【发布时间】：2021-02-11 12:49:34
【问题描述】：
我正在尝试在多个视频捕获流上绘制边界框。在更改下面的代码以使其处于 for 循环中后出现此错误（首先不确定这是否有效）。
有人建议我将视频捕获的数据放入 numpy 数组而不是列表中以避免错误，但我不确定。
这是错误：
Traceback (most recent call last):
File "C:\Users\abdul\PythonProjects\Social-distance-detection-mastera\social_distance_detector.py", line 119, in <module>
cv2.rectangle(frame, (startX, startY), (endX, endY), color, 2)
TypeError: an integer is required (got type tuple)
[Finished in 5.2s with exit code 1]
[shell_cmd: python -u "C:\Users\abdul\PythonProjects\Social-distance-detection-mastera\social_distance_detector.py"]
[dir: C:\Users\abdul\PythonProjects\Social-distance-detection-mastera]
[path: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1\bin;C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1\libnvvp;C:\Program Files (x86)\Intel\Intel(R) Management Engine Components\iCLS\;C:\Program Files\Intel\Intel(R) Management Engine Components\iCLS\;C:\Program Files (x86)\Common Files\Oracle\Java\javapath;C:\ProgramData\Oracle\Java\javapath;D:\oracle\product\10.2.0\db_1\bin;C:\windows\system32;C:\windows;C:\windows\System32\Wbem;C:\windows\System32\WindowsPowerShell\v1.0\;C:\WINDOWS\system32;C:\WINDOWS;C:\WINDOWS\System32\Wbem;C:\WINDOWS\System32\WindowsPowerShell\v1.0\;C:\WINDOWS\System32\OpenSSH\;C:\Program Files (x86)\NVIDIA Corporation\PhysX\Common;C:\Program Files (x86)\Microsoft SQL Server\100\Tools\Binn\;C:\Program Files\Microsoft SQL Server\100\Tools\Binn\;C:\Program Files\Microsoft SQL Server\100\DTS\Binn\;C:\Program Files (x86)\Intel\Intel(R) Management Engine Components\DAL;C:\Program Files\Intel\Intel(R) Management Engine Components\DAL;C:\Program Files (x86)\Intel\Intel(R) Management Engine Components\IPT;C:\Program Files\Intel\Intel(R) Management Engine Components\IPT;C:\WINDOWS\system32;C:\WINDOWS;C:\WINDOWS\System32\Wbem;C:\WINDOWS\System32\WindowsPowerShell\v1.0\;C:\WINDOWS\System32\OpenSSH\;C:\Program Files\dotnet\;C:\Program Files\Microsoft SQL Server\130\Tools\Binn\;C:\Program Files (x86)\Brackets\command;C:\Program Files\Intel\WiFi\bin\;C:\Program Files\Common Files\Intel\WirelessCommon\;D:\xampp\php;C:\ProgramData\ComposerSetup\bin;d:\Program Files\Git\cmd;d:\Program Files\Git\mingw64\bin;d:\Program Files\Git\usr\bin;C:\Program Files\NVIDIA Corporation\Nsight Compute 2019.1\;C:\Program Files\NVIDIA Corporation\NVIDIA NvDLISR;d:\Users\abdul\Anaconda3;d:\Users\abdul\Anaconda3\Library\mingw-w64\bin;d:\Users\abdul\Anaconda3\Library\usr\bin;d:\Users\abdul\Anaconda3\Library\bin;d:\Users\abdul\Anaconda3\Scripts;C:\Users\abdul\AppData\Local\Microsoft\WindowsApps;C:\Users\abdul\AppData\Roaming\Composer\vendor\bin;C:\Users\abdul\AppData\Local\GitHubDesktop\bin;%DASHLANE_DLL_DIR%;C:\Users\abdul\AppData\Local\Microsoft\WindowsApps;]
发生在这个区块中：
for frame) in streams:
cv2.rectangle(frame, (startX, startY), (endX, endY), color, 2)
cv2.circle(frame, (cX, cY), 5, color, 1)
我在互联网上找到了一个建议，可以像这样对 args 进行 prase：
for frame in streams:
cv2.rectangle(frame, int(startX, startY), int(endX, endY), color, 2)
cv2.circle(frame, (cX, cY), 5, color, 1)
但我得到了这个错误：
Traceback (most recent call last):
File "C:\Users\abdul\PythonProjects\Social-distance-detection-mastera\social_distance_detector.py", line 119, in <module>
cv2.rectangle(frame, int(startX, startY), int(endX, endY), color, 2)
ValueError: int() base must be >= 2 and <= 36, or 0
