用Flask搭建简单的web模型部署服务

目录结构如下：
在这里插入图片描述

分类模型web部署

classification.py

import os
import cv2
import numpy as np
import onnxruntime
from flask import Flask, render_template, request, jsonify
 
 
app = Flask(__name__)

onnx_session = onnxruntime.InferenceSession("mobilenet_v2.onnx", providers=['CPUExecutionProvider'])

input_name =[]for node in onnx_session.get_inputs():
    input_name.append(node.name)

output_name =[]for node in onnx_session.get_outputs():
    output_name.append(node.name)defallowed_file(filename):return'.'in filename and filename.rsplit('.',1)[1]inset(['bmp','jpg','JPG','png','PNG'])defpreprocess(image):if image.shape[0]< image.shape[1]:#h<w
        image = cv2.resize(image,(int(256*image.shape[1]/image.shape[0]),256))else:
        image = cv2.resize(image,(256,int(256*image.shape[0]/image.shape[1])))

    crop_size =min(image.shape[0], image.shape[1])
    left =int((image.shape[1]-crop_size)/2)
    top =int((image.shape[0]-crop_size)/2)
    image_crop = image[top:top+crop_size, left:left+crop_size]
    image_crop = cv2.resize(image_crop,(224,224))

    image_crop = image_crop[:,:,::-1].transpose(2,0,1).astype(np.float32)#BGR2RGB和HWC2CHW
    image_crop[0,:]=(image_crop[0,:]-123.675)/58.395   
    image_crop[1,:]=(image_crop[1,:]-116.28)/57.12
    image_crop[2,:]=(image_crop[2,:]-103.53)/57.375return  np.expand_dims(image_crop, axis=0)@app.route('/classification', methods=['POST','GET'])# 添加路由defclassification():if request.method =='POST':
        f = request.files['file']ifnot(f and allowed_file(f.filename)):return jsonify({"error":1001,"msg":"only support image formats: .bmp .png .PNG .jpg .JPG"})
 
        basepath = os.path.dirname(__file__)# 当前文件所在路径
        upload_path = os.path.join(basepath,'static/images/temp.jpg')# 注意：没有的文件夹一定要先创建，不然会提示没有该路径
        f.save(upload_path)
 
        image = cv2.imread(upload_path)     
        tensor = preprocess(image)
        inputs ={}for name in input_name:
            inputs[name]= tensor   
        outputs = onnx_session.run(None, inputs)[0]
        label = np.argmax(outputs)
        score = np.exp(outputs[0][label])/ np.sum(np.exp(outputs), axis=1)return render_template('classification.html', label=label, score=score[0])return render_template('upload.html')if __name__ =='__main__':
    app.run(host='0.0.0.0', port=8000, debug=True)

classification.html

<!DOCTYPEhtml><htmllang="en"><head><metacharset="UTF-8"></head><body><h1>请上传本地图片</h1><formaction=""enctype='multipart/form-data'method='POST'><inputtype="file"name="file"style="margin-top:20px;"/><inputtype="submit"value="上传"class="button-new"style="margin-top:15px;"/></form><h2>图片类别为：{{label}}        置信度为：{{score}} </h2><imgsrc="{{ url_for('static', filename= './images/temp.jpg') }}"alt="你的图片被外星人劫持了～～"/></body></html>

运行程序，在浏览器输入http://127.0.0.1:8000/classification，效果展示：
在这里插入图片描述

检测模型web部署

detection.py

import os
import cv2
import numpy as np
import onnxruntime
from flask import Flask, render_template, request, jsonify
 
 
app = Flask(__name__)

class_names =['person','bicycle','car','motorcycle','airplane','bus','train','truck','boat','traffic light','fire hydrant','stop sign','parking meter','bench','bird','cat','dog','horse','sheep','cow','elephant','bear','zebra','giraffe','backpack','umbrella','handbag','tie','suitcase','frisbee','skis','snowboard','sports ball','kite','baseball bat','baseball glove','skateboard','surfboard','tennis racket','bottle','wine glass','cup','fork','knife','spoon','bowl','banana','apple','sandwich','orange','broccoli','carrot','hot dog','pizza','donut','cake','chair','couch','potted plant','bed','dining table','toilet','tv','laptop','mouse','remote','keyboard','cell phone','microwave','oven','toaster','sink','refrigerator','book','clock','vase','scissors','teddy bear','hair drier','toothbrush']#coco80类别      
input_shape =(640,640) 
score_threshold =0.2  
nms_threshold =0.5
confidence_threshold =0.2   

onnx_session = onnxruntime.InferenceSession("yolov5n.onnx", providers=['CPUExecutionProvider'])

input_name =[]for node in onnx_session.get_inputs():
    input_name.append(node.name)

output_name =[]for node in onnx_session.get_outputs():
    output_name.append(node.name)defallowed_file(filename):return'.'in filename and filename.rsplit('.',1)[1]inset(['bmp','jpg','JPG','png','PNG'])defnms(boxes, scores, score_threshold, nms_threshold):
    x1 = boxes[:,0]
    y1 = boxes[:,1]
    x2 = boxes[:,2]
    y2 = boxes[:,3]
    areas =(y2 - y1 +1)*(x2 - x1 +1)
    keep =[]
    index = scores.argsort()[::-1]while index.size >0:
        i = index[0]
        keep.append(i)
        x11 = np.maximum(x1[i], x1[index[1:]]) 
        y11 = np.maximum(y1[i], y1[index[1:]])
        x22 = np.minimum(x2[i], x2[index[1:]])
        y22 = np.minimum(y2[i], y2[index[1:]])
        w = np.maximum(0, x22 - x11 +1)                              
        h = np.maximum(0, y22 - y11 +1) 
        overlaps = w * h
        ious = overlaps /(areas[i]+ areas[index[1:]]- overlaps)
        idx = np.where(ious <= nms_threshold)[0]
        index = index[idx +1]return keep

