detectonnx.py

import os
import cv2
import numpy as np
import onnxruntime
import time
 
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']  # coco80类别
 
 
class YOLOV5():
    def __init__(self, onnxpath):
        self.onnx_session = onnxruntime.InferenceSession(onnxpath, providers=['CPUExecutionProvider'])
        self.input_name = self.get_input_name()
        self.output_name = self.get_output_name()
 
    # -------------------------------------------------------
    #   获取输入输出的名字
    # -------------------------------------------------------
    def get_input_name(self):
        input_name = []
        for node in self.onnx_session.get_inputs():
            input_name.append(node.name)
        return input_name
 
    def get_output_name(self):
        output_name = []
        for node in self.onnx_session.get_outputs():
            output_name.append(node.name)
        return output_name
 
    # -------------------------------------------------------
    #   输入图像
    # -------------------------------------------------------
    def get_input_feed(self, img_tensor):
        input_feed = {}
        for name in self.input_name:
            input_feed[name] = img_tensor
        return input_feed
 
    # -------------------------------------------------------
    #   1.cv2读取图像并resize
    #   2.图像转BGR2RGB和HWC2CHW
    #   3.图像归一化
    #   4.图像增加维度
    #   5.onnx_session 推理
    # -------------------------------------------------------
    def inference(self, img_path):
        img = cv2.imread(img_path)
        img_o=img.copy()
        or_img = cv2.resize(img, (640, 640))
        img = or_img[:, :, ::-1].transpose(2, 0, 1)  # BGR2RGB和HWC2CHW
        img = img.astype(dtype=np.float32)
        img /= 255.0
        img = np.expand_dims(img, axis=0)
        input_feed = self.get_input_feed(img)
        pred = self.onnx_session.run(None, input_feed)[0]
        return pred, img_o
 
 
# dets:  array [x,6] 6个值分别为x1,y1,x2,y2,score,class
# thresh: 阈值
def nms(dets, thresh):
    x1 = dets[:, 0]
    y1 = dets[:, 1]
    x2 = dets[:, 2]
    y2 = dets[:, 3]
    # -------------------------------------------------------
    #   计算框的面积
    #   置信度从大到小排序
    # -------------------------------------------------------
    areas = (y2 - y1 + 1) * (x2 - x1 + 1)
    scores = dets[:, 4]
    keep = []
    index = scores.argsort()[::-1]
 
    while index.size > 0:
        i = index[0]
        keep.append(i)
        # -------------------------------------------------------
        #   计算相交面积
        #   1.相交
        #   2.不相交
        # -------------------------------------------------------
        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
        # -------------------------------------------------------
        #   计算该框与其它框的IOU，去除掉重复的框，即IOU值大的框
        #   IOU小于thresh的框保留下来
        # -------------------------------------------------------
        ious = overlaps / (areas[i] + areas[index[1:]] - overlaps)
        idx = np.where(ious <= thresh)[0]
        index = index[idx + 1]
    return keep
 
 
def xywh2xyxy(x):
    # [x, y, w, h] to [x1, y1, x2, y2]
    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 y
 
 
def filter_box(org_box, conf_thres, iou_thres):  # 过滤掉无用的框
    # -------------------------------------------------------
    #   删除为1的维度
    #   删除置信度小于conf_thres的BOX
    # -------------------------------------------------------
    org_box = np.squeeze(org_box)
    conf = org_box[..., 4] > conf_thres
    box = org_box[conf == True]
    # -------------------------------------------------------
    #   通过argmax获取置信度最大的类别
    # -------------------------------------------------------
    cls_cinf = box[..., 5:]
    cls = []
    for i in range(len(cls_cinf)):
        cls.append(int(np.argmax(cls_cinf[i])))
    all_cls = list(set(cls))
    # -------------------------------------------------------
    #   分别对每个类别进行过滤
    #   1.将第6列元素替换为类别下标
    #   2.xywh2xyxy 坐标转换
    #   3.经过非极大抑制后输出的BOX下标
    #   4.利用下标取出非极大抑制后的BOX
    # -------------------------------------------------------
    output = []
 
 
    for i in range(len(all_cls)):
        curr_cls = all_cls[i]
        curr_cls_box = []
        curr_out_box = []
        for j in range(len(cls)):
            if cls[j] == curr_cls:
                box[j][5] = curr_cls
                curr_cls_box.append(box[j][:6])
        curr_cls_box = np.array(curr_cls_box)
        # curr_cls_box_old = np.copy(curr_cls_box)
        curr_cls_box = xywh2xyxy(curr_cls_box)
        curr_out_box = nms(curr_cls_box, iou_thres)
        for k in curr_out_box:
            output.append(curr_cls_box[k])
    output = np.array(output)
    return output
 
 
def draw(image, box_data):
    # -------------------------------------------------------
    #   取整，方便画框
    # -------------------------------------------------------
    boxes = box_data[..., :4].astype(np.int32)
    scores = box_data[..., 4]
    classes = box_data[..., 5].astype(np.int32)
 
    img_height_o=image.shape[0]
    img_width_o=image.shape[1]
    x_ratio=img_width_o/640
    y_ratio=img_height_o/640
 
    for box, score, cl in zip(boxes, scores, classes):
        top, left, right, bottom = box
        print('class: {}, score: {}'.format(CLASSES[cl], score))
        print('box coordinate left,top,right,down: [{}, {}, {}, {}]'.format(top, left, right, bottom))
 
        top=int(top*x_ratio)
        right=int(right*x_ratio)
        left=int(left*y_ratio)
        bottom=int(bottom*y_ratio)
 
        cv2.rectangle(image, (top, left), (right, bottom), (255, 0, 0), 2)
        cv2.putText(image, '{0} {1:.2f}'.format(CLASSES[cl], score),
                    (top, left),
                    cv2.FONT_HERSHEY_SIMPLEX,
                    0.6, (0, 0, 255), 2)
 
 
if __name__ == "__main__":
    onnx_path = 'yolov5s.onnx'
    model = YOLOV5(onnx_path)  #模型的输出85组成：x、y、w、h、前景得分、80个类别
    output, or_img = model.inference('zidane.jpg')
    outbox = filter_box(output, 0.5, 0.5)
    draw(or_img, outbox)
    cv2.imwrite('res.jpg', or_img)