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import numpy as np
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import cv2
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def format_img_size(img):
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""" formats the image size based on config """
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img_min_side = float(640)
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(height,width,_) = img.shape
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if width <= height:
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ratio = img_min_side/width
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new_height = int(ratio * height)
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new_width = int(img_min_side)
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else:
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ratio = img_min_side/height
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new_width = int(ratio * width)
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new_height = int(img_min_side)
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img = cv2.resize(img, (new_width, new_height), interpolation=cv2.INTER_CUBIC)
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return img, ratio
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def format_img_channels(img):
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""" formats the image channels based on config """
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img_channel_mean = [103.939, 116.779, 123.68]
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img_scaling_factor = 1.0
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img = img[:, :, (2, 1, 0)]
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img = img.astype(np.float32)
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img[:, :, 0] -= img_channel_mean[0]
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img[:, :, 1] -= img_channel_mean[1]
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img[:, :, 2] -= img_channel_mean[2]
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img /= img_scaling_factor
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img = np.transpose(img, (2, 0, 1))
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img = np.expand_dims(img, axis=0)
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return img
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def format_img(img):
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""" formats an image for model prediction based on config """
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img, ratio = format_img_size(img)
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img = format_img_channels(img)
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return img, ratio
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def get_real_coordinates(ratio, x1, y1, x2, y2):
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real_x1 = int(round(x1 // ratio))
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real_y1 = int(round(y1 // ratio))
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real_x2 = int(round(x2 // ratio))
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real_y2 = int(round(y2 // ratio))
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return (real_x1, real_y1, real_x2 ,real_y2)
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