image_file))# Generate colors for drawing bounding boxes.colors=generate_colors(class_names)# Draw bounding boxes on the image filedraw_boxes(image,out_scores,out_boxes,out_classes,class_names,colors)# Save the predicted bounding box on the imageimage.save(os.path.join("out...
results=model(frame) # Draw bounding boxes forresultinresults: boxes=result['boxes'] forboxinboxes: x1,y1,x2,y2=box['coords'] label=box['label'] confidence=box['confidence'] cv2.rectangle(frame, (x1,y1), (x2,y2), (0,255,0),2) cv2.putText(frame,f'{label}{confidence:.2f}',...
cx=int(x3+x4)// 2cy=int(y3+y4)// 2# cv2.circle(frame,(cx,cy),4,(0,0,255),-1)#draw ceter pointsofbounding box # cv2.rectangle(frame,(x3,y3),(x4,y4),(0,255,0),2)# Draw bounding box # cv2.putText(frame,str(id),(cx,cy),cv2.FONT_HERSHEY_COMPLEX,0.8,(0,255,255),...
Returns: Image with bounding boxes. """ for detection in detection_result.detections: # Draw bounding_box bbox = detection.bounding_box start_point = bbox.origin_x, bbox.origin_y end_point = bbox.origin_x + bbox.width, bbox.origin_y + bbox.height cv2.rectangle(image, start_point...
# Draw bounding boxes for result in results: boxes = result['boxes'] for box in boxes: x1, y1, x2, y2 = box['coords'] label = box['label'] confidence = box['confidence'] cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2) ...
cv2.putText(org, str(confidence), (start_x, start_y), font, font_scale, (0, 0, 0), thickness)#Run non-max suppression algorithmpicked_boxes, picked_score =nms(bounding_boxes, confidence_score, threshold)#Draw bounding boxes and confidence score after non-maximum supressionfor(start_x, ...
# draw bounding boxes on the image using labels draw_boxes(image, boxes, labels, obj_thresh) # write the image with bounding boxes to file cv2.imwrite(image_path[:-4] + '_detected' + image_path[-4:], (image).astype('uint8')) ...
# Generate colors for drawing bounding boxes. colors = generate_colors(class_names) # Draw bounding boxes on the image file draw_boxes(image, out_scores, out_boxes, out_classes, class_names, colors) # Save the predicted bounding box on the image ...
importmatplotlib.pyplotaspltfrommatplotlib.imageimportimreadimportmatplotlib.patchesaspatchesimportmatplotlib.axes#定义一个绘制矩形框的函数#输入参数依此为:绘制的目标图像、边界框的坐标以及框的颜色defdraw_rect(pic,bbox,color): box=patches.Rectangle((bbox[0],bbox[1]),bbox[2]-bbox[0],bbox[3]-bbo...
YOLO v3将输入图像分成S*S个格子,每个格子预测B个bounding box,每个boundingbox预测内容包括: Location(x, y, w, h)、Confidence Score和C个类别的概率,因此YOLO v3输出层的channel数为S*S*B*(5+ C)。YOLO v3的loss函数也有三部分组成:Location误差,Confidence误差和分类误差。 图:YOLO v3检测原理 YOLO v3...