title: How do I run my YOLOv3 algorithm with OpenCV in Visual Studio 2022 using GPU?
body: <p>I am new to computer vision and object detection and I have came across this exercise on performing object detection on videos with OpenCV, pre-trained YOLOv3 model and the coco dataset. I am currently using visual studio 2022 with the lastest opencv-python and the needed files such as the weight, cfg and name files. The algorithm works, however, the processing speed when displaying the output video has a low fps. I searched online and most of them suggested of using a GPU.</p>
  <p>In my current setup, I have a <strong>CPU: Intel(R) Core(TM) i5-4570 @3.20GHz</strong> and <strong>GPU: NVDIA GeForce GTX 650 Ti BOOST</strong></p>
  <p>I am planning on making a custom dataset for training later on. Is there anyway I can perform my object detection and training using the GPU on visual studio 2022? If so, what procedures and steps do I need to do in order to do so? Thanks in advance.</p>
  <p>python code:</p>
  <pre><code>import cv2
  import numpy as np
  
  # Load YOLO
  net = cv2.dnn.readNet(&quot;YOLO_COCO/yolov3.weights&quot;, &quot;YOLO_COCO/yolov3.cfg&quot;)
  classes = []
  with open(&quot;YOLO_COCO/coco.names&quot;,&quot;r&quot;) as f:
      classes = [line.strip() for line in f.readlines()]
  layer_names = net.getLayerNames()
  output_layers = [layer_names[i - 1] for i in net.getUnconnectedOutLayers()]
  colors = np.random.uniform(0, 255, size=(len(classes), 3))
  
  # Load Video
  cap = cv2.VideoCapture('videos/overpass.mp4')
  
  while True:
      _, img = cap.read()
  
      height, width, channel = img.shape
  
      # Detecting Objects
      blob = cv2.dnn.blobFromImage(img, 1/255, (416,416), (0, 0, 0), swapRB=True, crop=False) 
  
      net.setInput(blob)
      outs = net.forward(output_layers)
  
  
      # Show information on the screen
      # Initialise variables
      class_ids = []
      confidences = [] 
      boxes = []
  
      for out in outs:
          for detection in out:
              scores = detection[5:]
              class_id = np.argmax(scores)
              confidence = scores[class_id]
              if confidence&gt;0.5:
                  # Object detected
                  center_x = int(detection[0] * width)
                  center_y = int(detection[1] * height)
                  w = int(detection[2] * width)
                  h = int(detection[3] * height)
  
                  # Rectangle coordinates
                  x = int (center_x - w/2)
                  y = int (center_y - h/2)
  
                  boxes.append([x, y, w, h])
                  confidences.append(float(confidence))
                  class_ids.append(class_id)
  
      # Non Maximum Suppresion (Keep one box)
      indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
  
      # Label object
      font = cv2.FONT_HERSHEY_PLAIN
      for i in range (len(boxes)):
          if i in indexes:
              x, y, w, h = boxes[i]
              label = str(classes[class_ids[i]])
              confidence = str(round(confidences[i],2))
              color = colors[i]
              cv2.rectangle(img, (x,y), (x+w, y+h), color, 2)
              cv2.putText(img, label + &quot;  &quot; + confidence, (x, y+20), font, 1, color, 2)
  
      cv2.imshow(&quot;Video&quot;, img)
      key = cv2.waitKey(1)
      if key == 27:
          break
  
  cap.release()
  cv2.destroyAllWindows()
  </code></pre>