教你用YOLOv5实现多路摄像头实时目标检测功能
目录
- 前言
- 一、YOLOV5的强大之处
- 二、YOLOV5部署多路摄像头的web应用
- 1.多路摄像头读取
- 2.模型封装
- 3.Flask后端处理
- 4.前端展示
- 总结
前言
YOLOV5模型从发布到现在都是炙手可热的目标检测模型，被广泛运用于各大场景之中。因此，我们不光要知道如何进行yolov5模型的训练，而且还要知道怎么进行部署应用。在本篇博客中，我将利用yolov5模型简单的实现从摄像头端到web端的部署应用demo，为读者提供一些部署思路。
一、YOLOV5的强大之处
你与目标检测高手之差一个YOLOV5模型。YOLOV5可以说是现目前几乎将所有目标检测tricks运用于一身的模型了。在它身上能找到很多目前主流的数据增强、模型训练、模型后处理的方法，下面我们就简单总结一下yolov5所使用到的方法：
yolov5增加的功能：
yolov5训练和预测的tricks：
二、YOLOV5部署多路摄像头的web应用
1.多路摄像头读取
在此篇博客中，采用了yolov5源码的datasets.py代码中的LoadStreams类进行多路摄像头视频流的读取。因为，我们只会用到datasets.py中视频流读取的部分代码，所以，将其提取出来，新建一个camera.py文件，下面则是camera.py文件的代码部分：
# coding:utf-8
import os
import cv2
import glob
import time
import numpy as np
from pathlib import Path
from utils.datasets import letterbox
from threading import Thread
from utils.general import clean_str
img_formats = ['bmp', 'jpg', 'jpeg', 'png', 'tif', 'tiff', 'dng', 'webp'] # acceptable image suffixes
vid_formats = ['mov', 'avi', 'mp4', 'mpg', 'mpeg', 'm4v', 'wmv', 'mkv'] # acceptable video suffixes
class LoadImages: # for inference
def __init__(self, path, img_size=640, stride=32):
p = str(Path(path).absolute()) # os-agnostic absolute path
if '*' in p:
files = sorted(glob.glob(p, recursive=True)) # glob
elif os.path.isdir(p):
files = sorted(glob.glob(os.path.join(p, '*.*'))) # dir
elif os.path.isfile(p):
files = [p] # files
else:
raise Exception(f'ERROR: {p} does not exist')
images = [x for x in files if x.split('.')[-1].lower() in img_formats]
videos = [x for x in files if x.split('.')[-1].lower() in vid_formats]
ni, nv = len(images), len(videos)
self.img_size = img_size
self.stride = stride
self.files = images + videos
self.nf = ni + nv # number of files
self.video_flag = [False] * ni + [True] * nv
self.mode = 'image'
if any(videos):
self.new_video(videos[0]) # new video
else:
self.cap = None
assert self.nf > 0, f'No images or videos found in {p}. ' \
f'Supported formats are:\nimages: {img_formats}\nvideos: {vid_formats}'
def __iter__(self):
self.count = 0
return self
def __next__(self):
if self.count == self.nf:
raise StopIteration
path = self.files[self.count]
if self.video_flag[self.count]:
# Read video
self.mode = 'video'
ret_val, img0 = self.cap.read()
if not ret_val:
self.count += 1
self.cap.release()
if self.count == self.nf: # last video
raise StopIteration
else:
path = self.files[self.count]
self.new_video(path)
ret_val, img0 = self.cap.read()
self.frame += 1
print(f'video {self.count + 1}/{self.nf} ({self.frame}/{self.nframes}) {path}: ', end='')
else:
# Read image
self.count += 1
img0 = cv2.imread(path) # BGR
assert img0 is not None, 'Image Not Found ' + path
print(f'image {self.count}/{self.nf} {path}: ', end='')
# Padded resize
img = letterboNgIrycUMx(img0, self.img_size, stride=self.stride)[0]
# Convert
img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416
img = np.ascontiguousarray(img)
