广州网站制作有哪些,东莞网站seo推广,网站转移,百度登陆页面一、模型转换
1.onnxruntime
ONNX Runtime#xff08;ONNX Runtime或ORT#xff09;是一个开源的高性能推理引擎#xff0c;用于部署和运行机器学习模型。它的设计目标是优化执行使用Open Neural Network Exchange#xff08;ONNX#xff09;格式定义的模型#xff0c;…一、模型转换
1.onnxruntime
ONNX RuntimeONNX Runtime或ORT是一个开源的高性能推理引擎用于部署和运行机器学习模型。它的设计目标是优化执行使用Open Neural Network ExchangeONNX格式定义的模型ONNX是一种用于表示机器学习模型的开放标准。 ONNX Runtime提供了几个关键功能和优势 跨平台兼容性ONNX Runtime旨在与各种硬件和操作系统平台兼容包括Windows、Linux以及各种加速器如CPU、GPU和FPGA。这使得在不同环境中轻松部署和运行模型成为可能。 高性能ONNX Runtime经过性能优化能够提供低延迟的模型执行。它针对不同的硬件平台进行了优化以确保模型高效运行。 多框架支持ONNX Runtime可以与使用不同的机器学习框架创建的模型一起使用包括PyTorch、TensorFlow等这要归功于其对ONNX格式的支持。 模型转换ONNX Runtime可以将来自支持的框架的模型转换为ONNX格式从而更容易在各种部署场景中使用这些模型。 多语言支持ONNX Runtime可用于多种编程语言包括C、C、Python等使其可以被广泛的开发人员使用。 自定义运算符它支持自定义运算符允许开发人员扩展其功能以支持特定操作或硬件加速。
ONNX Runtime广泛用于各种机器学习应用的生产部署包括计算机视觉、自然语言处理等。它由ONNX社区积极维护并持续接受更新和改进。
2. pt模型与onnx模型
.pt 模型和 .onnx 模型是两种不同的模型文件格式用于表示深度学习模型。它们之间的主要区别包括 文件格式 .pt 模型这是PyTorch框架的权重文件格式通常以.pt或.pth扩展名保存。它包含了模型的权重参数和模型结构的定义。这个文件格式是PyTorch特定的。.onnx 模型这是ONNXOpen Neural Network Exchange格式的模型文件通常以.onnx扩展名保存。ONNX是一种中间表示格式独立于任何特定的深度学习框架用于跨不同框架之间的模型转换和部署。 框架依赖 .pt 模型它依赖于PyTorch框架因此在加载和运行时需要使用PyTorch库。这限制了它在不同框架上的直接使用。.onnx 模型ONNX 模型是独立于深度学习框架的可以在支持ONNX的不同框架中加载和运行例如ONNX Runtime、TensorFlow、Caffe2等。 跨平台兼容性 .pt 模型它通常需要在不同平台上进行PyTorch的兼容性配置可能需要额外的工作和依赖处理。.onnx 模型由于ONNX的独立性更容易在不同平台和硬件上进行部署无需担心框架依赖性问题。
3.Yolov8 .pt模型转换onnx
如果想跨平台兼容性.pt 模型要在不同框架中使用或进行跨平台部署要使用代码或库将其转换为 ONNX 格式。ONNX转换工具可以将PyTorch模型转换为ONNX格式。
from ultralytics import YOLO# load model
model YOLO(yolov8m.pt)# Export model
success model.export(formatonnx)二、模型推理
1.环境部署
onnx模型模型推理只要依赖onnxruntime库图像处理要依赖opencv所以只要安装这两个库就行不用安装过多的依赖。
pip install onnxruntime
pip install opencv-python
pip install numpy
pip install gradio2.部署代码
utils.py
import numpy as np
import cv2class_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]# Create a list of colors for each class where each color is a tuple of 3 integer values
rng np.random.default_rng(3)
colors rng.uniform(0, 255, size(len(class_names), 3))def nms(boxes, scores, iou_threshold):# Sort by scoresorted_indices np.argsort(scores)[::-1]keep_boxes []while sorted_indices.size 0:# Pick the last boxbox_id sorted_indices[0]keep_boxes.append(box_id)# Compute IoU of the picked box with the restious compute_iou(boxes[box_id, :], boxes[sorted_indices[1:], :])# Remove boxes with IoU over the thresholdkeep_indices np.where(ious iou_threshold)[0]# print(keep_indices.shape, sorted_indices.shape)sorted_indices sorted_indices[keep_indices 1]return keep_boxesdef multiclass_nms(boxes, scores, class_ids, iou_threshold):unique_class_ids np.unique(class_ids)keep_boxes []for class_id in unique_class_ids:class_indices np.where(class_ids class_id)[0]class_boxes boxes[class_indices,:]class_scores scores[class_indices]class_keep_boxes nms(class_boxes, class_scores, iou_threshold)keep_boxes.extend(class_indices[class_keep_boxes])return keep_boxesdef compute_iou(box, boxes):# Compute xmin, ymin, xmax, ymax for both boxesxmin np.maximum(box[0], boxes[:, 0])ymin np.maximum(box[1], boxes[:, 1])xmax np.minimum(box[2], boxes[:, 2])ymax np.minimum(box[3], boxes[:, 3])# Compute intersection areaintersection_area np.maximum(0, xmax - xmin) * np.maximum(0, ymax - ymin)# Compute union areabox_area (box[2] - box[0]) * (box[3] - box[1])boxes_area (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])union_area box_area boxes_area - intersection_area# Compute