我正在Ubuntu 18.04中使用OpenVINO设置一个PPE检测模块。虽然视频输入与我的网络摄像头dev/video/0配合得很好，但我希望它可以更改为RTSP输入。每当我将RTSP Url放入config.json时，它都不起作用，并显示Either wrong input path or empty line is found. Please check the conf.json file。
这是主要的.py
#!/usr/bin/env python3
from __future__ import print_function
import sys
import os
import cv2
import numpy as np
from argparse import ArgumentParser
import datetime
import json
from inference import Network
# Global vars
cpu_extension = ''
conf_modelLayers = ''
conf_modelWeights = ''
conf_safety_modelLayers = ''
conf_safety_modelWeights = ''
targetDevice = "CPU"
conf_batchSize = 1
conf_modelPersonLabel = 1
conf_inferConfidenceThreshold = 0.7
conf_inFrameViolationsThreshold = 19
conf_inFramePeopleThreshold = 5
use_safety_model = False
padding = 30
viol_wk = 0
acceptedDevices = ['CPU', 'GPU', 'MYRIAD', 'HETERO:FPGA,CPU', 'HDDL']
videos = []
name_of_videos = []
CONFIG_FILE = '../resources/config.json'
is_async_mode = True
class Video:
def __init__(self, idx, path):
if path.isnumeric():
self.video = cv2.VideoCapture(int(path))
self.name = "Cam " + str(idx)
else:
if os.path.exists(path):
self.video = cv2.VideoCapture("rtsp://edwin:Passw0rd@192.168.0.144:554/cam/realmonitor?channel=1&subtype=1")
self.name = "Video " + str(idx)
else:
print("Either wrong input path or empty line is found. Please check the conf.json file")
exit(21)
if not self.video.isOpened():
print("Couldn't open video: " + path)
sys.exit(20)
self.height = int(self.video.get(cv2.CAP_PROP_FRAME_HEIGHT))
self.width = int(self.video.get(cv2.CAP_PROP_FRAME_WIDTH))
self.currentViolationCount = 0
self.currentViolationCountConfidence = 0
self.prevViolationCount = 0
self.totalViolations = 0
self.totalPeopleCount = 0
self.currentPeopleCount = 0
self.currentPeopleCountConfidence = 0
self.prevPeopleCount = 0
self.currentTotalPeopleCount = 0
cv2.namedWindow(self.name, cv2.WINDOW_NORMAL)
self.frame_start_time = datetime.datetime.now()
def get_args():
"""
Parses the argument.
:return: None
"""
global is_async_mode
parser = ArgumentParser()
parser.add_argument("-d", "--device",
help="Specify the target device to infer on; CPU, GPU,"
"FPGA, MYRIAD or HDDL is acceptable. Application will"
"look for a suitable plugin for device specified"
" (CPU by default)",
type=str, required=False)
parser.add_argument("-m", "--model",
help="Path to an .xml file with a trained model's"
" weights.",
required=True, type=str)
parser.add_argument("-sm", "--safety_model",
help="Path to an .xml file with a trained model's"
" weights.",
required=False, type=str, default=None)
parser.add_argument("-e", "--cpu_extension",
help="MKLDNN (CPU)-targeted custom layers. Absolute "
"path to a shared library with the kernels impl",
type=str, default=None)
parser.add_argument("-f", "--flag", help="sync or async", default="async", type=str)
args = parser.parse_args()
global conf_modelLayers, conf_modelWeights, conf_safety_modelLayers, conf_safety_modelWeights,
targetDevice, cpu_extension, videos, use_safety_model
if args.model:
conf_modelLayers = args.model
conf_modelWeights = os.path.splitext(conf_modelLayers)[0] + ".bin"
if args.safety_model:
conf_safety_modelLayers = args.safety_model
conf_safety_modelWeights = os.path.splitext(conf_safety_modelLayers)[0] + ".bin"
use_safety_model = True
if args.device:
targetDevice = args.device
if "MULTI:" not in targetDevice:
if targetDevice not in acceptedDevices:
print("Selected device, %s not supported." % (targetDevice))
sys.exit(12)
if args.cpu_extension:
cpu_extension = args.cpu_extension
if args.flag == "async":
is_async_mode = True
print('Application running in Async mode')
else:
is_async_mode = False
print('Application running in Sync mode')
assert os.path.isfile(CONFIG_FILE), "{} file doesn't exist".format(CONFIG_FILE)
config = json.loads(open(CONFIG_FILE).read())
for idx, item in enumerate(config['inputs']):
vid = Video(idx, item['video'])
name_of_videos.append([idx, item['video']])
videos.append([idx, vid])
def detect_safety_hat(img):
"""
Detection of the hat of the person.