return path, img, img0, self.cap
def new_video(self, path):
self.frame = 0
self.cap = cv2.VideoCapture(path)
self.nframes = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT))
def __len__(self):
return self.nf # number of files
class LoadWebcam: # for inference
def __init__(self, pipe='0', img_size=640, stride=32):
self.img_size = img_size
self.stride = stride
if pipe.isnumeric():
pipe = eval(pipe) # local camera
# pipe = 'rtsp://192.168.1.64/1' # IP camera
# pipe = 'rtsp://username:password@192.168.1.64/1' # IP camera with login
# pipe = 'http://wmccpinetop.axiscam.net/mjpg/video.mjpg' # IP golf camera
self.pipe = pipe
self.cap = cv2.VideoCapture(pipe) # video capture object
self.cap.set(cv2.CAP_PROP_BUFFERSIZE, 3) # set buffer size
def __iter__(self):
self.count = -1
return self
def __next__(self):
self.count += 1
if cv2.waitKey(1) == ord('q'): # q to quit
self.cap.release()
cv2.destroyAllWindows()
raise StopIteration
# Read frame
if self.pipe == 0: # local camera
ret_val, img0 = self.cap.read()
img0 = cv2.flip(img0, 1) # flip left-right
else: # IP camera
n = 0
while True:
n += 1
self.cap.grab()
if n % 30 == 0: # skip frames
ret_val, img0 = self.cap.retrieve()
if ret_val:
break
# Print
assert ret_val, f'Camera Error {self.pipe}'
img_path = 'webcam.jpg'
print(f'webcam {self.count}: ', end='')
# Padded resize
img = letterbox(img0, self.img_size, stride=self.stride)[0]
# Convert
img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416
img = np.ascontiguousarray(img)
return img_path, img, img0, None
def __len__(self):
return 0
class LoadStreams: # multiple IP or RTSP cameras
def __init__(self, sources='streams.txt', img_size=640, stride=32):
self.mode = 'stream'
self.img_size = img_size
self.stride = stride
if os.path.isfile(sources):
with open(sources, 'r') as f:
sources = [x.strip() for x in f.read().strip().splitlines() if len(x.strip())]
else:
sources = [sources]
n = len(sources)
self.imgs = [None] * n
self.sources = [clean_str(x) for x in sources] # clean source names for later
for i, s in enumerate(sources):
# Start the thread to read frames from the video stream
print(f'{i + 1}/{n}: {s}... ', end='')
cap = cv2.VideoCapture(eval(s) if s.isnumeric() else s)
assert cap.isOpened(), f'Failed to open {s}'
w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = cap.get(cv2.CAP_PROP_FPS) % 100
_, self.imgs[i] = cap.read() # guarantee first frame
thread = Thread(target=self.update, args=([i, cap]), daemon=True)
print(f' success ({w}x{h} at {fps:.2f} FPS).')
thread.start()
print('') # newline
# check for common shapes
s = np.stack([letterbox(x, self.img_size, stride=self.stride)[0].shape for x in self.imgs], 0) # shapes
self.rect = np.unique(s, axis=0).shape[0] == 1 # rect inference if all shapes equal
if not self.rect:
print('WARNING: Different stream shapes detected. For optimal performance supply similarly-shaped streams.')