IoUiou intersection_area / union_areareturn ioudef xywh2xyxy(x):# Convert bounding box (x, y, w, h) to bounding box (x1, y1, x2, y2)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 draw_detections(image, boxes, scores, class_ids, mask_alpha0.3):det_img image.copy()img_height, img_width image.shape[:2]font_size min([img_height, img_width]) * 0.0006text_thickness int(min([img_height, img_width]) * 0.001)det_img draw_masks(det_img, boxes, class_ids, mask_alpha)# Draw bounding boxes and labels of detectionsfor class_id, box, score in zip(class_ids, boxes, scores):color colors[class_id]draw_box(det_img, box, color)label class_names[class_id]caption f{label} {int(score * 100)}%draw_text(det_img, caption, box, color, font_size, text_thickness)return det_imgdef detections_dog(image, boxes, scores, class_ids, mask_alpha0.3):det_img image.copy()img_height, img_width image.shape[:2]font_size min([img_height, img_width]) * 0.0006text_thickness int(min([img_height, img_width]) * 0.001)# det_img draw_masks(det_img, boxes, class_ids, mask_alpha)# Draw bounding boxes and labels of detectionsfor class_id, box, score in zip(class_ids, boxes, scores):color colors[class_id]draw_box(det_img, box, color)label class_names[class_id]caption f{label} {int(score * 100)}%draw_text(det_img, caption, box, color, font_size, text_thickness)return det_imgdef draw_box( image: np.ndarray, box: np.ndarray, color: tuple[int, int, int] (0, 0, 255),thickness: int 2) - np.ndarray:x1, y1, x2, y2 box.astype(int)return cv2.rectangle(image, (x1, y1), (x2, y2), color, thickness)def draw_text(image: np.ndarray, text: str, box: np.ndarray, color: tuple[int, int, int] (0, 0, 255),font_size: float 0.001, text_thickness: int 2) - np.ndarray:x1, y1, x2, y2 box.astype(int)(tw, th), _ cv2.getTextSize(texttext, fontFacecv2.FONT_HERSHEY_SIMPLEX,fontScalefont_size, thicknesstext_thickness)th int(th * 1.2)cv2.rectangle(image, (x1, y1),(x1 tw, y1 - th), color, -1)return cv2.putText(image, text, (x1, y1), cv2.FONT_HERSHEY_SIMPLEX, font_size, (255, 255, 255), text_thickness, cv2.LINE_AA)def draw_masks(image: np.ndarray, boxes: np.ndarray, classes: np.ndarray, mask_alpha: float 0.3) - np.ndarray:mask_img image.copy()# Draw bounding boxes and labels of detectionsfor box, class_id in zip(boxes, classes):color colors[class_id]x1, y1, x2, y2 box.astype(int)# Draw fill rectangle in mask imagecv2.rectangle(mask_img, (x1, y1), (x2, y2), color, -1)return cv2.addWeighted(mask_img, mask_alpha, image, 1 - mask_alpha, 0)
YOLODet.py
import time
import cv2
import numpy as np
import onnxruntimefrom detection.utils import xywh2xyxy, draw_detections, multiclass_nms,detections_dogclass YOLODet:def __init__(self, path, conf_thres0.7, iou_thres0.5):self.conf_threshold conf_thresself.iou_threshold iou_thres# Initialize modelself.initialize_model(path)def __call__(self, image):return self.detect_objects(image)def initialize_model(self, path):self.session onnxruntime.InferenceSession(path,providersonnxruntime.get_available_providers())# Get model infoself.get_input_details()self.get_output_details()def detect_objects(self, image):input_tensor self.prepare_input(image)# Perform inference on the imageoutputs self.inference(input_tensor)self.boxes, self.scores, self.class_ids self.process_output(outputs)return self.boxes, self.scores, self.class_idsdef prepare_input(self, image):self.img_height, self.img_width