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Open Source Computer Vision Library
https://opencv.org/
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51 lines
1.7 KiB
51 lines
1.7 KiB
import cv2 as cv |
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import numpy as np |
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import argparse |
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parser = argparse.ArgumentParser( |
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description='This script is used to run style transfer models from ' |
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'https://github.com/jcjohnson/fast-neural-style using OpenCV') |
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parser.add_argument('--input', help='Path to image or video. Skip to capture frames from camera') |
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parser.add_argument('--model', help='Path to .t7 model') |
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parser.add_argument('--width', default=-1, type=int, help='Resize input to specific width.') |
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parser.add_argument('--height', default=-1, type=int, help='Resize input to specific height.') |
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parser.add_argument('--median_filter', default=0, type=int, help='Kernel size of postprocessing blurring.') |
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args = parser.parse_args() |
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net = cv.dnn.readNetFromTorch(args.model) |
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if args.input: |
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cap = cv.VideoCapture(args.input) |
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else: |
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cap = cv.VideoCapture(0) |
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cv.namedWindow('Styled image', cv.WINDOW_NORMAL) |
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while cv.waitKey(1) < 0: |
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hasFrame, frame = cap.read() |
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if not hasFrame: |
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cv.waitKey() |
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break |
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inWidth = args.width if args.width != -1 else frame.shape[1] |
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inHeight = args.height if args.height != -1 else frame.shape[0] |
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inp = cv.dnn.blobFromImage(frame, 1.0, (inWidth, inHeight), |
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(103.939, 116.779, 123.68), swapRB=False, crop=False) |
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net.setInput(inp) |
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out = net.forward() |
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out = out.reshape(3, out.shape[2], out.shape[3]) |
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out[0] += 103.939 |
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out[1] += 116.779 |
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out[2] += 123.68 |
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out /= 255 |
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out = out.transpose(1, 2, 0) |
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t, _ = net.getPerfProfile() |
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freq = cv.getTickFrequency() / 1000 |
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print t / freq, 'ms' |
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if args.median_filter: |
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out = cv.medianBlur(out, args.median_filter) |
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cv.imshow('Styled image', out)
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