def update(self, index, cap):
# Read next stream frame in a daemon thread
n = 0
while cap.isOpened():
n += 1
# _, self.imgs[index] = cap.read()
cap.grab()
if n == 4: # read every 4th frame
success, im = cap.retrieve()
self.imgs[index] = im if success else self.imgs[index] * 0
n = 0
time.sleep(0.01) # wait time
def __iter__(self):
self.count = -1
return self
def __next__(self):
self.count += 1
img0 = self.imgs.copy()
if cv2.waitKey(1) == ord('q'): # q to quit
cv2.destroyAllWindows()
raise StopIteration
# Letterbox
img = [letterbox(x, self.img_size, auto=self.rect, stride=self.stride)[0] for x in img0]
# Stack
img = np.stack(img, 0)
# Convert
img = img[:, :, :, ::-1].transpose(0, 3, 1, 2) # BGR to RGB, to bsx3x416x416
img = np.ascontiguousarray(img)
return self.sources, img, img0, None
def __len__(self):
return 0 # 1E12 frames = 32 streams at 30 FPS for 30 years
2.模型封装
接下来，我们借助detect.py文件对yolov5模型进行接口封装，使其提供模型推理能力。新建一个yolov5.py文件，构建一个名为darknet的类，使用函数detect，提供目标检测能力。其代码如下：
# coding:utf-8
import cv2
import json
import time
import torch
import numpy as np
from camera import LoadStreams, LoadImages
from utils.torch_utils import select_device
from models.experimental import attempt_load
from utils.general import non_max_suppression, scale_coords, letterbox, check_imshow
class Darknet(object):
"""docstring for Darknet"""
def __init__(self, opt):
self.opt = opt
self.device = select_device(self.opt["device"])
self.half = self.device.type != 'cpu' # half precision only supported on CUDA
self.model = attempt_load(self.opt["weights"], map_location=self.device)
self.stride = int(self.model.stride.max())
self.model.to(self.device).eval()
self.names = self.model.module.names if hasattr(self.model, 'module') else self.model.names
if self.half: self.model.half()
self.source = self.opt["source"]
self.webcam = self.source.isnumeric() or self.source.endswith('.txt') or self.source.lower().startswith(
('rtsp://', 'rtmp://', 'http://'))
def preprocess(self, img):
img = np.ascontiguousarray(img)
img = torch.from_numpy(img).to(self.device)
img = img.half() if self.half else img.float() # uint8 to fp16/32
img /= 255.0 # 图像归一化
if img.ndimension() == 3:
img = img.unsqueeze(0)
return img
def detect(self, dataset):
view_img = check_imshow()
t0 = time.time()
for path, img, img0s, vid_cap in dataset:
img = self.preprocess(img)
t1 = time.time()
pred = self.model(img, augment=self.opt["augment"])[0] # 0.22s
pred = pred.float()
pred = non_max_suppression(pred, self.opt["conf_thres"], self.opt["iou_thres"])
t2 = time.time()
pred_boxes = []
for i, det in enumerate(pred):
if self.webcam: # batch_size >= 1
p, s, im0, frame = path[i], '%g: ' % i, img0s[i].copy(), dataset.count
else:
p, s, im0, frame = path, '', img0s, getattr(dataset, 'frame', 0)
s += '%gx%g ' % img.shape[2:] # print string
gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
if det is not None and len(det):
det[:, :4] = scale_coords(
img.shape[2:], det[:, :4], im0.shape).round()
# Print results
for c in det[:, -1].unique():
n = (det[:, -1] == c).sum() # detections per class
s += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, " # add to shttp://www.cppcns.comtring
for *xyxy, conf, cls_id in det:
lbl = self.names[int(cls_id)]
xyxy = torch.tensor(xyxy).view(1, 4).view(-1).tolist()
score = round(conf.tolist(), 3)
label = "{}: {}".format(lbl, score)
x1, y1, x2, y2 = int(xyxy[0]), int(xyxy[1]), int(xyxy[2]), int(xyxy[3])
pred_boxes.append((x1, y1, x2, y2, lbl, score))
if view_img:
self.plot_one_box(xyxy, im0, color=(255, 0, 0), label=label)
# Print time (inference + NMS)
# print(pred_boxes)
print(f'{s}Done. ({t2 - t1:.3f}s)')
if view_img:
print(str(p))
cv2.imshow(str(p), cv2.resize(im0, (800, 600)))
if self.webcam:
if cv2.waitKey(1) & 0xFF == ord('q'): break
else:
cv2.waitKey(0)
print(f'Done. ({time.time() - t0:.3f}s)')
# print('[INFO] Inference time: {:.2f}s'.format(t3-t2))
# return pred_boxes
# Plotting functions
def plot_one_box(self, x, img, color=None, label=None, line_thickness=None):
# Plots one bounding box on image img
tl = line_thickness or round(0.001 * max(img.shape[0:2])) + 1 # line thickness
color = color or [random.randint(0, 255) for _ in range(3)]
c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))
cv2.rectangle(img, c1, c2, color, thickness=tl)
if label:
tf = max(tl - 1, 1) # font thickness
