Initial commit

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)