defxywh2xyxy(x):
    y = np.copy(x)
    y[:,0]= x[:,0]- x[:,2]/2
    y[:,1]= x[:,1]- x[:,3]/2
    y[:,2]= x[:,0]+ x[:,2]/2
    y[:,3]= x[:,1]+ x[:,3]/2return y

deffilter_box(outputs):#过滤掉无用的框    
    outputs = np.squeeze(outputs)
    outputs = outputs[outputs[...,4]> confidence_threshold]
    classes_scores = outputs[...,5:]
     
    boxes =[]
    scores =[]
    class_ids =[]for i inrange(len(classes_scores)):
        class_id = np.argmax(classes_scores[i])
        outputs[i][4]*= classes_scores[i][class_id]
        outputs[i][5]= class_id
        if outputs[i][4]> score_threshold:
            boxes.append(outputs[i][:6])
            scores.append(outputs[i][4])
            class_ids.append(outputs[i][5])iflen(boxes)==0:return      
    boxes = np.array(boxes)
    boxes = xywh2xyxy(boxes)
    scores = np.array(scores)
    indices = nms(boxes, scores, score_threshold, nms_threshold) 
    output = boxes[indices]return output

defletterbox(im, new_shape=(416,416), color=(114,114,114)):# Resize and pad image while meeting stride-multiple constraints
    shape = im.shape[:2]# current shape [height, width]# Scale ratio (new / old)
    r =min(new_shape[0]/ shape[0], new_shape[1]/ shape[1])# Compute padding
    new_unpad =int(round(shape[1]* r)),int(round(shape[0]* r))    
    dw, dh =(new_shape[1]- new_unpad[0])/2,(new_shape[0]- new_unpad[1])/2# wh padding 
    top, bottom =int(round(dh -0.1)),int(round(dh +0.1))
    left, right =int(round(dw -0.1)),int(round(dw +0.1))if shape[::-1]!= new_unpad:# resize
        im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)
    im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)# add borderreturn im

defscale_boxes(boxes, shape):# Rescale boxes (xyxy) from input_shape to shape
    gain =min(input_shape[0]/ shape[0], input_shape[1]/ shape[1])# gain  = old / new
    pad =(input_shape[1]- shape[1]* gain)/2,(input_shape[0]- shape[0]* gain)/2# wh padding
    boxes[...,[0,2]]-= pad[0]# x padding
    boxes[...,[1,3]]-= pad[1]# y padding
    boxes[...,:4]/= gain
    boxes[...,[0,2]]= boxes[...,[0,2]].clip(0, shape[1])# x1, x2
    boxes[...,[1,3]]= boxes[...,[1,3]].clip(0, shape[0])# y1, y2return boxes

defdraw(image, box_data):
    box_data = scale_boxes(box_data, image.shape)
    boxes = box_data[...,:4].astype(np.int32) 
    scores = box_data[...,4]
    classes = box_data[...,5].astype(np.int32)for box, score, cl inzip(boxes, scores, classes):
        top, left, right, bottom = box
        cv2.rectangle(image,(top, left),(right, bottom),(255,0,0),1)
        cv2.putText(image,'{0} {1:.2f}'.format(class_names[cl], score),(top, left), cv2.FONT_HERSHEY_SIMPLEX,0.6,(0,0,255),1)defpreprocess(img):input= letterbox(img, input_shape)input=input[:,:,::-1].transpose(2,0,1).astype(dtype=np.float32)input=input/255.0input= np.expand_dims(input, axis=0)[email protected]('/detection', methods=['POST','GET'])# 添加路由defdetection():if request.method =='POST':
        f = request.files['file']ifnot(f and allowed_file(f.filename)):return jsonify({"error":1001,"msg":"only support image formats: .bmp .png .PNG .jpg .JPG"})
 
        basepath = os.path.dirname(__file__)# 当前文件所在路径
        upload_path = os.path.join(basepath,'static/images/temp.jpg')# 注意：没有的文件夹一定要先创建，不然会提示没有该路径
        f.save(upload_path)
 
        image = cv2.imread(upload_path)     
        tensor = preprocess(image)
        inputs ={}for name in input_name:
            inputs[name]= tensor   
        outputs = onnx_session.run(None, inputs)[0]
        
        boxes = filter_box(outputs)if boxes isnotNone:
            draw(image, boxes)
        cv2.imwrite(os.path.join(basepath,'static/images/temp.jpg'), image)return render_template('detection.html')return render_template('upload.html')if __name__ =='__main__':
    app.run(host='0.0.0.0', port=8000, debug=True)

detection.html

<!DOCTYPEhtml><htmllang="en"><head><metacharset="UTF-8"></head><body><h1>请上传本地图片</h1><formaction=""enctype='multipart/form-data'method='POST'><inputtype="file"name="file"style="margin-top:20px;"/><inputtype="submit"value="上传"class="button-new"style="margin-top:15px;"/></form><imgsrc="{{ url_for('static', filename= './images/temp.jpg') }}"alt="你的图片被外星人劫持了～～"/></body></html>

运行程序，在浏览器输入http://127.0.0.1:8000/detection，效果展示：
在这里插入图片描述

标签： flask 前端 python

本文转载自: https://blog.csdn.net/taifyang/article/details/134766010
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用Flask搭建简单的web模型部署服务

分类模型web部署

检测模型web部署

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