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Backup turn-taking-dataset from MIR NAS
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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])