该脚本的目标是使用Keras现有的图像预处理模块对视频数据进行增强。在这个原型中，一个样本视频被分割成一系列帧并进行处理，最终步骤包括执行随机旋转、平移、剪切和缩放操作：
from keras import backend as Kfrom keras.preprocessing.image import random_rotation, random_shift, random_shear, random_zoomK.set_image_dim_ordering("th")import cv2import numpy as npvideo_file_path = "./training-data/yes/1.mov"samples_generated_per_sample = 10self_rows = 100self_columns = 150self_frames_per_sequence = 45# haar cascades for localizing oral regionface_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')mouth_cascade = cv2.CascadeClassifier('haarcascade_mcs_mouth.xml')video = cv2.VideoCapture(video_file_path)success, frame = video.read()frames = []success = True# convert to grayscale, localize oral region, equalize dimensions, # normalize pixels, equalize lengths, and accumulate valid frames while success: success, frame = video.read() if success: # convert to grayscale frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # localize single facial region faces_coords = face_cascade.detectMultiScale(frame, 1.3, 5) if len(faces_coords) == 1: face_x, face_y, face_w, face_h = faces_coords[0] frame = frame[face_y:face_y + face_h, face_x:face_x + face_w] # localize oral region mouth_coords = mouth_cascade.detectMultiScale(frame, 1.3, 5) threshold = 0 for (mouth_x, mouth_y, mouth_w, mouth_h) in mouth_coords: if (mouth_y > threshold): threshold = mouth_y valid_mouth_coords = (mouth_x, mouth_y, mouth_w, mouth_h) else: pass mouth_x, mouth_y, mouth_w, mouth_h = valid_mouth_coords frame = frame[mouth_y:mouth_y + mouth_h, mouth_x:mouth_x + mouth_w] frames.append(frame) # ignore multiple facial region detections else: pass# pre-pad short sequences and equalize sequence lengthsif len(frames) < self_frames_per_sequence: frames = [frames[0]]*(self_frames_per_sequence - len(frames)) + framesframes = frames[0:self_frames_per_sequence]frames = np.asarray(frames)rotated_frames = random_rotation(frames, rg=45)shifted_frames = random_shift(rotated_frames, wrg=0.25, hrg=0.25)sheared_frames = random_shear(shifted_frames, intensity=0.79)zoomed_frames = random_zoom(sheared_frames, zoom_range=(1.25, 1.25))当脚本运行时，会出现以下错误：
回答：
问题在于帧的尺寸不一致。解决方案是在应用变换之前先将帧的尺寸统一：
# pre-pad short sequences, equalize frame dimensions, and equalize sequence lengthsif len(frames) < self_frames_per_sequence: frames = [frames[0]]*(self_frames_per_sequence - len(frames)) + framesframes = frames[0:self_frames_per_sequence]frames = [cv2.resize(frame, (self_columns, self_rows)).astype('float32') for frame in frames]frames = np.asarray(frames)rotated_frames = random_rotation(frames, rg=45)shifted_frames = random_shift(rotated_frames, wrg=0.25, hrg=0.25)sheared_frames = random_shear(shifted_frames, intensity=0.79)zoomed_frames = random_zoom(sheared_frames, zoom_range=(1.25, 1.25))