:param img: Current frame
:return: Boolean value of the detected hat
"""
lowH = 15
lowS = 65
lowV = 75
highH = 30
highS = 255
highV = 255
crop = 0
height = 15
perc = 8
hsv = np.zeros(1)
try:
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
except cv2.error as e:
print("%d %d %d" % (img.shape))
print("%d %d %d" % (img.shape))
print(e)
threshold_img = cv2.inRange(hsv, (lowH, lowS, lowV), (highH, highS, highV))
x = 0
y = int(threshold_img.shape[0] * crop / 100)
w = int(threshold_img.shape[1])
h = int(threshold_img.shape[0] * height / 100)
img_cropped = threshold_img[y: y + h, x: x + w]
if cv2.countNonZero(threshold_img) < img_cropped.size * perc / 100:
return False
return True
def detect_safety_jacket(img):
"""
Detection of the safety jacket of the person.
:param img: Current frame
:return: Boolean value of the detected jacket
"""
lowH = 0
lowS = 150
lowV = 42
highH = 11
highS = 255
highV = 255
crop = 15
height = 40
perc = 23
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
threshold_img = cv2.inRange(hsv, (lowH, lowS, lowV), (highH, highS, highV))
x = 0
y = int(threshold_img.shape[0] * crop / 100)
w = int(threshold_img.shape[1])
h = int(threshold_img.shape[0] * height / 100)
img_cropped = threshold_img[y: y + h, x: x + w]
if cv2.countNonZero(threshold_img) < img_cropped.size * perc / 100:
return False
return True
def detect_workers(workers, frame):
"""
Detection of the person with the safety guards.
:param workers: Total number of the person in the current frame
:param frame: Current frame
:return: Total violation count of the person
"""
violations = 0
global viol_wk
for worker in workers:
xmin, ymin, xmax, ymax = worker
crop = frame[ymin:ymax, xmin:xmax]
if 0 not in crop.shape:
if detect_safety_hat(crop):
if detect_safety_jacket(crop):
cv2.rectangle(frame, (xmin, ymin), (xmax, ymax),
(0, 255, 0), 2)
else:
cv2.rectangle(frame, (xmin, ymin), (xmax, ymax),
(0, 0, 255), 2)
violations += 1
viol_wk += 1
else:
cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), (0, 0, 255), 2)
violations += 1
viol_wk += 1
return violations
def main():
"""
Load the network and parse the output.
