md.py

import numpy as np
import cv2
import time


path_model = "~/test/Downloads/"

# Read Network
model_name = "model-f6b98070.onnx"; # MiDaS v2.1 Large
#model_name = "model-small.onnx"; # MiDaS v2.1 Small


# Load the DNN model
print(path_model+model_name)
model = cv2.dnn.readNet(path_model + model_name)


if (model.empty()):
    print("Could not load the neural net! - Check path")


# Set backend and target to CUDA to use GPU
model.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA)
model.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA)
 
# Webcam
cap = cv2.VideoCapture(0)


while cap.isOpened():

    # Read in the image
    success, img = cap.read()

    imgHeight, imgWidth, channels = img.shape

    # start time to calculate FPS
    start = time.time()


    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)


    # Create Blob from Input Image
    # MiDaS v2.1 Large ( Scale : 1 / 255, Size : 384 x 384, Mean Subtraction : ( 123.675, 116.28, 103.53 ), Channels Order : RGB )
    blob = cv2.dnn.blobFromImage(img, 1/255., (384,384), (123.675, 116.28, 103.53), True, False)

    # MiDaS v2.1 Small ( Scale : 1 / 255, Size : 256 x 256, Mean Subtraction : ( 123.675, 116.28, 103.53 ), Channels Order : RGB )
    #blob = cv2.dnn.blobFromImage(img, 1/255., (256,256), (123.675, 116.28, 103.53), True, False)

    # Set input to the model
    model.setInput(blob)

    # Make forward pass in model
    output = model.forward()
    
    output = output[0,:,:]
    output = cv2.resize(output, (imgWidth, imgHeight))

    # Normalize the output
    output = cv2.normalize(output, None, 0, 1, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_32F)



    # End time
    end = time.time()
    # calculate the FPS for current frame detection
    fps = 1 / (end-start)
    # Show FPS
    cv2.putText(img, f"{fps:.2f} FPS", (20, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
    
    img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)

    cv2.imshow('image', img)
    cv2.imshow('Depth Map', output)


    if cv2.waitKey(10) & 0xFF == ord('q'):
        break

 

cap.release()
cv2.destroyAllWindows()