前前篇链接:yolov3 flask部署 返回json（小白教程）_学术菜鸟小晨的博客-CSDN博客
前篇链接: yolov4 yolov4-tiny flask部署web服务_学术菜鸟小晨的博客-CSDN博客
有小伙伴想要一期部署检测视频的web服务,这之前一样把图片改成视频帧就可以啦.
下面就是完整代码!!!
webs1.py:
# -*- coding: utf-8 -*-#
#导入flask类库，render_template模板，
from flask import Flask, render_template, request, jsonify, make_response
#安全文件名
from werkzeug.utils import secure_filename
import os
import cv2
import time
import json
from PIL import Image
from io import BytesIO
import json
import numpy as np
from datetime import timedelta
import yolov4ss
set_upload_path = './images'
set_result_path = './out'
ALLOWED_EXTENSIONS = set(['png', 'jpg', 'JPG', 'PNG', 'bmp'])
app = Flask(__name__)
#URL地址
@app.route('/', methods=['POST', 'GET'])
def upload():
if request.method == 'POST':
f = request.form.get("name")
types = request.form.get("types")
#print(types,type(types))
filelist = os.listdir(f)
# print(pics)
#for im in pics:
basepath='./out'
for k in filelist:
im = os.path.join(f,k)
videoCapture = cv2.VideoCapture(im)
success, frame = videoCapture.read()
i = 0
timeF = 25
while success :
lab, img, loc, res = yolov4ss.yolo_detect(types,pathIn=frame)
print(img)
#检测结果写到的目录
if loc:#判断loc是否为空，检测到框再保存
cv2.imwrite(os.path.join(basepath, str(i)+'_res.jpg'), img)
i+=1
success, frame = videoCapture.read()
#return "ss"
#return res
return render_template('upload2.html')
if __name__ == '__main__':
app.run(host='0.0.0.0',port=4557, debug=True)yolov4ss:
# -*- coding: utf-8 -*-
from flask import Flask, request, jsonify
import cv2
import numpy as np
import os
import time
import json
'''
pathIn：原始图片的路径
pathOut：结果图片的路径
label_path：类别标签文件的路径
config_path：模型配置文件的路径
weights_path：模型权重文件的路径
confidence_thre：0-1，置信度（概率/打分）阈值，即保留概率大于这个值的边界框，默认为0.5
nms_thre：非极大值抑制的阈值，默认为0.3
'''
def yolo_detect(im=None,
pathIn=None,
label_path='./cfg/coco.names',
config_path='./cfg/yolov4.cfg',
weights_path='./cfg/yolov4.weights',
confidence_thre=0.5,
nms_thre=0.3):
#加载类别标签文件
LABELS = open(label_path).read().strip().split("\n")
nclass = len(LABELS)
# 为每个类别的边界框随机匹配相应颜色
np.random.seed(42)
COLORS = np.random.randint(0, 255, size=(nclass, 3), dtype='uint8')
img = (pathIn)
# print(pathIn)
# 载入图片并获取其维度
(H, W) = img.shape[:2]
# 加载模型配置和权重文件
net = cv2.dnn.readNetFromDarknet(config_path, weights_path)
# 获取YOLO输出层的名字
ln = net.getLayerNames()
ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()]
# 将图片构建成一个blob，设置图片尺寸，然后执行一次
# YOLO前馈网络计算，最终获取边界框和相应概率
blob = cv2.dnn.blobFromImage(img, 1 / 255.0, (416, 416), swapRB=True, crop=False)
net.setInput(blob)
start = time.time()
layerOutputs = net.forward(ln)
end = time.time()
# 初始化边界框，置信度（概率）以及类别
boxes = []
confidences = []
classIDs = []
# 迭代每个输出层，总共三个
for output in layerOutputs:
# 迭代每个检测
for detection in output:
# 提取类别ID和置信度
scores = detection[5:]
classID = np.argmax(scores)
confidence = scores[classID]
# 只保留置信度大于某值的边界框
if confidence > confidence_thre:
# 将边界框的坐标还原至与原图片相匹配，记住YOLO返回的是
# 边界框的中心坐标以及边界框的宽度和高度
box = detection[0:4] * np.array([W, H, W, H])
(centerX, centerY, width, height) = box.astype("int")
# 计算边界框的左上角位置
x = int(centerX - (width / 2))
y = int(centerY - (height / 2))
# 更新边界框，置信度（概率）以及类别
boxes.append([x, y, int(width), int(height)])
confidences.append(float(confidence))
classIDs.append(classID)
# 使用非极大值抑制方法抑制弱、重叠边界框
idxs = cv2.dnn.NMSBoxes(boxes, confidences, confidence_thre, nms_thre)
lab = []
loc = []
data={}
data["counts"]=len(idxs)
# 确保至少一个边界框
if len(idxs) > 0:
# 迭代每个边界框
for i in idxs.flatten():
# 提取边界框的坐标
(x, y) = (boxes[i][0], boxes[i][1])
(w, h) = (boxes[i][2], boxes[i][3])
# 绘制边界框以及在左上角添加类别标签和置信度
color = [int(c) for c in COLORS[classIDs[i]]]
cv2.rectangle(img, (x, y), (x + w, y + h), color, 2)
text = '{}: {:.3f}'.format(LABELS[classIDs[i]], confidences[i])
(text_w, text_h), baseline = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 2)
cv2.rectangle(img, (x, y-text_h-baseline), (x + text_w, y), color, -1)
cv2.putText(img, text, (x, y-5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 2)
text_inf = text + ' ' + '(' + str(x) + ',' + str(y) + ')' + ' ' + '宽:' + str(w) + '高:' + str(h)
info = {"label":LABELS[classIDs[i]],"confidences":confidences[i],"x":str(x),"y":str(y),"w":str(w),"h":str(h)}
data["data"+str(i)]=info
# print(filename,LABELS[classIDs[i]],confidences[i],str(x),str(y),str(w),str(h))
loc.append([x, y, w, h])
lab.append(text_inf)
res = jsonify(data)
return lab, img, loc, res
# if __name__ == '__main__':
# pathIn = './static/images/test1.jpg'
# im = cv2.imread('./static/images/test2.jpg')
# lab, img, loc = yolo_detect(pathIn=pathIn)
# print(lab)upload2.py:
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<title>上传图片</title>
</head>
<body>
<h1>YOLOV4视频检测</h1>
<form action="" enctype='multipart/form-data' method='POST'>
<p>检测视频路径：<input type="text" name="name" style="margin-top:10px;"/><p>
<p>类型：<input type='text' name='types' placeholder="5全部" style="width:300px;"/></p>
<br>
<input type="submit" value="上传并识别"/>
</form>
</body>
</html>
注:
1.其中类型可以为修改只检测某类或都检测.
2.检测视频路径填写检测视频的文件夹路径.
3.如果检测到框,会保存报警图片到out文件夹.
