test.py

import cv2
 
# Load a model imported from Tensorflow
tensorflowNet = cv2.dnn.readNetFromTensorflow('frozen_inference_graph.pb', 'frozen_inference_graph.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=(rows, cols), 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 > 0.8: #the confidence threshold
        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.imwrite('result.jpg',img)