@Diyar

Почему выводит ошибку при приближении к камере?

import the necessary packages
from tensorflow.keras.applications.mobilenet_v2 import preprocess_input
from os.path import dirname, join
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.models import load_model
from imutils.video import VideoStream
import numpy as np
import imutils
import time
import cv2
import os

def detect_and_predict_mask(frame, faceNet, maskNet):
  # grab the dimensions of the frame and then construct a blob
  # from it
  (h, w) = frame.shape[:2]
  blob = cv2.dnn.blobFromImage(frame, 1.0, (224, 224),
    (104.0, 177.0, 123.0))

  # pass the blob through the network and obtain the face detections
  faceNet.setInput(blob)
  detections = faceNet.forward()
  print(detections.shape)

  # initialize our list of faces, their corresponding locations,
  # and the list of predictions from our face mask network
  faces = []
  locs = []
  preds = []

  # loop over the detections
  for i in range(0, detections.shape[2]):
    # extract the confidence (i.e., probability) associated with
    # the detection
    confidence = detections[0, 0, i, 2]

    # filter out weak detections by ensuring the confidence is
    # greater than the minimum confidence
    if confidence > 0.5:
      # compute the (x, y)-coordinates of the bounding box for
      # the object
      box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
      (startX, startY, endX, endY) = box.astype("int")

      # ensure the bounding boxes fall within the dimensions of
      # the frame
      (startX, startY) = (max(0, startX), max(0, startY))
      (endX, endY) = (min(w - 1, endX), min(h - 1, endY))

      # extract the face ROI, convert it from BGR to RGB channel
      # ordering, resize it to 224x224, and preprocess it
      face = frame[startY:endY, startX:endX]
      face = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)
      face = cv2.resize(face, (224, 224))
      face = img_to_array(face)
      face = preprocess_input(face)

      # add the face and bounding boxes to their respective
      # lists
      faces.append(face)
      locs.append((startX, startY, endX, endY))

  # only make a predictions if at least one face was detected
  if len(faces) > 0:
    # for faster inference we'll make batch predictions on *all*
    # faces at the same time rather than one-by-one predictions
    # in the above `for` loop
    faces = np.array(faces, dtype="float32")
    preds = maskNet.predict(faces, batch_size=32)

  # return a 2-tuple of the face locations and their corresponding
  # locations
  return (locs, preds)

# load our serialized face detector model from disk
prototxtPath = r"deploy.protext"
weightsPath = r"res10_300x300_ssd_iter_140000.caffemodel"
faceNet = cv2.dnn.readNet(prototxtPath, weightsPath)

# load the face mask detector model from disk
maskNet = load_model("mask_detector.model")

# initialize the video stream
print("Starting the CAMERA...")
vs = VideoStream(src=0).start()

# loop over the frames from the video stream
while True:
  # grab the frame from the threaded video stream and resize it
  # to have a maximum width of 400 pixels
  frame = vs.read()
  frame = imutils.resize(frame, width=900, height = 600)

  # detect faces in the frame and determine if they are wearing a
  # face mask or not
  (locs, preds) = detect_and_predict_mask(frame, faceNet, maskNet)

  # loop over the detected face locations and their corresponding
  # locations
  for (box, pred) in zip(locs, preds):
    # unpack the bounding box and predictions
    (startX, startY, endX, endY) = box
    (mask, withoutMask) = pred

    # determine the class label and color we'll use to draw
    # the bounding box and text
    label = "Mask" if mask > withoutMask else "No Mask"
    color = (0, 255, 0) if label == "Mask" else (0, 0, 255)

    # include the probability in the label
    label = "{}: {:.2f}%".format(label, max(mask, withoutMask) * 100)

    # display the label and bounding box rectangle on the output
    # frame
    cv2.putText(frame, label, (startX, startY - 10),
      cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 2)
    cv2.rectangle(frame, (startX, startY), (endX, endY), color, 2)

  # show the output frame
  cv2.imshow("Frame", frame)
  key = cv2.waitKey(1) & 0xFF

  # if the `q` key was pressed, break from the loop
  if key == ord("q"):
    break

# do a bit of cleanup
cv2.destroyAllWindows()
vs.stop()<code/>
Этот код работает корректно когда стои на одном месте, но если я камеру чуть дерну она в какой то момент вылает, но когда медленно приближаюсь к камере как я понял она не может нарисовать квадрат что то вроде того, прошу помогиие решить проблему.
Traceback (most recent call last):
File "detect_mask.py", line 96, in
(locs, preds) = detect_and_predict_mask(frame, faceNet, maskNet)
File "detect_mask.py", line 53, in detect_and_predict_mask
face = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)
cv2.error: OpenCV(4.5.1) /tmp/pip-req-build-ms668fyv/opencv/modules/imgproc/src/color.cpp:182: error: (-215:Assertion failed) !_src.empty() in function 'cvtColor'
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phaggi
@phaggi Куратор тега Python
лужу, паяю, ЭВМы починяю
Да, выводит ошибку при приближении к камере.
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