:return: None
"""
get_args()
global is_async_mode
nextReq = 1
currReq = 0
nextReq_s = 1
currReq_s = 0
prevVideo = None
vid_finished = [False] * len(videos)
min_FPS = min([videos[i][1].video.get(cv2.CAP_PROP_FPS) for i in range(len(videos))])
# Initialise the class
infer_network = Network()
infer_network_safety = Network()
# Load the network to IE plugin to get shape of input layer
plugin, (batch_size, channels, model_height, model_width) =
infer_network.load_model(conf_modelLayers, targetDevice, 1, 1, 2, cpu_extension)
if use_safety_model:
batch_size_sm, channels_sm, model_height_sm, model_width_sm =
infer_network_safety.load_model(conf_safety_modelLayers, targetDevice, 1, 1, 2, cpu_extension, plugin)[1]
while True:
for index, currVideo in videos:
# Read image from video/cam
vfps = int(round(currVideo.video.get(cv2.CAP_PROP_FPS)))
for i in range(0, int(round(vfps / min_FPS))):
ret, current_img = currVideo.video.read()
if not ret:
vid_finished[index] = True
break
if vid_finished[index]:
stream_end_frame = np.zeros((int(currVideo.height), int(currVideo.width), 1),
dtype='uint8')
cv2.putText(stream_end_frame, "Input file {} has ended".format
(name_of_videos[index][1].split('/')[-1]),
(10, int(currVideo.height / 2)),
cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2)
cv2.imshow(currVideo.name, stream_end_frame)
continue
# Transform image to person detection model input
rsImg = cv2.resize(current_img, (model_width, model_height))
rsImg = rsImg.transpose((2, 0, 1))
rsImg = rsImg.reshape((batch_size, channels, model_height, model_width))
infer_start_time = datetime.datetime.now()
# Infer current image
if is_async_mode:
infer_network.exec_net(nextReq, rsImg)
else:
infer_network.exec_net(currReq, rsImg)
prevVideo = currVideo
previous_img = current_img
# Wait for previous request to end
if infer_network.wait(currReq) == 0:
infer_end_time = (datetime.datetime.now() - infer_start_time) * 1000
in_frame_workers = []
people = 0
violations = 0
hard_hat_detection = False
vest_detection = False
result = infer_network.get_output(currReq)
# Filter output
for obj in result[0][0]:
if obj[2] > conf_inferConfidenceThreshold:
xmin = int(obj[3] * prevVideo.width)
ymin = int(obj[4] * prevVideo.height)
xmax = int(obj[5] * prevVideo.width)
ymax = int(obj[6] * prevVideo.height)
xmin = int(xmin - padding) if (xmin - padding) > 0 else 0
ymin = int(ymin - padding) if (ymin - padding) > 0 else 0
xmax = int(xmax + padding) if (xmax + padding) < prevVideo.width else prevVideo.width
ymax = int(ymax + padding) if (ymax + padding) < prevVideo.height else prevVideo.height
cv2.rectangle(previous_img, (xmin, ymin), (xmax, ymax), (0, 255, 0), 2)
people += 1
in_frame_workers.append((xmin, ymin, xmax, ymax))
new_frame = previous_img[ymin:ymax, xmin:xmax]
if use_safety_model:
# Transform image to safety model input
in_frame_sm = cv2.resize(new_frame, (model_width_sm, model_height_sm))
in_frame_sm = in_frame_sm.transpose((2, 0, 1))
in_frame_sm = in_frame_sm.reshape(
(batch_size_sm, channels_sm, model_height_sm, model_width_sm))
infer_start_time_sm = datetime.datetime.now()
if is_async_mode:
infer_network_safety.exec_net(nextReq_s, in_frame_sm)
else:
infer_network_safety.exec_net(currReq_s, in_frame_sm)
# Wait for the result
infer_network_safety.wait(currReq_s)
infer_end_time_sm = (datetime.datetime.now() - infer_start_time_sm) * 1000
result_sm = infer_network_safety.get_output(currReq_s)
# Filter output
hard_hat_detection = False
vest_detection = False
detection_list = []
for obj_sm in result_sm[0][0]:
if (obj_sm[2] > 0.4):
# Detect safety vest
if (int(obj_sm[1])) == 2:
xmin_sm = int(obj_sm[3] * (xmax - xmin))
ymin_sm = int(obj_sm[4] * (ymax - ymin))
xmax_sm = int(obj_sm[5] * (xmax - xmin))
ymax_sm = int(obj_sm[6] * (ymax - ymin))
if vest_detection == False:
detection_list.append(
[xmin_sm + xmin, ymin_sm + ymin, xmax_sm + xmin, ymax_sm + ymin])
