83 lines
3.3 KiB
Python
83 lines
3.3 KiB
Python
import os
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import numpy as np
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import cv2 as cv
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import argparse
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from common import findFile
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parser = argparse.ArgumentParser(description='Use this script to run action recognition using 3D ResNet34',
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formatter_class=argparse.ArgumentDefaultsHelpFormatter)
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parser.add_argument('--input', '-i', help='Path to input video file. Skip this argument to capture frames from a camera.')
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parser.add_argument('--model', required=True, help='Path to model.')
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parser.add_argument('--classes', default=findFile('action_recongnition_kinetics.txt'), help='Path to classes list.')
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# To get net download original repository https://github.com/kenshohara/video-classification-3d-cnn-pytorch
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# For correct ONNX export modify file: video-classification-3d-cnn-pytorch/models/resnet.py
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# change
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# - def downsample_basic_block(x, planes, stride):
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# - out = F.avg_pool3d(x, kernel_size=1, stride=stride)
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# - zero_pads = torch.Tensor(out.size(0), planes - out.size(1),
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# - out.size(2), out.size(3),
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# - out.size(4)).zero_()
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# - if isinstance(out.data, torch.cuda.FloatTensor):
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# - zero_pads = zero_pads.cuda()
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# -
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# - out = Variable(torch.cat([out.data, zero_pads], dim=1))
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# - return out
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# To
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# + def downsample_basic_block(x, planes, stride):
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# + out = F.avg_pool3d(x, kernel_size=1, stride=stride)
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# + out = F.pad(out, (0, 0, 0, 0, 0, 0, 0, int(planes - out.size(1)), 0, 0), "constant", 0)
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# + return out
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# To ONNX export use torch.onnx.export(model, inputs, model_name)
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def get_class_names(path):
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class_names = []
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with open(path) as f:
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for row in f:
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class_names.append(row[:-1])
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return class_names
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def classify_video(video_path, net_path):
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SAMPLE_DURATION = 16
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SAMPLE_SIZE = 112
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mean = (114.7748, 107.7354, 99.4750)
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class_names = get_class_names(args.classes)
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net = cv.dnn.readNet(net_path)
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net.setPreferableBackend(cv.dnn.DNN_BACKEND_INFERENCE_ENGINE)
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net.setPreferableTarget(cv.dnn.DNN_TARGET_CPU)
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winName = 'Deep learning image classification in OpenCV'
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cv.namedWindow(winName, cv.WINDOW_AUTOSIZE)
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cap = cv.VideoCapture(video_path)
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while cv.waitKey(1) < 0:
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frames = []
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for _ in range(SAMPLE_DURATION):
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hasFrame, frame = cap.read()
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if not hasFrame:
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exit(0)
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frames.append(frame)
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inputs = cv.dnn.blobFromImages(frames, 1, (SAMPLE_SIZE, SAMPLE_SIZE), mean, True, crop=True)
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inputs = np.transpose(inputs, (1, 0, 2, 3))
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inputs = np.expand_dims(inputs, axis=0)
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net.setInput(inputs)
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outputs = net.forward()
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class_pred = np.argmax(outputs)
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label = class_names[class_pred]
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for frame in frames:
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labelSize, baseLine = cv.getTextSize(label, cv.FONT_HERSHEY_SIMPLEX, 0.5, 1)
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cv.rectangle(frame, (0, 10 - labelSize[1]),
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(labelSize[0], 10 + baseLine), (255, 255, 255), cv.FILLED)
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cv.putText(frame, label, (0, 10), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0))
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cv.imshow(winName, frame)
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if cv.waitKey(1) & 0xFF == ord('q'):
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break
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if __name__ == "__main__":
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args, _ = parser.parse_known_args()
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classify_video(args.input if args.input else 0, args.model)
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