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用Flask搭建简单的web模型部署服务

news 2025/7/30 10:23:00

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, jsonifyapp = 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)def allowed_file(filename):return '.' in filename and filename.rsplit('.', 1)[1] in set(['bmp', 'jpg', 'JPG', 'png', 'PNG'])def preprocess(image):if image.shape[0] < image.shape[1]: #h<wimage = 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HWC2CHWimage_crop[0,:] = (image_crop[0,:] - 123.675) / 58.395   image_crop[1,:] = (image_crop[1,:] - 116.28) / 57.12image_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
def classification():if request.method == 'POST':f = request.files['file']if not (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

<!DOCTYPE html>
<html lang="en">
<head><meta charset="UTF-8">
</head>
<body><h1>请上传本地图片</h1><form action="" enctype='multipart/form-data' method='POST'><input type="file" name="file" style="margin-top:20px;"/><input type="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><img src="{{ 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, jsonifyapp = 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)def allowed_file(filename):return '.' in filename and filename.rsplit('.', 1)[1] in set(['bmp', 'jpg', 'JPG', 'png', 'PNG'])def nms(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 * hious = overlaps / (areas[i] + areas[index[1:]] - overlaps)idx = np.where(ious <= nms_threshold)[0]index = index[idx + 1]return keepdef xywh2xyxy(x):y = np.copy(x)y[:, 0] = x[:, 0] - x[:, 2] / 2y[:, 1] = x[:, 1] - x[:, 3] / 2y[:, 2] = x[:, 0] + x[:, 2] / 2y[:, 3] = x[:, 1] + x[:, 3] / 2return ydef filter_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 in range(len(classes_scores)):class_id = np.argmax(classes_scores[i])outputs[i][4] *= classes_scores[i][class_id]outputs[i][5] = class_idif outputs[i][4] > score_threshold:boxes.append(outputs[i][:6])scores.append(outputs[i][4])class_ids.append(outputs[i][5])if len(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 outputdef letterbox(im, new_shape=(416, 416), color=(114, 114, 114)):# Resize and pad image while meeting stride-multiple constraintsshape = im.shape[:2]  # current shape [height, width]# Scale ratio (new / old)r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])# Compute paddingnew_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:  # resizeim = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # add borderreturn imdef scale_boxes(boxes, shape): # Rescale boxes (xyxy) from input_shape to shapegain = min(input_shape[0] / shape[0], input_shape[1] / shape[1])  # gain  = old / newpad = (input_shape[1] - shape[1] * gain) / 2, (input_shape[0] - shape[0] * gain) / 2  # wh paddingboxes[..., [0, 2]] -= pad[0]  # x paddingboxes[..., [1, 3]] -= pad[1]  # y paddingboxes[..., :4] /= gainboxes[..., [0, 2]] = boxes[..., [0, 2]].clip(0, shape[1])  # x1, x2boxes[..., [1, 3]] = boxes[..., [1, 3]].clip(0, shape[0])  # y1, y2return boxesdef draw(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 in zip(boxes, scores, classes):top, left, right, bottom = boxcv2.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)def preprocess(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)return input@app.route('/detection', methods=['POST', 'GET'])  # 添加路由
def detection():if request.method == 'POST':f = request.files['file']if not (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 is not None: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

<!DOCTYPE html>
<html lang="en">
<head><meta charset="UTF-8">
</head>
<body><h1>请上传本地图片</h1><form action="" enctype='multipart/form-data' method='POST'><input type="file" name="file" style="margin-top:20px;"/><input type="submit" value="上传" class="button-new" style="margin-top:15px;"/></form><img src="{{ 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

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