ReDet
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Published
20 hours ago •
csuhan
csuhan
作者你好，请问文章中的这个特征图是怎么绘制的呢，文章中有相关代码吗，谢谢啦
This script may help you.
from mmdet.models import build_detector
from mmcv.runner import load_checkpoint
import numpy as np
from mmdet.datasets import to_tensor
from mmdet.datasets.transforms import ImageTransform
import mmcv
import argparse
import ipdb
import cv2
import os
import os.path as osp
def rotate_bound(image, angle):
(h, w) = image.shape[:2]
(cX, cY) = (w // 2, h // 2)
M = cv2.getRotationMatrix2D((cX, cY), -angle, 1.0)
cos = np.abs(M[0, 0])
sin = np.abs(M[0, 1])
nW = int((h * sin) + (w * cos))
nH = int((h * cos) + (w * sin))
M[0, 2] += (nW / 2) - cX
M[1, 2] += (nH / 2) - cY
return cv2.warpAffine(image, M, (nW, nH))
def img2feature(model, img, cfg):
img_trans = ImageTransform(cfg.img_norm_cfg['mean'], cfg.img_norm_cfg['std'], size_divisor=32, to_rgb=True)
img = to_tensor(img_trans(img, scale=1.0)[0]).unsqueeze(0).cuda()
ys = model.extract_feat(img)
ys_out = []
for y in ys:
y = y.detach().cpu().numpy().reshape(y.size(-2), y.size(-1))
ys_out.append(y)
return ys_out
def visualize(img_path, img_name, cfg_path, checkpoint_path, out_path, angles=[0, 90, 180, 270]):
cfg = mmcv.Config.fromfile(cfg_path)
model = build_detector(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg)
checkpoint = load_checkpoint(model, checkpoint_path, map_location='cpu')
model.eval()
model = model.cuda()
img = mmcv.imread(img_path)
img = mmcv.imresize(img,(2048,2048))
for angle in angles:
# rot image
img = rotate_bound(img, angle)
ys_img = img2feature(model, img, cfg)
for i,y_img in enumerate(ys_img):
y_img = np.uint8((y_img - y_img.min()) / (y_img.max() - y_img.min()) * 255)
y_img = cv2.applyColorMap(y_img, cv2.COLORMAP_JET)
img_path = osp.join(out_path, img_name + '-angle' + str(angle) + '-group' + str(i) + '.png')
mmcv.imwrite(y_img, img_path)
def parse_arg():
parser = argparse.ArgumentParser()
parser.add_argument('--cfg_path',
default="experiments/dota15/visualize/faster_rcnn_RoITrans_er50_fpn_1x_dota1_5_ms_visualize.py")
parser.add_argument('--checkpoint_path',
default="work_dirs/faster_rcnn_RoITrans_er50_fpn_1x_dota1_5_gap512_msTrainTest_rotationTrainTest/latest.pth")
parser.add_argument('--img_dir', default='demo/vis_supp_img')
parser.add_argument('--out_dir', default='demo_results/vis_supp')
return parser.parse_args()
if __name__ == '__main__':
args = parse_arg()
img_dir = args.img_dir
img_names = os.listdir(img_dir)
img_paths = [osp.join(img_dir, img_name) for img_name in img_names]
img_base_names = [osp.basename(img_name) for img_name in img_names]
for img_path, img_name in zip(img_paths, img_base_names):
out_dir = osp.join(args.out_dir,img_name)
if not osp.exists(out_dir):
os.mkdir(out_dir)
visualize(img_path, img_name, args.cfg_path, args.checkpoint_path, out_dir, angles=[0,90,180,270])
This script may help you.
