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import cv2
import numpy as np
def video_Frames(clip_path,img_size = 64):
video = cv2.VideoCapture(clip_path)
frame_count = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
for count in range(frame_count):
flag, frame = video.read()
if not flag:
break
frame = cv2.resize(frame,(img_size,img_size))
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
#normalizing the pixels between 0 and 1
frame = frame/255.0
yield frame
video.release()
def load_video(folder_path):
imgs = []
frames_generator = video_Frames(folder_path)
frames_array = np.array(list(frames_generator))
imgs.append(frames_array)
real_imgs = np.array(imgs)
return imgs
def eval_real(real_imgs, model):
pred1 = model.predict(real_imgs)
pred1_max = pred1.argmax()
return pred1_max |