vest_detection = True
# Detect hard-hat
if int(obj_sm[1]) == 4:
xmin_sm_v = int(obj_sm[3] * (xmax - xmin))
ymin_sm_v = int(obj_sm[4] * (ymax - ymin))
xmax_sm_v = int(obj_sm[5] * (xmax - xmin))
ymax_sm_v = int(obj_sm[6] * (ymax - ymin))
if hard_hat_detection == False:
detection_list.append([xmin_sm_v + xmin, ymin_sm_v + ymin, xmax_sm_v + xmin,
ymax_sm_v + ymin])
hard_hat_detection = True
if hard_hat_detection is False or vest_detection is False:
violations += 1
for _rect in detection_list:
cv2.rectangle(current_img, (_rect[0], _rect[1]), (_rect[2], _rect[3]), (0, 255, 0), 2)
if is_async_mode:
currReq_s, nextReq_s = nextReq_s, currReq_s
# Use OpenCV if worker-safety-model is not provided
else:
violations = detect_workers(in_frame_workers, previous_img)
# Check if detected violations equals previous frames
if violations == prevVideo.currentViolationCount:
prevVideo.currentViolationCountConfidence += 1
# If frame threshold is reached, change validated count
if prevVideo.currentViolationCountConfidence == conf_inFrameViolationsThreshold:
# If another violation occurred, save image
if prevVideo.currentViolationCount > prevVideo.prevViolationCount:
prevVideo.totalViolations += (
prevVideo.currentViolationCount - prevVideo.prevViolationCount)
prevVideo.prevViolationCount = prevVideo.currentViolationCount
else:
prevVideo.currentViolationCountConfidence = 0
prevVideo.currentViolationCount = violations
# Check if detected people count equals previous frames
if people == prevVideo.currentPeopleCount:
prevVideo.currentPeopleCountConfidence += 1
# If frame threshold is reached, change validated count
if prevVideo.currentPeopleCountConfidence == conf_inFrameViolationsThreshold:
prevVideo.currentTotalPeopleCount += (
prevVideo.currentPeopleCount - prevVideo.prevPeopleCount)
if prevVideo.currentTotalPeopleCount > prevVideo.prevPeopleCount:
prevVideo.totalPeopleCount += prevVideo.currentTotalPeopleCount - prevVideo.prevPeopleCount
prevVideo.prevPeopleCount = prevVideo.currentPeopleCount
else:
prevVideo.currentPeopleCountConfidence = 0
prevVideo.currentPeopleCount = people
frame_end_time = datetime.datetime.now()
cv2.putText(previous_img, 'Total people count: ' + str(
prevVideo.totalPeopleCount), (10, prevVideo.height - 10),
cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)
cv2.putText(previous_img, 'Current people count: ' + str(
prevVideo.currentTotalPeopleCount),
(10, prevVideo.height - 40),
cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)
cv2.putText(previous_img, 'Total violation count: ' + str(
prevVideo.totalViolations), (10, prevVideo.height - 70),
cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)
cv2.putText(previous_img, 'FPS: %0.2fs' % (1 / (
frame_end_time - prevVideo.frame_start_time).total_seconds()),
(10, prevVideo.height - 100),
cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)
cv2.putText(previous_img, "Inference time: NA for async mode" if is_async_mode else
"Inference time: {:.3f} ms".format((infer_end_time).total_seconds()),
(10, prevVideo.height - 130),
cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)
cv2.imshow(prevVideo.name, previous_img)
prevVideo.frame_start_time = datetime.datetime.now()
# Swap
if is_async_mode:
currReq, nextReq = nextReq, currReq
previous_img = current_img
prevVideo = currVideo
if cv2.waitKey(1) == 27:
print("Attempting to stop input files")
infer_network.clean()
infer_network_safety.clean()
cv2.destroyAllWindows()
return
if False not in vid_finished:
infer_network.clean()
infer_network_safety.clean()
cv2.destroyAllWindows()
break
if __name__ == '__main__':
main()
这是配置文件
{
"inputs": [
{
"video": "rtsp://xxx:xxx@192.168.0.144:554/cam/realmonitor?channel=1&subtype=1"
}
]
}
这是因为行if os.path.exists(path):。此if条件检查path是否指向现有文件。您的RTSP流不是文件，这会导致您的错误。
例如，您可以将此条件修改为：
if os.path.exists(path) or path.startswith("rtsp"):
顺便说一句，你在代码中硬编码了rtsp流地址，所以它不会使用你配置的路径。您可能需要将硬编码路径替换为path。