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SivananthamandGitHub
a869657f25
* Fix python linter Signed-off-by: Sivanantham Chinnaiyan <[email protected]> * Format e2e tests Signed-off-by: Sivanantham Chinnaiyan <[email protected]> * Format hack, docs Signed-off-by: Sivanantham Chinnaiyan <[email protected]> * Add py-fmt target Signed-off-by: Sivanantham Chinnaiyan <[email protected]> * Update python lint workflow Signed-off-by: Sivanantham Chinnaiyan <[email protected]> * Rebase master Signed-off-by: Sivanantham Chinnaiyan <[email protected]> --------- Signed-off-by: Sivanantham Chinnaiyan <[email protected]>
42 lines
1.2 KiB
Python
42 lines
1.2 KiB
Python
import cv2
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import numpy as np
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def resize_short(img, target_size):
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"""resize_short"""
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percent = float(target_size) / min(img.shape[0], img.shape[1])
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resized_width = int(round(img.shape[1] * percent))
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resized_height = int(round(img.shape[0] * percent))
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resized = cv2.resize(img, (resized_width, resized_height))
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return resized
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def crop_image(img, target_size, center):
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"""crop_image"""
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height, width = img.shape[:2]
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size = target_size
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if center:
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w_start = (width - size) / 2
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h_start = (height - size) / 2
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else:
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w_start = np.random.randint(0, width - size + 1)
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h_start = np.random.randint(0, height - size + 1)
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w_end = w_start + size
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h_end = h_start + size
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img = img[int(h_start) : int(h_end), int(w_start) : int(w_end), :]
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return img
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def preprocess(img):
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mean = [0.485, 0.456, 0.406]
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std = [0.229, 0.224, 0.225]
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img = resize_short(img, 224)
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img = crop_image(img, 224, True)
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# bgr-> rgb && hwc->chw
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img = img[:, :, ::-1].astype("float32").transpose((2, 0, 1)) / 255
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img_mean = np.array(mean).reshape((3, 1, 1))
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img_std = np.array(std).reshape((3, 1, 1))
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img -= img_mean
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img /= img_std
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return img[np.newaxis, :]
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