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yolov5返回坐標的方法實例

更新時間：2022年03月17日 11:19:20 作者：weixin_44726793

這篇文章主要給大家介紹了關(guān)于yolov5返回坐標的相關(guān)資料,文中通過實例代碼介紹的非常詳細,對大家的學(xué)習或者工作具有一定的參考學(xué)習價值,需要的朋友可以參考下

yolov5返回坐標（v6版）

1 、從yolov5文件夾李找到detect.py，按Ctrl+F 輸入annotator.box_label；

if save_img or save_crop or view_img:  # Add bbox to image
                        c = int(cls)  # integer class
                        label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
                        annotator.box_label(xyxy, label, color=colors(c, True))

2、找到這個代碼后按住ctrl鍵，鼠標點擊box_label，就會跳到plots.py文件并定位到box_label定義的地方；

3、找到p1, p2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3]))，在這行代碼下面新增：

print("左上點的坐標為：(" + str(p1[0]) + "," + str(p1[1]) + ")，右下點的坐標為(" + str(p2[0]) + "," + str(p2[1]) + ")")

4、完成后的代碼如下：

def box_label(self, box, label='', color=(128, 128, 128), txt_color=(255, 255, 255)):
        # Add one xyxy box to image with label
        if self.pil or not is_ascii(label):
            self.draw.rectangle(box, width=self.lw, outline=color)  # box
            if label:
                w, h = self.font.getsize(label)  # text width, height
                outside = box[1] - h >= 0  # label fits outside box
                self.draw.rectangle([box[0],
                                     box[1] - h if outside else box[1],
                                     box[0] + w + 1,
                                     box[1] + 1 if outside else box[1] + h + 1], fill=color)
                # self.draw.text((box[0], box[1]), label, fill=txt_color, font=self.font, anchor='ls')  # for PIL>8.0
                self.draw.text((box[0], box[1] - h if outside else box[1]), label, fill=txt_color, font=self.font)
        else:  # cv2
            p1, p2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3]))
            print("左上點的坐標為：(" + str(p1[0]) + "," + str(p1[1]) + ")，右下點的坐標為(" + str(p2[0]) + "," + str(p2[1]) + ")")
            
            cv2.rectangle(self.im, p1, p2, color, thickness=self.lw, lineType=cv2.LINE_AA)

5、測試情況：回到命令行，cd到y(tǒng)olov5文件夾，輸入指令：python detect.py --source ../mask.1.jpg，其中mask.1.jpg應(yīng)改為你yolov5文件夾下的圖片名稱；按回車鍵后運行就發(fā)現(xiàn)輸出的信息多了剛才添加的一行

(venv) (base) rongxiao@rongxiao:~/PycharmProjects/yolococo/yolov5$ python detect.py --source ../mask.1.jpg
detect: weights=yolov5s.pt, source=../mask.1.jpg, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs/detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False
YOLOv5 ?? v6.0-147-g628817d torch 1.8.2+cpu CPU

Fusing layers... 
Model Summary: 213 layers, 7225885 parameters, 0 gradients
左上點的坐標為：(982,384)，右下點的坐標為(1445,767)
左上點的坐標為：(724,237)，右下點的坐標為(770,277)
左上點的坐標為：(711,226)，右下點的坐標為(1689,938)
image 1/1 /home/rongxiao/PycharmProjects/yolococo/mask.1.jpg: 384x640 2 persons, 1 airplane, Done. (0.182s)
Speed: 1.1ms pre-process, 181.7ms inference, 1.0ms NMS per image at shape (1, 3, 640, 640)
Results saved to runs/detect/exp15

附參考：yolov5輸出檢測到的目標坐標信息（舊版本）

找到detect.py，在大概113行，找到plot_one_box

? ? ? ? ? ? ? ? # Write results
? ? ? ? ? ? ? ? for *xyxy, conf, cls in reversed(det):
? ? ? ? ? ? ? ? ? ? if save_txt: ?# Write to file
? ? ? ? ? ? ? ? ? ? ? ? xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() ?# normalized xywh
? ? ? ? ? ? ? ? ? ? ? ? with open(txt_path + '.txt', 'a') as f:
? ? ? ? ? ? ? ? ? ? ? ? ? ? f.write(('%g ' * 5 + '\n') % (cls, *xywh)) ?# label format

? ? ? ? ? ? ? ? ? ? if save_img or view_img: ?# Add bbox to image
? ? ? ? ? ? ? ? ? ? ? ? label = '%s %.2f' % (names[int(cls)], conf)
? ? ? ? ? ? ? ? ? ? ? ? plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3)

ctr+鼠標點擊，進入general.py，并自動定位到plot_one_box函數(shù)，修改函數(shù)為

def plot_one_box(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)
    print("左上點的坐標為：(" + str(c1[0]) + "," + str(c1[1]) + ")，右下點的坐標為(" + str(c2[0]) + "," + str(c2[1]) + ")")

即可輸出目標坐標信息了