OpenVino快速落地部署教程

        Openvino是由Intel开发的专门用于优化和部署人工智能推理的半开源的工具包，主要用于对深度推理做优化。本教程适用于Yolov5-7.0，直接跑Yolov5为6FPS，使用OpenVino后为30FPS，未来将会出一系列其他模型（Paddle等）的OpenVino部署教程，测试平台——Intel Nuc 11代i5处理器

一、安装OpenVino

进入OpenVino官网

https://docs.openvino.ai/2024/get-started/install-openvino.html

选择自己喜欢的下载方式，本教程采用OpenVino-2022.3.1版本

二、模型转换

1. 通过Yolov5自带的export.py文件将.pt转为.onnx格式

   python3 export.py --weights xxxx/xxxxx.pt --include onnx --batch_size 1 --opset 10PS:如果出现转换失败的提示，如：opset 10不支持或是onnx版本问题请重新搭建yolov5环境，按照requirements.txt里库的最低版本进行安装

2. 使用OpenVino工具链将.onnx转为xml、bin模型

   mo --input_model xxx/xxx.onnxPS：如果openvino环境安装成功将可以在yolov5的环境中直接使用mo命令

PS：转换完成后请一定用模型可视化工具查看转换是否正确

三、采用以下代码快速部署

import openvino.runtime as ovimport cv2import numpy as npimport openvino.preprocess as opclass ObjectDetector: def __init__(self, model_xml, model_bin, labels, device="CPU"): self.core = ov.Core() self.model = self.core.read_model(model_xml, model_bin) self.labels = labels self.preprocess_model() self.compiled_model = self.core.compile_model(self.model, device) self.infer_request = self.compiled_model.create_infer_request() def preprocess_model(self): premodel = op.PrePostProcessor(self.model) premodel.input().tensor().set_element_type(ov.Type.u8).set_layout(ov.Layout("NHWC")).set_color_format(op.ColorFormat.BGR) premodel.input().preprocess().convert_element_type(ov.Type.f32).convert_color(op.ColorFormat.RGB).scale([255., 255., 255.]) premodel.input().model().set_layout(ov.Layout("NCHW")) premodel.output(0).tensor().set_element_type(ov.Type.f32) self.model = premodel.build() def infer(self, img): detections = [] img_re, dw, dh = self.resizeimg(img, (640, 640)) input_tensor = np.expand_dims(img_re, 0) self.infer_request.infer({0: input_tensor}) output = self.infer_request.get_output_tensor(0) detections = self.process_output(output.data[0]) return detections def process_output(self, detections): boxes = [] class_ids = [] confidences = [] for prediction in detections: confidence = prediction[4].item() if confidence >= 0.6: classes_scores = prediction[5:] _, _, _, max_indx = cv2.minMaxLoc(classes_scores) class_id = max_indx[1] if (classes_scores[class_id] > .25): confidences.append(confidence) class_ids.append(class_id) x, y, w, h = prediction[0].item(), prediction[1].item(), prediction[2].item(), prediction[3].item() xmin = x - (w / 2) ymin = y - (h / 2) box = np.array([xmin, ymin, w, h]) boxes.append(box) indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.5) detections = [] for i in indexes: j = i.item() detections.append({"class_index": class_ids[j],"confidence": confidences[j],"box": boxes[j]}) return detections def resizeimg(self, image, new_shape): old_size = image.shape[:2] ratio = float(new_shape[-1] / max(old_size)) new_size = tuple([int(x * ratio) for x in old_size]) image = cv2.resize(image, (new_size[1], new_size[0])) delta_w = new_shape[1] - new_size[1] delta_h = new_shape[0] - new_size[0] color = [100, 100, 100] new_im = cv2.copyMakeBorder(image, 0, delta_h, 0, delta_w, cv2.BORDER_CONSTANT, value=color) return new_im, delta_w, delta_hif __name__ =="__main__": # Example usage: labels = ["right","warning","left","people","10","pullover","10off","green","red" ] detector = ObjectDetector("/home/nuc/MyCar/yolov5-7.0/best.xml","/home/nuc/MyCar/yolov5-7.0/best.bin", labels,"CPU") cap = cv2.VideoCapture(0) while True: ret, frame = cap.read() detections = detector.infer(frame) for detection in detections: classId = detection["class_index"] confidence = detection["confidence"] label = labels[classId] box = detection["box"] area = box[2] * box[3] print(f"Detected object: {label}, Confidence: {confidence}, Area: {area}") cap.release()