[Finished in 2.8s with exit code 1]
[shell_cmd: python -u "C:\Users\abdul\PythonProjects\Social-distance-detection-mastera\social_distance_detector.py"]
[dir: C:\Users\abdul\PythonProjects\Social-distance-detection-mastera]
[path: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1\bin;C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1\libnvvp;C:\Program Files (x86)\Intel\Intel(R) Management Engine Components\iCLS\;C:\Program Files\Intel\Intel(R) Management Engine Components\iCLS\;C:\Program Files (x86)\Common Files\Oracle\Java\javapath;C:\ProgramData\Oracle\Java\javapath;D:\oracle\product\10.2.0\db_1\bin;C:\windows\system32;C:\windows;C:\windows\System32\Wbem;C:\windows\System32\WindowsPowerShell\v1.0\;C:\WINDOWS\system32;C:\WINDOWS;C:\WINDOWS\System32\Wbem;C:\WINDOWS\System32\WindowsPowerShell\v1.0\;C:\WINDOWS\System32\OpenSSH\;C:\Program Files (x86)\NVIDIA Corporation\PhysX\Common;C:\Program Files (x86)\Microsoft SQL Server\100\Tools\Binn\;C:\Program Files\Microsoft SQL Server\100\Tools\Binn\;C:\Program Files\Microsoft SQL Server\100\DTS\Binn\;C:\Program Files (x86)\Intel\Intel(R) Management Engine Components\DAL;C:\Program Files\Intel\Intel(R) Management Engine Components\DAL;C:\Program Files (x86)\Intel\Intel(R) Management Engine Components\IPT;C:\Program Files\Intel\Intel(R) Management Engine Components\IPT;C:\WINDOWS\system32;C:\WINDOWS;C:\WINDOWS\System32\Wbem;C:\WINDOWS\System32\WindowsPowerShell\v1.0\;C:\WINDOWS\System32\OpenSSH\;C:\Program Files\dotnet\;C:\Program Files\Microsoft SQL Server\130\Tools\Binn\;C:\Program Files (x86)\Brackets\command;C:\Program Files\Intel\WiFi\bin\;C:\Program Files\Common Files\Intel\WirelessCommon\;D:\xampp\php;C:\ProgramData\ComposerSetup\bin;d:\Program Files\Git\cmd;d:\Program Files\Git\mingw64\bin;d:\Program Files\Git\usr\bin;C:\Program Files\NVIDIA Corporation\Nsight Compute 2019.1\;C:\Program Files\NVIDIA Corporation\NVIDIA NvDLISR;d:\Users\abdul\Anaconda3;d:\Users\abdul\Anaconda3\Library\mingw-w64\bin;d:\Users\abdul\Anaconda3\Library\usr\bin;d:\Users\abdul\Anaconda3\Library\bin;d:\Users\abdul\Anaconda3\Scripts;C:\Users\abdul\AppData\Local\Microsoft\WindowsApps;C:\Users\abdul\AppData\Roaming\Composer\vendor\bin;C:\Users\abdul\AppData\Local\GitHubDesktop\bin;%DASHLANE_DLL_DIR%;C:\Users\abdul\AppData\Local\Microsoft\WindowsApps;]
我是个菜鸟，所以我把完整的代码放在这行后面，以防万一
# 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", type=str, default="",
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(0,cv2.CAP_DSHOW),
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, 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, 0, 255)
# draw (1) a bounding box around the person and (2) the
# centroid coordinates of the person,
for frame in streams:
cv2.rectangle(frame, int(startX, startY), int(endX, endY), color, 2)
cv2.circle(frame, (cX, cY), 5, color, 1)
# draw the total number of social distancing violations on the
# output frame
for frame in streams:
text = "Social Distancing Violations: {}".format(len(violate))
cv2.putText(frame, text, (10, frame.shape[0] - 25),
cv2.FONT_HERSHEY_SIMPLEX, 0.85, (0, 0, 255), 3)
# check to see if the output frame should be displayed to our
# screen
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
# 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)
【问题讨论】：
标签： python numpy opencv video-streaming object-detection