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fb5d697 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 | import sys
import pickle
import cv2
import numpy as np
from moviepy.editor import VideoFileClip
from moviepy.audio.io.AudioFileClip import AudioFileClip
def get_label_str(value_list):
labels = ['background', 'wearer speaking start', 'wearer speaking', 'other person speaking']
decoded_labels = []
for label_str in value_list:
binary_string = ''.join(map(lambda x: '1' if x else '0', label_str))
temp = []
cnt = 0
for i, bit in enumerate(binary_string):
if bit == '1':
cnt+=1
temp.append(labels[i])
if cnt > 1:
temp = ' & '.join(temp)
print(binary_string, temp)
else:
temp = temp[0]
decoded_labels.append(temp)
return decoded_labels
def view_pkl(pickle_file):
with open(pickle_file, 'rb') as f:
data = pickle.load(f)
predict_list = data['prediction']
target_list = data['target']
target_value_list = get_label_str(target_list)
new_list = []
max_list = []
for row in predict_list:
new_row = [round(value / 10000, 3) for value in row]
new_list.append(new_row)
max_index = np.argmax(row)
result = np.zeros_like(row)
result[max_index] = 1
max_list.append(result)
max_value_list = get_label_str(max_list)
predict_list = new_list
return predict_list, target_value_list, max_value_list, target_list
def draw_text(img, text,
font=cv2.FONT_HERSHEY_SIMPLEX,
pos=(0, 0),
font_scale=1,
font_thickness=2,
text_color=(255, 255, 255),
text_color_bg=(0, 0, 0)
):
x, y = pos
text_size, _ = cv2.getTextSize(text, font, font_scale, font_thickness)
text_w, text_h = text_size
cv2.rectangle(img, pos, (x + text_w, y + text_h), text_color_bg, -1)
cv2.putText(img, text, (x, y + text_h + font_scale - 1), font, font_scale, text_color, font_thickness)
return text_size
def video_anno(predict_list, target_list, max_list, input_file_path, output_file_path, test_list):
cap = cv2.VideoCapture(input_file_path)
filename = input_file_path.split('/')[1].split('.')[0]
frame_width = int(cap.get(3))
frame_height = int(cap.get(4))
fps = int(cap.get(5))
frame_count = int(cap.get(7))
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
out = cv2.VideoWriter(output_file_path, fourcc, fps, (frame_width, frame_height))
print(len(predict_list))
frame_number = 0
anno_num = 0
sig_num = 0
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
frame_number+=1
sig_num+=1
if anno_num < len(predict_list):
#print(anno_num)
if frame_number>=384 and sig_num==6:
sig_num = 0
text_GT = f'Ground Truth: {frame_number} {target_list[anno_num]}'
text_PR = f'Model Predict: {frame_number} {max_list[anno_num]}'
text_DT = f'Predict detail: {frame_number} {predict_list[anno_num]}'
anno_num+=1
if sig_num > 5:
sig_num = 0
if frame_number >= 384:
draw_text(frame, text_GT, pos=(10, 10))
draw_text(frame, text_PR, pos=(10, 50))
draw_text(frame, text_DT, pos=(10, 100))
out.write(frame)
cap.release()
out.release()
cv2.destroyAllWindows()
def main(save, save2, pickle_file, audio_path):
video = '/original/video/path.mp4'
video_clip = VideoFileClip(video)
audio_clip = video_clip.audio
audio_clip.write_audiofile(audio_path)
predict_list, target_list, max_list, test_list = view_pkl(pickle_file)
video_anno(predict_list, target_list, max_list, video, save, test_list) # save => 'test_video/00792fa8-988c-4c85-8e80-73eb3ac53e80_anno2.mp4'
video_clip = VideoFileClip(save)
audio_clip = AudioFileClip(audio_path)
video_clip = video_clip.set_audio(audio_clip)
video_clip.write_videofile(save2, codec='libx264', audio_codec='aac')
video_clip.close()
audio_clip.close()
if __name__ == '__main__':
main(sys.argv[1], sys.argv[2], sys.argv[3], sys.argv[4])
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