一个目标实时检测的模型

utils/data_augmentation.py

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+import numpy as np
+import cv2
+
+def get_random_patch(image, patch_size):
+    h, w = image.shape[1:3]
+    x = np.random.randint(0, w - patch_size)
+    y = np.random.randint(0, h - patch_size)
+    return image[:, y:y+patch_size, x:x+patch_size]
+
+def get_random_homography(max_rotation=30, max_translation=20):
+    theta = np.random.uniform(-max_rotation, max_rotation) * np.pi / 180.0
+    tx = np.random.uniform(-max_translation, max_translation)
+    ty = np.random.uniform(-max_translation, max_translation)
+    cos_theta = np.cos(theta)
+    sin_theta = np.sin(theta)
+    H = np.array([
+        [cos_theta, -sin_theta, tx],
+        [sin_theta, cos_theta, ty],
+        [0, 0, 1]
+    ])
+    return H
+
+def apply_homography_to_image(image, H, output_size):
+    warped = np.zeros_like(image)
+    for c in range(image.shape[0]):
+        warped[c] = cv2.warpPerspective(image[c], H, output_size, flags=cv2.INTER_NEAREST)
+    return warped
+
+def generate_training_pair(image, patch_size):
+    patch = get_random_patch(image, patch_size)
+    H = get_random_homography()
+    warped_patch = apply_homography_to_image(patch, H, (patch_size, patch_size))
+    return patch, warped_patch, H