How to make gaussian distribution in pytorch?

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  im trying to get a screenshot of a window as fast and then inference on yolov5 it works but sometimes it doesnt detect very well compared to using detect.py on the same image. i think its probably because of img shape or array but i dont know where or how to edit those to make it work. can anyone help me with this please?

  import torch
  import numpy as np
  import win32gui
  import win32ui
  import win32con
  
  
  
  w = 800 # set this
  h = 600 # set this
  bmpfilenamename = "color.bmp" #set this
  windowname = 'put windowname'
  
  
  def screenshot():
      hwnd = win32gui.FindWindow(None, windowname)
      wDC = win32gui.GetWindowDC(hwnd)
      dcObj=win32ui.CreateDCFromHandle(wDC)
      cDC=dcObj.CreateCompatibleDC()
      dataBitMap = win32ui.CreateBitmap()
      dataBitMap.CreateCompatibleBitmap(dcObj, w, h)
      cDC.SelectObject(dataBitMap)
      cDC.BitBlt((0,0),(w, h) , dcObj, (0,0), win32con.SRCCOPY)
  
  #save the screenshot
  #dataBitMap.SaveBitmapFile(cDC, bmpfilenamename)
  
  signedIntsArray = dataBitMap.GetBitmapBits(True)
  img = np.frombuffer(signedIntsArray, dtype='uint8')
  img.shape = (h,w,4)
  
  # Free Resources
  dcObj.DeleteDC()
  cDC.DeleteDC()
  win32gui.ReleaseDC(hwnd, wDC)
  win32gui.DeleteObject(dataBitMap.GetHandle())
  
  #img = img[..., ::-1]
  #img = np.ascontiguousarray(img)
  
  return img
  
  #load
  model = torch.hub.load('./', 'custom', path='yolov5s.pt', source='local')
  
  
  #inference
  test = screenshot()
  results = model(test)
  boxes = results.pandas().xyxy[0]
  print (boxes)
  

  edit : i figured you can do it by changing the code from this

  results = model(test) 
  

  to this

  results = model(cv.cvtColor(test, cv.COLOR_BGR2RGB))
  

  but isnt this code supposed to do the same? for some reason this one wont work

  img[:    ,:    ,::-1]
  
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  I have a tensor a = torch.arange(6).reshape(2,3), and another tensor b=(torch.rand(a.size())> 0.5).int().nonzero().

  I want to create a new tensor that contains only values from a of the indices that are indicated by b.

  For example:

  a = torch.arange(6).reshape(2,3) # tensor([[0, 1, 2],
                                   #         [3, 4, 5]])
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                                                  #         [0, 2],
                                                  #         [1, 0],
                                                  #         [1, 1]])
  

  The desired output is:

  tensor([1,2,3,4])

  I know that I can iterate over the values of b and access those values in a as indices but I wanted to know if there is a better Pytorch way to to this (using tensor operations only).

  ** The shape of the output tensor doesn't really matter, I just need to have a tensor with only the values indicated by b.
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  I want to print the accuracy of my trained model. I do understand when I use the model through the camera it shows me the accuracy on the box only, but I want to see the accuracy of my whole model rather than a single instance of my image.

  cap  = cv2.VideoCapture(0)
  
  # loop through labels
  for label in labels:
      print('Collecting images for {}'.format(label)) #so that we can see when transitioning through images
      time.sleep(5)  #sleep or wait for 5 seconds when transitioning between image for other class
      
      # Loop through image range
      for img_num in range(number_imgs):
          print('Collecting images for {}, image number {}'.format(label, img_num))
          
          # webcam feed
          ret, frame = cap.read()   # read the feed from the web cam and store it in given vars
          
          # Naming image path
          imgname = os.path.join(IMAGES_PATH, label+'.'+str(uuid.uuid1())+'.jpg')
          
          #Writes out image to file
          cv2.imwrite(imgname,frame)
          
          # Render to screen
          cv2.imshow('Image Collection', frame)
          
          # 2 sec delay between diff captures
          time.sleep(2)
          
          if cv2.waitKey(10) & 0xFF == ord('q'):
              break
  cap.release()
  cv2.destroyAllWindows()
  

  !cd yolov5 && python train.py --img 320 --batch 16 --epochs 500 --data dataset.yml --weights yolov5s.pt
  

  model = torch.hub.load('ultralytics/yolov5', 'custom', path='yolov5/runs/train/exp/weights/last.pt', force_reload=True)
  while cap.isOpened():
      ret, frame = cap.read()
      
      # Make detections 
      results = model(frame)
      
      cv2.imshow('YOLO', np.squeeze(results.render()))
      
      if cv2.waitKey(1) & 0xFF == ord('q'):
          break
          
  cap.release()
  cv2.destroyAllWindows()
  cv2.waitKey(1)
  
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