yolov3做成flask接口返回json

# -*- coding: utf-8 -*- from flask import Flask, request, jsonify import cv2 import numpy as np import os import time import json def yolo_detect(im=None, pathIn=None, label_path='./cfg/voc.names', config_path='./cfg/yolov3.cfg', weights_path='./cfg/yolov35.weights', confidence_thre=0.5, nms_thre=0.3): LABELS = open(label_path).read().strip().split("\n") nclass = len(LABELS) np.random.seed(42) COLORS = np.random.randint(0, 255, size=(nclass, 3), dtype='uint8') if pathIn == None: img = im else: img = cv2.imread(pathIn) # print(pathIn) filename = pathIn.split('/')[-1] name = filename.split('.')[0] (H, W) = img.shape[:2] net = cv2.dnn.readNetFromDarknet(config_path, weights_path) ln = net.getLayerNames() ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()] 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() boxes = [] confidences = [] classIDs = [] for output in layerOutputs: for detection in output: scores = detection[5:] classID = np.argmax(scores) confidence = scores[classID] 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)) boxes.append([x, y, int(width), int(height)]) confidences.append(float(confidence)) classIDs.append(classID) idxs = cv2.dnn.NMSBoxes(boxes, confidences, confidence_thre, nms_thre) lab = [] loc = [] data={} data["filename"]=filename data["counts"]=len(idxs) if len(idxs) > 0: for i in idxs.flatten(): (x, y) = (boxes[i][0], boxes[i][1]) (w, h) = (boxes[i][2], boxes[i][3]) 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)