from mmdet.models import build_detector from mmcv.runner import load_checkpoint import numpy as np from mmdet.datasets import to_tensor from mmdet.datasets.transforms import ImageTransform import mmcv import argparse import ipdb import cv2 import os import os.path as osp def rotate_bound(image, angle): (h, w) = image.shape[:2] (cX, cY) = (w // 2, h // 2) M = cv2.getRotationMatrix2D((cX, cY), -angle, 1.0) cos = np.abs(M[0, 0]) sin = np.abs(M[0, 1]) nW = int((h * sin) + (w * cos)) nH = int((h * cos) + (w * sin)) M[0, 2] += (nW / 2) - cX M[1, 2] += (nH / 2) - cY return cv2.warpAffine(image, M, (nW, nH)) def img2feature(model, img, cfg): img_trans = ImageTransform(cfg.img_norm_cfg['mean'], cfg.img_norm_cfg['std'], size_divisor=32, to_rgb=True) img = to_tensor(img_trans(img, scale=1.0)[0]).unsqueeze(0).cuda() ys = model.extract_feat(img) ys_out = [] for y in ys: y = y.detach().cpu().numpy().reshape(y.size(-2), y.size(-1)) ys_out.append(y) return ys_out def visualize(img_path, img_name, cfg_path, checkpoint_path, out_path, angles=[0, 90, 180, 270]): cfg = mmcv.Config.fromfile(cfg_path) model = build_detector(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg) checkpoint = load_checkpoint(model, checkpoint_path, map_location='cpu') model.eval() model = model.cuda() img = mmcv.imread(img_path) img = mmcv.imresize(img,(2048,2048)) for angle in angles: # rot image img = rotate_bound(img, angle) ys_img = img2feature(model, img, cfg) for i,y_img in enumerate(ys_img): y_img = np.uint8((y_img - y_img.min()) / (y_img.max() - y_img.min()) * 255) y_img = cv2.applyColorMap(y_img, cv2.COLORMAP_JET) img_path = osp.join(out_path, img_name + '-angle' + str(angle) + '-group' + str(i) + '.png') mmcv.imwrite(y_img, img_path) def parse_arg(): parser = argparse.ArgumentParser() parser.add_argument('--cfg_path', default="experiments/dota15/visualize/faster_rcnn_RoITrans_er50_fpn_1x_dota1_5_ms_visualize.py") parser.add_argument('--checkpoint_path', default="work_dirs/faster_rcnn_RoITrans_er50_fpn_1x_dota1_5_gap512_msTrainTest_rotationTrainTest/latest.pth") parser.add_argument('--img_dir', default='demo/vis_supp_img') parser.add_argument('--out_dir', default='demo_results/vis_supp') return parser.parse_args() if __name__ == '__main__': args = parse_arg() img_dir = args.img_dir img_names = os.listdir(img_dir) img_paths = [osp.join(img_dir, img_name) for img_name in img_names] img_base_names = [osp.basename(img_name) for img_name in img_names] for img_path, img_name in zip(img_paths, img_base_names): out_dir = osp.join(args.out_dir,img_name) if not osp.exists(out_dir): os.mkdir(out_dir) visualize(img_path, img_name, args.cfg_path, args.checkpoint_path, out_dir, angles=[0,90,180,270])
谢谢你，请问你文章中的特征图是哪个layer提取出来的呢，如果提取fpn那部分的特征图，是直接对各个通道相加吗，还是需要对通道怎么操作呢，第一张图是提取的layer1的，第二张是fpn的一张，第一张看起来应该可以，我是直接将各个通道相加的，第二张fpn看起来好香有点问题，不知道正不正常，麻烦你帮忙解答一下吧，谢谢啦
缩放至相同尺寸后相加
你好，这个缩放至相同尺寸是指什么呢，同一层特征图各个通道的尺寸都是一样的啊，还有下面这个提取来的特征如果直接resize会报错误
麻烦你帮忙看一下吧
缩放至相同尺寸后相加
你好，这个缩放至相同尺寸是指什么呢，同一层特征图各个通道的尺寸都是一样的啊，还有下面这个提取来的特征如果直接resize会报错误
你好，请问您问题解决没 我遇到了跟您一样的问题
from mmdet.datasets import to_tensor from mmdet.datasets.transforms import ImageTransform
importError
