1.修改填充框
在mmdet/core/visulization/image.py下面，139行开始，写文字和填充框的代码，把填充框那部分注释掉
2.修改检测框字体大小、颜色
找到mmdet/models/detectors/base.py文件，修改class BaseDetector()中的show_result()函数的输入参数
def show_result(self,
img,
result,
score_thr=0.3,
bbox_color='green', # 检测框的颜色
text_color='green', # 字体的颜色
thickness=2, # 检测框的粗细
font_size=1.5, # 字体大小
win_name='',
show=False,
wait_time=0,
out_file=None):
3.修改字体的粗细程度
找到文件anaconda3/envs/open-mmlab/lib/python3.7/site-packages/mmcv/visualization/image.py。在mmcv.imshow_det_bboxes()函数调用cv2.putText函数将类别文字标注到图上，加上改变字体粗细的参数。
cv2.putText(img,
label_text,
(bbox_int[0], bbox_int[1] - 2),
cv2.FONT_HERSHEY_COMPLEX,
font_scale,
text_color,
3 #加入改变字体粗细的参数)
4.设置跟随图片分辨率自动设置字体大小的
我们可以根据输入图片的尺寸来给thickness和font_scale赋值，字体大小为np.floor(5e-4 * np.shape(img)[1] + 0.5)。检测框的粗细设置为max((np.shape(img)[0] + np.shape(img)[1]) // 1080, 1)。
def show_result(self,
img,
result,
score_thr=0.3,
bbox_color='green',
text_color='green',
#thickness=3,
#font_scale=0.8,
win_name='',
show=False,
wait_time=0,
out_file=None):
img = mmcv.imread(img)
font_scale=np.floor(5e-4 * np.shape(img)[1] + 0.5)
thickness=thickness = max((np.shape(img)[0] + np.shape(img)[1]) // 1080, 1)
img = img.copy()
if isinstance(result, tuple):
bbox_result, segm_result = result
if isinstance(segm_result, tuple):
segm_result = segm_result[0] # ms rcnn
else:
bbox_result, segm_result = result, None
bboxes = np.vstack(bbox_result)
labels = [
np.full(bbox.shape[0], i, dtype=np.int32)
for i, bbox in enumerate(bbox_result)
]
labels = np.concatenate(labels)
# draw segmentation masks
if segm_result is not None and len(labels) > 0: # non empty
segms = mmcv.concat_list(segm_result)
inds = np.where(bboxes[:, -1] > score_thr)[0]
np.random.seed(42)
color_masks = [
np.random.randint(0, 256, (1, 3), dtype=np.uint8)
for _ in range(max(labels) + 1)
]
for i in inds:
i = int(i)
color_mask = color_masks[labels[i]]
sg = segms[i]
if isinstance(sg, torch.Tensor):
sg = sg.detach().cpu().numpy()
mask = sg.astype(bool)
img[mask] = img[mask] * 0.5 + color_mask * 0.5
# if out_file specified, do not show image in window
if out_file is not None:
show = False
# draw bounding boxes
mmcv.imshow_det_bboxes(
img,
bboxes,
labels,
class_names=self.CLASSES,
score_thr=score_thr,
bbox_color=bbox_color,
text_color=text_color,
thickness=thickness,
font_scale=font_scale,
win_name=win_name,
show=show,
wait_time=wait_time,
out_file=out_file)
if not (show or out_file):
return img
5.更加详细的解说可以看大佬的博客
MMDetection V2.0 可视化参数修改_威风凛凛小程序员的博客-CSDN博客MMDetection V2.0 可视化参数修改改变检测框的颜色、粗细、文字大小改变文字粗细修改效果将MMDetection的检测结果在SAR图上可视化时，发现字体太小、太细，看不清。修改MMDtection中一些参数可以改变检测框的颜色、粗细、字体大小、字体粗细。改变检测框的颜色、粗细、文字大小找到mmdet/models/detectors/base.py文件，修改class BaseDetector()中的show_result()函数的输入参数 def show_result(selfhttps://blog.csdn.net/i013140225/article/details/109819366?utm_medium=distribute.pc_aggpage_search_result.none-task-blog-2~aggregatepage~first_rank_ecpm_v1~rank_v31_ecpm-1-109819366.pc_agg_new_rank&utm_term=mmdetection%E4%B8%AD%E6%A0%87%E6%B3%A8%E5%AD%97%E4%BD%93%E7%9A%84%E5%A4%A7%E5%B0%8F%E6%80%8E%E4%B9%88%E8%B0%83%E6%95%B4&spm=1000.2123.3001.4430
目标检测修改检测框大小、粗细_天涯小才的博客-CSDN博客_怎么调整目标检测框的大小https://blog.csdn.net/qq_40502460/article/details/117319923