image.shape[:2]input_img cv2.cvtColor(image, cv2.COLOR_BGR2RGB)# Resize input imageinput_img cv2.resize(input_img, (self.input_width, self.input_height))# Scale input pixel values to 0 to 1input_img input_img / 255.0input_img input_img.transpose(2, 0, 1)input_tensor input_img[np.newaxis, :, :, :].astype(np.float32)return input_tensordef inference(self, input_tensor):start time.perf_counter()outputs self.session.run(self.output_names, {self.input_names[0]: input_tensor})# print(fInference time: {(time.perf_counter() - start)*1000:.2f} ms)return outputsdef process_output(self, output):predictions np.squeeze(output[0]).T# Filter out object confidence scores below thresholdscores np.max(predictions[:, 4:], axis1)predictions predictions[scores self.conf_threshold, :]scores scores[scores self.conf_threshold]if len(scores) 0:return [], [], []# Get the class with the highest confidenceclass_ids np.argmax(predictions[:, 4:], axis1)# Get bounding boxes for each objectboxes self.extract_boxes(predictions)# Apply non-maxima suppression to suppress weak, overlapping bounding boxes# indices nms(boxes, scores, self.iou_threshold)indices multiclass_nms(boxes, scores, class_ids, self.iou_threshold)return boxes[indices], scores[indices], class_ids[indices]def extract_boxes(self, predictions):# Extract boxes from predictionsboxes predictions[:, :4]# Scale boxes to original image dimensionsboxes self.rescale_boxes(boxes)# Convert boxes to xyxy formatboxes xywh2xyxy(boxes)return boxesdef rescale_boxes(self, boxes):# Rescale boxes to original image dimensionsinput_shape np.array([self.input_width, self.input_height, self.input_width, self.input_height])boxes np.divide(boxes, input_shape, dtypenp.float32)boxes * np.array([self.img_width, self.img_height, self.img_width, self.img_height])return boxesdef draw_detections(self, image, draw_scoresTrue, mask_alpha0.4):return detections_dog(image, self.boxes, self.scores,self.class_ids, mask_alpha)def get_input_details(self):model_inputs self.session.get_inputs()self.input_names [model_inputs[i].name for i in range(len(model_inputs))]self.input_shape model_inputs[0].shapeself.input_height self.input_shape[2]self.input_width self.input_shape[3]def get_output_details(self):model_outputs self.session.get_outputs()self.output_names [model_outputs[i].name for i in range(len(model_outputs))]3. 模型测试
图像推理
import cv2
import numpy as np
from detection import YOLODet
import gradio as grmodel yolov8m.onnx
yolo_det YOLODet(model, conf_thres0.5, iou_thres0.3)def det_img(cv_src):yolo_det(cv_src)cv_dst yolo_det.draw_detections(cv_src)return cv_dstif __name__ __main__:input gr.Image()output gr.Image()demo gr.Interface(fndet_img, inputsinput, outputsoutput)demo.launch()视频推理
def detectio_video(input_path,model_path,output_path):cap cv2.VideoCapture(input_path)fps int(cap.get(5))t int(1000 / fps)videoWriter Nonedet YOLODet(model_path, conf_thres0.3, iou_thres0.5)while True:# try:_, img cap.read()if img is None:breakdet(img)cv_dst det.draw_detections(img)if videoWriter is None:fourcc cv2.VideoWriter_fourcc(m, p, 4, v)videoWriter cv2.VideoWriter(output_path, fourcc, fps, (cv_dst.shape[1], cv_dst.shape[0]))videoWriter.write(cv_dst)cv2.imshow(detection, cv_dst)cv2.waitKey(t)if cv2.getWindowProperty(detection, cv2.WND_PROP_AUTOSIZE) 1:# 点x退出breakcap.release()videoWriter.release()cv2.destroyAllWindows()测试结果 目标检测
