问题描述
在编辑代码以流式传输尽可能多的视频后，我试图在此代码中启用 2 个摄像头流，而不是仅一个我想通过将捕获代码从 vs = cv2.VideoCapture(0) 更改为 vs =[ cv2.VideoCapture(0,cv2.CAP_DSHOW),cv2.VideoCapture(0,cv2.CAP_DSHOW) ] 来使用列表和循环，但会出现一些阻止添加的错误。
并且查看代码行也来自：
if args["display"] > 0:
# 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
到：
for number,(grabbed,frame) in enumerate(streams):
if args["display"] > 0:
# show the output frame
cv2.imshow(f'Cam {number}',frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
由于该编辑，我能够将流乘以 vs 列表中捕获的摄像机数量。
当我运行代码时，我可以听到计算机声音变大，并且感觉它变慢了，就像代码正在执行它的工作一样，但输出并未显示在每一帧上。我确定我必须编辑更多代码才能将更改完全应用于每个单独的帧，但我尝试的越多，我得到的错误就越多...
这是添加列表和for循环之前的程序
# USAGE
# python social_distance_detector.py --input pedestrians.mp4
# python social_distance_detector.py --input pedestrians.mp4 --output output.avi
# import the necessary packages
from TheLazyCoder import social_distancing_config as config
from TheLazyCoder.detection import detect_people
from scipy.spatial import distance as dist
import numpy as np
import argparse
import imutils
import cv2
import os
# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i","--input",type=str,default="",help="path to (optional) input video file")
ap.add_argument("-o","--output",help="path to (optional) output video file")
ap.add_argument("-d","--display",type=int,default=1,help="whether or not output frame should be displayed")
args = vars(ap.parse_args())
# load the COCO class labels our YOLO model was trained on
labelsPath = os.path.sep.join([config.MODEL_PATH,"coco.names"])
LABELS = open(labelsPath).read().strip().split("\n")
# derive the paths to the YOLO weights and model configuration
weightsPath = os.path.sep.join([config.MODEL_PATH,"yolov3.weights"])
configPath = os.path.sep.join([config.MODEL_PATH,"yolov3.cfg"])
# load our YOLO object detector trained on COCO dataset (80 classes)
print("[INFO] loading YOLO from disk...")
net = cv2.dnn.readNetFromDarknet(configPath,weightsPath)
# check if we are going to use GPU
if config.USE_GPU:
# set CUDA as the preferable backend and target
print("[INFO] setting preferable backend and target to CUDA...")
net.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA)
net.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA)
# determine only the *output* layer names that we need from YOLO
ln = net.getLayerNames()
ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()]
# initialize the video stream and pointer to output video file
print("[INFO] accessing video stream...")
vs = cv2.VideoCapture(args["input"] if args["input"] else 0)
writer = None
# loop over the frames from the video stream
while True:
# read the next frame from the file
(grabbed,frame) = vs.read()
# if the frame was not grabbed,then we have reached the end
# of the stream
if not grabbed:
break
# resize the frame and then detect people (and only people) in it
frame = imutils.resize(frame,width=700)
results = detect_people(frame,net,ln,personIdx=LABELS.index("person"))
# initialize the set of indexes that violate the minimum social
# distance
violate = set()
# ensure there are *at least* two people detections (required in
# order to compute our pairwise distance maps)
if len(results) >= 2:
# extract all centroids from the results and compute the
# Euclidean distances between all pairs of the centroids
centroids = np.array([r[2] for r in results])
D = dist.cdist(centroids,centroids,metric="euclidean")
# loop over the upper triangular of the distance matrix
for i in range(0,D.shape[0]):
for j in range(i + 1,D.shape[1]):
# check to see if the distance between any two
# centroid pairs is less than the configured number
# of pixels
if D[i,j] < config.MIN_disTANCE:
# update our violation set with the indexes of
# the centroid pairs
violate.add(i)
violate.add(j)
# loop over the results
for (i,(prob,bBox,centroid)) in enumerate(results):
# extract the bounding Box and centroid coordinates,then
# initialize the color of the annotation
(startX,startY,endX,endY) = bBox
(cX,cY) = centroid
color = (0,255,0)
# if the index pair exists within the violation set,then
# update the color
if i in violate:
color = (0,255)
# draw (1) a bounding Box around the person and (2) the
# centroid coordinates of the person,cv2.rectangle(frame,(startX,startY),(endX,endY),color,2)
cv2.circle(frame,(cX,cY),5,1)
# draw the total number of social distancing violations on the
# output frame
text = "Social distancing Violations: {}".format(len(violate))
cv2.putText(frame,text,(10,frame.shape[0] - 25),cv2.FONT_HERShey_SIMPLEX,0.85,(0,255),3)
# check to see if the output frame should be displayed to our
# screen
if args["display"] > 0:
# show the output frame
cv2.imshow("Frame",break from the loop
if key == ord("q"):
break
# if an output video file path has been supplied and the video
# writer has not been initialized,do so Now
if args["output"] != "" and writer is None:
# initialize our video writer
fourcc = cv2.VideoWriter_fourcc(*"MJPG")
writer = cv2.VideoWriter(args["output"],fourcc,25,(frame.shape[1],frame.shape[0]),True)
# if the video writer is not None,write the frame to the output
# video file
if writer is not None:
writer.write(frame)
这是之后：
# USAGE
# python social_distance_detector.py --input pedestrians.mp4
# python social_distance_detector.py --input pedestrians.mp4 --output output.avi
# import the necessary packages
from TheLazyCoder import social_distancing_config as config
from TheLazyCoder.detection import detect_people
from scipy.spatial import distance as dist
import numpy as np
import argparse
import imutils
import cv2
import os
# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i",weightsPath)
# check if we are going to use GPU
if config.USE_GPU:
# set CUDA as the preferable backend and target
print("[INFO] setting preferable backend and target to CUDA...")
net.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA)
net.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA)
# determine only the *output* layer names that we need from YOLO
ln = net.getLayerNames()
ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()]
# initialize the video stream and pointer to output video file
print("[INFO] accessing video stream...")
vs =[
cv2.VideoCapture(0,cv2.CAP_DSHOW)
]
writer = None
# loop over the frames from the video stream
while True:
# read the next frame from the file
streams=[]
for cap in vs:
grabbed,frame = cap.read()
streams.append([grabbed,frame])
# if the frame was not grabbed,then we have reached the end
# of the stream
if not grabbed:
break
# resize the frame and then detect people (and only people) in it
frame = imutils.resize(frame,3)
# check to see if the output frame should be displayed to our
# screen
for number,frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# if an output video file path has been supplied and the video
# writer has not been initialized,write the frame to the output
# video file
if writer is not None:
writer.write(frame)
我希望每个摄像头都能提供诸如 this example 之类的输出，谢谢。
解决方法
暂无找到可以解决该程序问题的有效方法，小编努力寻找整理中！
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