调整视频数据大小以适应模型.预测

Question

模型按照以下方式进行训练

model = keras.Sequential()
model.add(Conv2D(64, (3, 3), input_shape=(16, 120, 120, 3), padding='same', activation='relu'))

如何调整视频大小以将其传递给下面的trained_model.predict进行预测？

trained_model = load_model("cyclist.h5")
trained_model.predict('7.avi')

Answer 1

它是这样工作的

import cv2
import numpy as np
file = '7.avi'
cap = cv2.VideoCapture(file)
frameCount = 16
frameWidth = 120
frameHeight = 120
buf = np.empty((frameCount, frameHeight, frameWidth, 3), np.dtype('uint8'))
fc = 0
ret = True
while (fc < frameCount and ret):
buf[fc] = cv2.resize(cap.read()[1], (frameWidth, frameHeight), fx=0, fy=0, interpolation=cv2.INTER_CUBIC)
fc += 1
cap.release()
cv2.destroyAllWindows()
trained_model.predict(np.expand_dims(buf, axis=0))

Answer 2

以下是您需要做的：

• 加载模型
• 使用OpenCV加载视频文件的帧
• 重塑框架形状并进行预处理

from tensorflow.keras.models import load_model
import numpy as np
import cv2
trained_model = load_model("cyclist.h5")
image_width = 120
image_height = 120
batch_size = 16
cap = cv2.VideoCapture('7.avi')
while cap.isOpened():
# Load a frame
ret, image = cap.read()
if ret:
# print(image.shape)
image = cv2.resize(image, (image_width, image_height))
image_to_predict = np.expand_dims(image, axis=0)
# print(image_to_predict.shape)
# Make a prediction
prediction = trained_model.predict(image_to_predict, batch_size=batch_size)
print(prediction)

# press q to exit
if cv2.waitKey(1) == ord('q'):
cap.release()
cv2.destroyAllWindows()
break