t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]
c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3
cv2.rectangle(img, c1, c2, color, -1) # filled
cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [0, 0, 0], thickness=tf, lineType=cv2.LINE_AA)
if __name__ == "__main__":
with open('yolov5_config.json', 'r', encoding='utf8') as fp:
opt = json.load(fp)
print('[INFO] YOLOv5 Config:', opt)
darknet = Darknet(opt)
if darknet.webcam:
# cudnn.benchmark = True # set True to speed up constant image size inference
dataset = LoadStreams(darknet.source, img_size=opt["imgsz"], stride=darknet.stride)
else:
dataset = LoadImages(darknet.source, img_size=opt["imgsz"], stride=darknet.stride)
darknet.detect(dataset)
cv2.destroyAllWindows()
此外，还需要提供一个模型配置文件，我们使用json文件进行保存。新建一个yolov5_config.json文件，内容如下：
{
"source": "streams.txt", # 为视频图像文件地址
"weights": "runs/train/exp/weights/best.pt", # 自己的模型地址
"device": "cpu", # 使用的device类别，如是GPU，可填"0"
"imgsz": 640, # 输入图像的大小
"stride": 32, # 步长
"conf_thres": 0.35, # 置信值阈值
"iou_thres": 0.45, # iou阈值
"augment": false # 是否使用图像增强
}
视频图像文件可以是单独的一张图像，如:"…/images/demo.jpg"，也可以是一个视频文件，如："…/videos/demo.mp4"，也可以是一个视频流地址，如：“rtsp://wowzaec2demo.streamlock.net/vod/mp4:BigBuckBunny_115k.mov”，还可以是一个txt文件，里面包含多个视频流地址，如：
rtsp://wowzaec2demo.streamlock.net/vod/mp4:BigBuckBunny_115k.mov rtsp://wowzaec2demo.streamlock.net/vod/mp4:BigBuckBunny_115k.mov
- 有了如此配置信息，通过运行yolov5.py代码，我们能实现对视频文件（mp4、avi等）、视频流地址（http、rtsp、rtmp等）、图片（jpg、png）等视频图像文件进行目标检测推理的效果。
3.Flask后端处理
有了对模型封装的代码，我们就可以利用flask框架实时向前端推送算法处理之后的图像了。新建一个web_main.py文件：
# import the necessary packages
from yolov5 import Darknet
from camera import LoadStreams, LoadImages
from utils.general import non_max_suppression, scale_coords, letterbox, check_imshow
from flask import Response
from flask import Flask
from flask import render_template
import time
import torch
import json
import cv2
import os
# initialize a flask object
app = Flask(__name__)
# initialize the video stream and allow the camera sensor to warmup
with open('yolov5_config.json', 'r', encoding='utf8') as fp:
opt = json.load(fp)
print('[INFO] YOLOv5 Config:', opt)
darknet = Darknet(opt)
if darknet.webcam:
# cudnn.benchmark = True # set True to speed up constant image size inference
dataset = LoadStreams(darknet.source, img_size=opt["imgsz"], stride=darknet.stride)
else:
dataset = LoadImages(darknet.source, img_size=opt["imgsz"], stride=darknet.stride)
time.sleep(2.0)
@app.route("/")
def index():
# return the rendered template
return render_template("index.html")
def detect_gen(dataset, feed_type):
view_img = check_i编程客栈mshow()
t0 = time.time()
for path, img, img0s, vid_cap in dataset:
img = darknet.preprocess(img)
t1 = time.time()
pred = darknet.model(img, augment=darknet.opt["augment"])[0] # 0.22s
pred = pred.float()
pred = non_max_suppression(pred, darknet.opt["conf_thres"], darknet.opt["iou_thres"])
t2 = time.time()
pred_boxes = []
for i, det in enumerate(pred):
if darknet.webcam: # batch_size >= 1
feed_type_curr, p, s, im0, frame = "Camera_%s" % str(i), path[i], '%g: ' % i, img0s[i].copy(), dataset.count
else:
feed_type_curr, p, s, im0, frame = "Camera", path, '', img0s, getattr(dataset, 'frame', 0)
s += '%gx%g ' % img.shape[2:] # print string
gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
if det is not None and len(det):
det[:, :4] = scale_coords(
img.shape[2:], det[:, :4], im0.shap编程客栈e).round()
# Print results
for c in det[:, -1].unique():
n = (det[:, -1] == c).sum() # detections per class
s += f"{n} {darknet.names[int(c)]}{'s' * (n > 1)}, " # add to string
for *xyxy, conf, cls_id in det:
lbl = darknet.names[int(cls_id)]
xyxy = torch.tensor(xyxy).view(1, 4).view(-1).tolist()
score = round(conf.tolist(), 3)
label = "{}: {}".format(lbl, score)
x1, y1, x2, y2 = int(xyxy[0]), int(xyxy[1]), int(xyxy[2]), int(xyxy[3])
pred_boxes.append((x1, y1, x2, y2, lbl, score))
if view_img:
darknet.plot_one_box(xyxy, im0, color=(255, 0, 0), label=label)
# Print time (inference + NMS)
# print(pred_boxes)
print(f'{s}Done. ({t2 - t1:.3f}s)')
if feed_type_curr == feed_type:
frame = cv2.imencode('.jpg', im0)[1].tobytes()
yield (b'--frame\r\n' b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n')
@app.route('/video_feed/<feed_type>')
def video_feed(feed_type):
"""Video streaming route. Put this in the src attribute of an img tag."""
