如何在python中的OpenCv中添加自定义Keras模型

Question

我创建了一个模型来分类两种类型的鞋子

现在如何在 OpenCv（视频对象检测）中部署它？

提前致谢

Answer 1

您可以在 OpenCV DNN 模块的帮助下做到这一点：

import cv2

# Load a model imported from Tensorflow
tensorflowNet = cv2.dnn.readNetFromTensorflow('card_graph/frozen_inference_graph.pb', 'exported_pbtxt/output.pbtxt')

# Input image
img = cv2.imread('image.jpg')
rows, cols, channels = img.shape

# Use the given image as input, which needs to be blob(s).
tensorflowNet.setInput(cv2.dnn.blobFromImage(img, size=(300, 300), swapRB=True, crop=False))

# Runs a forward pass to compute the net output
networkOutput = tensorflowNet.forward()

# Loop on the outputs
for detection in networkOutput[0,0]:

    score = float(detection[2])
    if score > 0.9:

        left = detection[3] * cols
        top = detection[4] * rows
        right = detection[5] * cols
        bottom = detection[6] * rows

        #draw a red rectangle around detected objects
        cv2.rectangle(img, (int(left), int(top)), (int(right), int(bottom)), (0, 0, 255), thickness=2)

# Show the image with a rectagle surrounding the detected objects 
cv2.imshow('Image', img)
cv2.waitKey()
cv2.destroyAllWindows()

您需要冻结推理图和 pbtxt 文件才能在 OpenCV 中运行您的模型