root_dir, self.class_names[label])
+        image = np.array(Image.open(os.path.join(root_and_dir, img_file)))
+
+        if self.transform is not None:
+            augmentations = self.transform(image=image)
+            image = augmentations["image"]
+
+        return image, label
+
+
+transform = A.Compose(
+    [
+        A.Resize(width=1920, height=1080),
+        A.RandomCrop(width=1280, height=720),
+        A.Rotate(limit=40, p=0.9, border_mode=cv2.BORDER_CONSTANT),
+        A.HorizontalFlip(p=0.5),
+        A.VerticalFlip(p=0.1),
+        A.RGBShift(r_shift_limit=25, g_shift_limit=25, b_shift_limit=25, p=0.9),
+        A.OneOf([
+            A.Blur(blur_limit=3, p=0.5),
+            A.ColorJitter(p=0.5),
+        ], p=1.0),
+        A.Normalize(
+            mean=[0, 0, 0],
+            std=[1, 1, 1],
+            max_pixel_value=255,
+        ),
+        ToTensorV2(),
+    ]
+)
+
+dataset = ImageFolder(root_dir="cat_dogs", transform=transform)
+
+for x,y in dataset:
+    print(x.shape)

ML/Pytorch/Basics/albumentations_tutorial/segmentation.py

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+import cv2
+import albumentations as A
+import numpy as np
+from utils import plot_examples
+from PIL import Image
+
+image = Image.open("images/elon.jpeg")
+mask = Image.open("images/mask.jpeg")
+mask2 = Image.open("images/second_mask.jpeg")
+
+transform = A.Compose(
+    [
+        A.Resize(width=1920, height=1080),
+        A.RandomCrop(width=1280, height=720),
+        A.Rotate(limit=40, p=0.9, border_mode=cv2.BORDER_CONSTANT),
+        A.HorizontalFlip(p=0.5),
+        A.VerticalFlip(p=0.1),
+        A.RGBShift(r_shift_limit=25, g_shift_limit=25, b_shift_limit=25, p=0.9),
+        A.OneOf([
+            A.Blur(blur_limit=3, p=0.5),
+            A.ColorJitter(p=0.5),
+        ], p=1.0),
+    ]
+)
+
+images_list = [image]
+image = np.array(image)
+mask = np.array(mask) # np.asarray(mask), np.array(mask)
+mask2 = np.array(mask2)
+for i in range(4):
+    augmentations = transform(image=image, masks=[mask, mask2])
+    augmented_img = augmentations["image"]
+    augmented_masks = augmentations["masks"]
+    images_list.append(augmented_img)
+    images_list.append(augmented_masks[0])
+    images_list.append(augmented_masks[1])
+plot_examples(images_list)

ML/Pytorch/Basics/albumentations_tutorial/utils.py

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+import random
+import cv2
+from matplotlib import pyplot as plt
+import matplotlib.patches as patches
+import numpy as np
+import albumentations as A
+
+
+def visualize(image):
+    plt.figure(figsize=(10, 10))
+    plt.axis('off')
+    plt.imshow(image)
+    plt.show()
+
+
+def plot_examples(images, bboxes=None):
+    fig = plt.figure(figsize=(15, 15))
+    columns = 4
+    rows = 5
+
+    for i in range(1, len(images)):
+        if bboxes is not None:
+            img = visualize_bbox(images[i - 1], bboxes[i - 1], class_name="Elon")
+        else:
+            img = images[i-1]
+        fig.add_subplot(rows, columns, i)
+        plt.imshow(img)
+    plt.show()
+
+
+# From https://albumentations.ai/docs/examples/example_bboxes/
+def visualize_bbox(img, bbox, class_name, color=(255, 0, 0), thickness=5):
+    """Visualizes a single bounding box on the image"""
+    x_min, y_min, x_max, y_max = map(int, bbox)
+    cv2.rectangle(img, (x_min, y_min), (x_max, y_max), color, thickness)
+    return img