if feed_type == 'Camera_0':
return Response(detect_gen(dataset=dataset, feed_type=feed_type),
mimetype='multipart/x-mixed-replace; boundary=framehttp://www.cppcns.com')
elif feed_type == 'Camera_1':
return Response(detect_gen(dataset=dataset, feed_type=feed_type),
mimetype='multipart/x-mixed-replace; boundary=frame')
if __name__ == '__main__':
app.run(host='0.0.0.0', port="5000", threaded=True)
通过detect_gen函数将多个视频流地址推理后的图像按照feed_type类型，通过video_feed视频流路由进行传送到前端。
4.前端展示
最后，我们写一个简单的前端代码。首先新建一个templates文件夹，再在此文件夹中新建一个index.html文件，将下面h5代码写入其中：
<html>
<head>
<style>
* {
box-sizing: border-box;
text-align: center;
}
.img-container {
float: left;
width: 30%;
padding: 5px;
}
.clearfix::after {
content: "";
clear: both;
display: table;
}
.clearfix{
margin-left: 500px;
}
</style>
</head>
<body>
<h1>Multi-camera with YOLOv5</h1>
<div class="clearfix">
<div class="img-container" align="center">
<p align="center">Live stream 1</p>
<img src="{{ url_for('video_feed', feed_type='Camera_0') }}" class="center" style="border:1px solid black;width:100%" alt="Live Stream 1">
</div>
<div class="img-container" align="center">
<p align="center">Live stream 2</p>
<img src="{{ url_for('video_feed', feed_type='Camera_1') }}" class="center" style="border:1px solid black;width:100%" alt="Live Stream 2">
</div>
</div>
</body>
</html>
至此，我们利用YOLOv5模型实现多路摄像头实时推理代码就写完了，下面我们开始运行：
- 在终端中进行跟目录下，直接运行：
python web_main.py
然后，会在终端中出现如下信息：
[INFO] YOLOv5 Config: {'source': 'streams.txt', 'weights': 'runs/train/exp/weights/best.pt', 'device': 'cpu', 'imgsz': 640, 'stride': 32, 'conf_thres': 0.35, 'iou_thres': 0.45, 'augment': False}
Fusing layers...
1/2: rtsp://wowzaec2demo.streamlock.net/vod/mp4:BigBuckBunny_115k.mov... success (240x160 at 24.00 FPS).
2/2: rtsp://wowzaec2demo.streamlock.net/vod/mp4:BigBuckBunny_115k.mov... success (240x160 at 24.00 FPS).
* Serving Flask app "web_main" (lazy loading)
* Environment: production
WARNING: This is a development server. Do not use it in a production deployment.
Use a production WSGI server instead.
* Debug mode: off
* Running on http://0.0.0.0:5000/ (Press CTRL+C to quit)
* 接着打开浏览器，输入localhost:5000后，终端没有报任何错误，则就会出现如下页面：
总结
1. 由于没有额外的视频流rtmp/rtsp文件地址，所以就找了一个公开的视频流地址，但是没有办法看到检测效果；
2. 部署的时候，只能使用视频流地址进行推理，且可以为多个视频流地址，保存为stream.txt，用yolov5_config.json导入；
3. 此demo版本为简易版的端到端模型部署方案，还可以根据场景需要添加更多功能。
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