YOLOV5 onnx推理 python
pip install onnx coremltools onnx-simplifier
3.使用onnx-simplier简化模型
python -m onnxsim best.onnx best-sim.onnx
# coding=utf-8 import cv2 import numpy as np import onnxruntime import torch import torchvision import time import random from utils.general import non_max_suppression class YOLOV5_ONNX(object): def __init__(self,onnx_path): '''初始化onnx''' self.onnx_session=onnxruntime.InferenceSession(onnx_path) print(onnxruntime.get_device()) self.input_name=self.get_input_name() self.output_name=self.get_output_name() self.classes=['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'] def get_input_name(self): '''获取输入节点名称''' input_name=[] for node in self.onnx_session.get_inputs(): input_name.append(node.name) return input_name def get_output_name(self): '''获取输出节点名称''' output_name=[] for node in self.onnx_session.get_outputs(): output_name.append(node.name) return output_name def get_input_feed(self,image_tensor): '''获取输入tensor''' input_feed={} for name in self.input_name: input_feed[name]=image_tensor return input_feed def letterbox(self,img, new_shape=(640, 640), color=(114, 114, 114), auto=False, scaleFill=False, scaleup=True, stride=32): '''图片归一化''' # Resize and pad image while meeting stride-multiple constraints shape = img.shape[:2] # current shape [height, width] if isinstance(new_shape, int): new_shape = (new_shape, new_shape) # Scale ratio (new / old) r = min(new_shape[0] / shape[0], new_shape[1] / shape[1]) if not scaleup: # only scale down, do not scale up (for better test mAP) r = min(r, 1.0) # Compute padding ratio = r, r # width, height ratios new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r)) dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding if auto: # minimum rectangle dw, dh = np.mod(dw, stride), np.mod(dh, stride) # wh padding elif scaleFill: # stretch dw, dh = 0.0, 0.0 new_unpad = (new_shape[1], new_shape[0]) ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratios dw /= 2 # divide padding into 2 sides dh /= 2 if shape[::-1] != new_unpad: # resize img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR) top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1)) left, right = int(round(dw - 0.1)), int(round(dw + 0.1)) img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border return img, ratio, (dw, dh) def xywh2xyxy(self,x): # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right y = np.copy(x) y[:, 0] = x[:, 0] - x[:, 2] / 2 # top left x y[:, 1] = x[:, 1] - x[:, 3] / 2 # top left y y[:, 2] = x[:, 0] + x[:, 2] / 2 # bottom right x y[:, 3] = x[:, 1] + x[:, 3] / 2 # bottom right y return y def nms(self,prediction, conf_thres=0.1, iou_thres=0.6, agnostic=False): if prediction.dtype is torch.float16: prediction = prediction.float() # to FP32 xc = prediction[..., 4] > conf_thres # candidates min_wh, max_wh = 2, 4096 # (pixels) minimum and maximum box width and height max_det = 300 # maximum number of detections per image output = [None] * prediction.shape[0] for xi, x in enumerate(prediction): # image index, image inference x = x[xc[xi]] # confidence if not x.shape[0]: continue x[:, 5:] *= x[:, 4:5] # conf = obj_conf * cls_conf box = self.xywh2xyxy(x[:, :4]) conf, j = x[:, 5:].max(1, keepdim=True) x = torch.cat((torch.tensor(box), conf, j.float()), 1)[conf.view(-1) > conf_thres] n = x.shape[0] # number of boxes if not n: continue c = x[:, 5:6] * (0 if agnostic else max_wh) # classes boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores i = torchvision.ops.boxes.nms(boxes, scores, iou_thres) if i.shape[0] > max_det: # limit detections i = i[:max_det] output[xi] = x[i] return output def clip_coords(self,boxes, img_shape): '''查看是否越界''' # Clip bounding xyxy bounding boxes to image shape (height, width) boxes[:, 0].clamp_(0, img_shape[1]) # x1 boxes[:, 1].clamp_(0, img_shape[0]) # y1 boxes[:, 2].clamp_(0, img_shape[1]) # x2 boxes[:, 3].clamp_(0, img_shape[0]) # y2 def scale_coords(self,img1_shape, coords, img0_shape, ratio_pad=None): ''' 坐标对应到原始图像上，反操作：减去pad，除以最小缩放比例 :param img1_shape: 输入尺寸 :param coords: 输入坐标 :param img0_shape: 映射的尺寸 :param ratio_pad: :return: ''' # Rescale coords (xyxy) from img1_shape to img0_shape if ratio_pad is None: # calculate from img0_shape gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new,计算缩放比率 pad = (img1_shape[1] - img0_shape[1] * gain) / 2, ( img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding ，计算扩充的尺寸 else: gain = ratio_pad[0][0] pad = ratio_pad[1] coords[:, [0, 2]] -= pad[0] # x padding，减去x方向上的扩充 coords[:, [1, 3]] -= pad[1] # y padding，减去y方向上的扩充 coords[:, :4] /= gain # 将box坐标对应到原始图像上 self.clip_coords(coords, img0_shape) # 边界检查 return coords def sigmoid(self,x): return 1 / (1 + np.exp(-x)) def infer(self,img_path): '''执行前向操作预测输出''' # 超参数设置 img_size=(640,640) #图片缩放大小 # 读取图片 src_img=cv2.imread(img_path) start=time.time() src_size=src_img.shape[:2] # 图片填充并归一化 img=self.letterbox(src_img,img_size,stride=32)[0] # Convert img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 img = np.ascontiguousarray(img) # 归一化 img=img.astype(dtype=np.float32) img/=255.0 # # BGR to RGB # img = img[:, :, ::-1].transpose(2, 0, 1) # img = np.ascontiguousarray(img) # 维度扩张 img=np.expand_dims(img,axis=0) print('img resuming: ',time.time()-start) # 前向推理 # start=time.time() input_feed=self.get_input_feed(img) # ort_inputs = {self.onnx_session.get_inputs()[0].name: input_feed[None].numpy()} pred = torch.tensor(self.onnx_session.run(None, input_feed)[0]) results = non_max_suppression(pred, 0.5,0.5) print('onnx resuming: ',time.time()-start) # pred=self.onnx_session.run(output_names=self.output_name,input_feed=input_feed) #映射到原始图像 img_shape=img.shape[2:] # print(img_size) for det in results: # detections per image if det is not None and len(det): det[:, :4] = self.scale_coords(img_shape, det[:, :4],src_size).round() print(time.time()-start) if det is not None and len(det): self.draw(src_img, det) def plot_one_box(self,x, img, color=None, label=None, line_thickness=None): # Plots one bounding box on image img tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1 # line/font thickness color = color or [random.randint(0, 255) for _ in range(3)] c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3])) cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA) if label: tf = max(tl - 1, 1) # font thickness t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0] c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3 cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA) # filled cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA) def draw(self,img, boxinfo): colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(self.classes))] for *xyxy, conf, cls in boxinfo: label = '%s %.2f' % (self.classes[int(cls)], conf) # print('xyxy: ', xyxy) self.plot_one_box(xyxy, img, label=label, color=colors[int(cls)], line_thickness=1) cv2.namedWindow("dst",0) cv2.imshow("dst", img) cv2.imwrite("res1.jpg",img) cv2.waitKey(0) # cv2.imencode('.jpg', img)[1].tofile(os.path.join(dst, id + ".jpg")) return 0 if __name__=="__main__": model=YOLOV5_ONNX(onnx_path='./yolov5s6.onnx') model.infer(img_path="./data/images/bus.jpg")
结果显示：
