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Я транслирую камеру lepton FLIR на jetson nano с помощью python3 и OpenCV, и у меня проблемы с тем, что я не могу изменить размер видео в реальном времени. Исходное разрешение лептона составляет (160 x 120), и я хотел бы изменить его размер на (640 x 480). Я пытался использовать разные команды OpenCV, но это не работает

нужна помощь

В настоящем документе кодекс

 # imports import cv2 import numpy as np import time import argparse  # own modules import utills, plot  confid = 0.5 thresh = 0.5 mouse_pts = []   # Function to get points for Region of Interest(ROI) and distance scale. It will take 8 points on first frame using mouse click  # event.First four points will define ROI where we want to moniter social distancing. Also these points should form parallel  # lines in real world if seen from above(birds eye view). Next 3 points will define 6 feet(unit length) distance in  # horizontal and vertical direction and those should form parallel lines with ROI. Unit length we can take based on choice. # Points should pe in pre-defined order - bottom-left, bottom-right, top-right, top-left, point 5 and 6 should form  # horizontal line and point 5 and 7 should form verticle line. Horizontal and vertical scale will be different.   # Function will be called on mouse events   def get_mouse_points(event, x, y, flags, param):   global mouse_pts  if event == cv2.EVENT_LBUTTONDOWN:  if len(mouse_pts) lt; 4:  cv2.circle(image, (x, y), 5, (0, 0, 255), 10)  else:  cv2.circle(image, (x, y), 5, (255, 0, 0), 10)    if len(mouse_pts) gt;= 1 and len(mouse_pts) lt;= 3:  cv2.line(image, (x, y), (mouse_pts[len(mouse_pts)-1][0], mouse_pts[len(mouse_pts)-1][1]), (70, 70, 70), 2)  if len(mouse_pts) == 3:  cv2.line(image, (x, y), (mouse_pts[0][0], mouse_pts[0][1]), (70, 70, 70), 2)    if "mouse_pts" not in globals():  mouse_pts = []  mouse_pts.append((x, y))  #print("Point detected")  #print(mouse_pts)     def calculate_social_distancing(vid_path, net, output_dir, output_vid, ln1):    count = 0  vs = cv2.VideoCapture(0)   # Get video height, width and fps  height = int(vs.get(cv2.CAP_PROP_FRAME_HEIGHT))  width = int(vs.get(cv2.CAP_PROP_FRAME_WIDTH))  fps = int(vs.get(cv2.CAP_PROP_FPS))    # Set scale for birds eye view  # Bird's eye view will only show ROI  scale_w, scale_h = utills.get_scale(width, height)   fourcc = cv2.VideoWriter_fourcc(*"XVID")  output_movie = cv2.VideoWriter("./output_vid/distancing.avi", fourcc, fps, (width, height))  bird_movie = cv2.VideoWriter("./output_vid/bird_eye_view.avi", fourcc, fps, (int(width * scale_w), int(height * scale_h)))    points = []  global image    while True:   (grabbed, frame) = vs.read()   if not grabbed:  print('here')  break    (H, W) = frame.shape[:2]    # first frame will be used to draw ROI and horizontal and vertical 180 cm distance(unit length in both directions)  if count == 0:  while True:  image = frame  cv2.imshow("image", image)  cv2.waitKey(1)  if len(mouse_pts) == 8:  cv2.destroyWindow("image")  break    points = mouse_pts     # Using first 4 points or coordinates for perspective transformation. The region marked by these 4 points are   # considered ROI. This polygon shaped ROI is then warped into a rectangle which becomes the bird eye view.   # This bird eye view then has the property property that points are distributed uniformally horizontally and   # vertically(scale for horizontal and vertical direction will be different). So for bird eye view points are   # equally distributed, which was not case for normal view.  src = np.float32(np.array(points[:4]))  dst = np.float32([[0, H], [W, H], [W, 0], [0, 0]])  prespective_transform = cv2.getPerspectiveTransform(src, dst)   # using next 3 points for horizontal and vertical unit length(in this case 180 cm)  pts = np.float32(np.array([points[4:7]]))  warped_pt = cv2.perspectiveTransform(pts, prespective_transform)[0]    # since bird eye view has property that all points are equidistant in horizontal and vertical direction.  # distance_w and distance_h will give us 180 cm distance in both horizontal and vertical directions  # (how many pixels will be there in 180cm length in horizontal and vertical direction of birds eye view),  # which we can use to calculate distance between two humans in transformed view or bird eye view  distance_w = np.sqrt((warped_pt[0][0] - warped_pt[1][0]) ** 2   (warped_pt[0][1] - warped_pt[1][1]) ** 2)  distance_h = np.sqrt((warped_pt[0][0] - warped_pt[2][0]) ** 2   (warped_pt[0][1] - warped_pt[2][1]) ** 2)  pnts = np.array(points[:4], np.int32)  cv2.polylines(frame, [pnts], True, (70, 70, 70), thickness=2)    ####################################################################################    # YOLO v4  blob = cv2.dnn.blobFromImage(frame, 1 / 255.0, (128, 128), swapRB=True, crop=False)  net.setInput(blob)  start = time.time()  layerOutputs = net.forward(ln1)  end = time.time()  boxes = []  confidences = []  classIDs = []     for output in layerOutputs:  for detection in output:  scores = detection[5:]  classID = np.argmax(scores)  confidence = scores[classID]  # detecting humans in frame  if classID == 1:   if confidence gt; confid:   box = detection[0:4] * np.array([W, H, W, H])  (centerX, centerY, width, height) = box.astype("int")   x = int(centerX - (width / 2))  y = int(centerY - (height / 2))   boxes.append([x, y, int(width), int(height)])  confidences.append(float(confidence))  classIDs.append(classID)    idxs = cv2.dnn.NMSBoxes(boxes, confidences, confid, thresh)  font = cv2.FONT_HERSHEY_PLAIN  boxes1 = []  for i in range(len(boxes)):  if i in idxs:  boxes1.append(boxes[i])  x,y,w,h = boxes[i]    if len(boxes1) == 0:  count = count   1  continue    # Here we will be using bottom center point of bounding box for all boxes and will transform all those  # bottom center points to bird eye view  person_points = utills.get_transformed_points(boxes1, prespective_transform)    # Here we will calculate distance between transformed points(humans)  distances_mat, bxs_mat = utills.get_distances(boxes1, person_points, distance_w, distance_h)  risk_count = utills.get_count(distances_mat)    frame1 = np.copy(frame)    # Draw bird eye view and frame with bouding boxes around humans according to risk factor   bird_image = plot.bird_eye_view(frame, distances_mat, person_points, scale_w, scale_h, risk_count)  img = plot.social_distancing_view(frame1, bxs_mat, boxes1, risk_count)    # Show/write image and videos  if count != 0:  output_movie.write(img)  bird_movie.write(bird_image)    cv2.imshow('Bird Eye View', bird_image)  cv2.imshow('Social Distancing', img)  cv2.imwrite(output_dir "frame%d.jpg" % count, img)  cv2.imwrite(output_dir "bird_eye_view/frame%d.jpg" % count, bird_image)    count = count   1  if cv2.waitKey(1) amp; 0xFF == ord('q'):  break    vs.release()  cv2.destroyAllWindows()     if __name__== "__main__":   # Receives arguments specified by user  parser = argparse.ArgumentParser()    parser.add_argument('-v', '--video_path', action='store', dest='video_path', default='./data/example.mp4' ,  help='Path for input video')    parser.add_argument('-o', '--output_dir', action='store', dest='output_dir', default='./output/' ,  help='Path for Output images')    parser.add_argument('-O', '--output_vid', action='store', dest='output_vid', default='./output_vid/' ,  help='Path for Output videos')   parser.add_argument('-m', '--model', action='store', dest='model', default='./models/',  help='Path for models directory')    parser.add_argument('-u', '--uop', action='store', dest='uop', default='NO',  help='Use open pose or not (YES/NO)')    values = parser.parse_args()    model_path = values.model  if model_path[len(model_path) - 1] != '/':  model_path = model_path   '/'    output_dir = values.output_dir  if output_dir[len(output_dir) - 1] != '/':  output_dir = output_dir   '/'    output_vid = values.output_vid  if output_vid[len(output_vid) - 1] != '/':  output_vid = output_vid   '/'    # load Yolov4 weights    weightsPath = model_path   "yolov4-tiny-custom_best.weights"  configPath = model_path   "yolov4-tiny-custom.cfg"   net_yl = cv2.dnn.readNetFromDarknet(configPath, weightsPath)  ln = net_yl.getLayerNames()  ln1 = [ln[i[0] - 1] for i in net_yl.getUnconnectedOutLayers()]   # set mouse callback    cv2.namedWindow("image")  cv2.setMouseCallback("image", get_mouse_points)  np.random.seed(42)    calculate_social_distancing(values.video_path, net_yl, output_dir, output_vid, ln1)