目录结构如下：
分类模型web部署
classification.py
import osimport cv2import numpy as npimport onnxruntimefrom 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)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<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) / 57.375 return 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：
检测模型web部署
detection.py
import osimport cv2import numpy as npimport onnxruntimefrom 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 = 0.5confidence_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 * h ious = 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] / 2 y[:, 1] = x[:, 1] - x[:, 3] / 2 y[:, 2] = x[:, 0] + x[:, 2] / 2 y[:, 3] = x[:, 1] + x[:, 3] / 2 return 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_id if 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 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 border return imdef scale_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, y2 return 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 = 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)def preprocess(img): input = letterbox(img, input_shape) input = input[:, :, ::-1].transpose(2, 0, 1).astype(dtype=np.float32) input = input / 255.0 input = 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：