104 lines
4.2 KiB
Python
104 lines
4.2 KiB
Python
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#!/usr/bin/env python
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'''
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This sample using FlowNet v2 model to calculate optical flow.
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Original paper: https://arxiv.org/abs/1612.01925.
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Original repo: https://github.com/lmb-freiburg/flownet2.
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Download the converted .caffemodel model from https://drive.google.com/open?id=16qvE9VNmU39NttpZwZs81Ga8VYQJDaWZ
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and .prototxt from https://drive.google.com/file/d/1RyNIUsan1ZOh2hpYIH36A-jofAvJlT6a/view?usp=sharing.
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Otherwise download original model from https://lmb.informatik.uni-freiburg.de/resources/binaries/flownet2/flownet2-models.tar.gz,
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convert .h5 model to .caffemodel and modify original .prototxt using .prototxt from link above.
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'''
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import argparse
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import os.path
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import numpy as np
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import cv2 as cv
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class OpticalFlow(object):
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def __init__(self, proto, model, height, width):
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self.net = cv.dnn.readNetFromCaffe(proto, model)
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self.net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV)
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self.height = height
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self.width = width
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def compute_flow(self, first_img, second_img):
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inp0 = cv.dnn.blobFromImage(first_img, size=(self.width, self.height))
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inp1 = cv.dnn.blobFromImage(second_img, size=(self.width, self.height))
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self.net.setInput(inp0, "img0")
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self.net.setInput(inp1, "img1")
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flow = self.net.forward()
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output = self.motion_to_color(flow)
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return output
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def motion_to_color(self, flow):
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arr = np.arange(0, 255, dtype=np.uint8)
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colormap = cv.applyColorMap(arr, cv.COLORMAP_HSV)
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colormap = colormap.squeeze(1)
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flow = flow.squeeze(0)
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fx, fy = flow[0, ...], flow[1, ...]
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rad = np.sqrt(fx**2 + fy**2)
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maxrad = rad.max() if rad.max() != 0 else 1
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ncols = arr.size
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rad = rad[..., np.newaxis] / maxrad
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a = np.arctan2(-fy / maxrad, -fx / maxrad) / np.pi
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fk = (a + 1) / 2.0 * (ncols - 1)
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k0 = fk.astype(np.int)
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k1 = (k0 + 1) % ncols
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f = fk[..., np.newaxis] - k0[..., np.newaxis]
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col0 = colormap[k0] / 255.0
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col1 = colormap[k1] / 255.0
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col = (1 - f) * col0 + f * col1
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col = np.where(rad <= 1, 1 - rad * (1 - col), col * 0.75)
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output = (255.0 * col).astype(np.uint8)
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return output
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Use this script to calculate optical flow using FlowNetv2',
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formatter_class=argparse.ArgumentDefaultsHelpFormatter)
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parser.add_argument('-input', '-i', required=True, help='Path to input video file. Skip this argument to capture frames from a camera.')
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parser.add_argument('--height', default=320, type=int, help='Input height')
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parser.add_argument('--width', default=448, type=int, help='Input width')
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parser.add_argument('--proto', '-p', default='FlowNet2_deploy_anysize.prototxt', help='Path to prototxt.')
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parser.add_argument('--model', '-m', default='FlowNet2_weights.caffemodel', help='Path to caffemodel.')
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args, _ = parser.parse_known_args()
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if not os.path.isfile(args.model) or not os.path.isfile(args.proto):
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raise OSError("Prototxt or caffemodel not exist")
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winName = 'Calculation optical flow in OpenCV'
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cv.namedWindow(winName, cv.WINDOW_NORMAL)
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cap = cv.VideoCapture(args.input if args.input else 0)
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hasFrame, first_frame = cap.read()
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divisor = 64.
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var = {}
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var['ADAPTED_WIDTH'] = int(np.ceil(args.width/divisor) * divisor)
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var['ADAPTED_HEIGHT'] = int(np.ceil(args.height/divisor) * divisor)
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var['SCALE_WIDTH'] = args.width / float(var['ADAPTED_WIDTH'])
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var['SCALE_HEIGHT'] = args.height / float(var['ADAPTED_HEIGHT'])
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config = ''
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proto = open(args.proto).readlines()
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for line in proto:
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for key, value in var.items():
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tag = "$%s$" % key
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line = line.replace(tag, str(value))
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config += line
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caffemodel = open(args.model, 'rb').read()
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opt_flow = OpticalFlow(bytearray(config.encode()), caffemodel, var['ADAPTED_HEIGHT'], var['ADAPTED_WIDTH'])
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while cv.waitKey(1) < 0:
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hasFrame, second_frame = cap.read()
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if not hasFrame:
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break
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flow = opt_flow.compute_flow(first_frame, second_frame)
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first_frame = second_frame
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cv.imshow(winName, flow)
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