def preprocess_example_input(input_config):
"""Prepare an example input image for ``generate_inputs_and_wrap_model``.
Args:
input_config (dict): customized config describing the example input.
Returns:
tuple: (one_img, one_meta), tensor of the example input image and \
meta information for the example input image.
Examples:
>>> from mmdet.core.export import preprocess_example_input
>>> input_config = {
>>> 'input_shape': (1,3,224,224),
>>> 'input_path': 'demo/demo.jpg',
>>> 'normalize_cfg': {
>>> 'mean': (123.675, 116.28, 103.53),
>>> 'std': (58.395, 57.12, 57.375)
>>> }
>>> }
>>> one_img, one_meta = preprocess_example_input(input_config)
>>> print(one_img.shape)
torch.Size([1, 3, 224, 224])
>>> print(one_meta)
{'img_shape': (224, 224, 3),
'ori_shape': (224, 224, 3),
'pad_shape': (224, 224, 3),
'filename': '<demo>.png',
'scale_factor': 1.0,
'flip': False}
"""
input_path = input_config['input_path']
input_shape = input_config['input_shape']
one_img = mmcv.imread(input_path)
if 'normalize_cfg' in input_config.keys():
normalize_cfg = input_config['normalize_cfg']
mean = np.array(normalize_cfg['mean'], dtype=np.float32)
std = np.array(normalize_cfg['std'], dtype=np.float32)
one_img = mmcv.imnormalize(one_img, mean, std)
one_img = mmcv.imresize(one_img, input_shape[2:][::-1]).transpose(2, 0, 1)
one_img = torch.from_numpy(one_img).unsqueeze(0).float().requires_grad_(
True)
(_, C, H, W) = input_shape
one_meta = {
'img_shape': (H, W, C),
'ori_shape': (H, W, C),
'pad_shape': (H, W, C),
'filename': '<demo>.png',
'scale_factor': 1.0,
'flip': False
}
return one_img, one_meta
def __call__(self, results):
norm_cfgs = {}
for key in filter(
lambda x: x.startswith('img') and isinstance(
results[x], np.ndarray), results.keys()):
results[key] = mmcv.imnormalize(results[key], self.mean, self.std,
self.to_rgb)
norm_cfgs[f'{key}_norm_cfg'] = dict(mean=self.mean,
std=self.std,
to_rgb=self.to_rgb)
results.update(norm_cfgs)
return results
def __call__(self, results):
"""Call function to normalize images.
Args:
results (dict): Result dict from loading pipeline.
Returns:
dict: Normalized results, 'img_norm_cfg' key is added into
result dict.
"""
results['img'] = mmcv.imnormalize(results['img'], self.mean, self.std,
self.to_rgb)
results['sequence_imgs'] = [
mmcv.imnormalize(img, self.mean, self.std, self.to_rgb)
for img in results['sequence_imgs']
]
results['img_norm_cfg'] = dict(mean=self.mean,
std=self.std,
to_rgb=self.to_rgb)
return results
def __call__(self, img, scale, flip=False):
img, scale_factor = mmcv.imrescale(img, scale, return_scale=True)
img_shape = img.shape
img = mmcv.imnormalize(img, self.mean, self.std, self.to_rgb)
if flip:
img = mmcv.imflip(img)
if self.size_divisor is not None:
img = mmcv.impad_to_multiple(img, self.size_divisor)
pad_shape = img.shape
else:
pad_shape = img_shape
img = img.transpose(2, 0, 1)
return img, img_shape, pad_shape, scale_factor
def __call__(self, results):
"""Call function.
Args:
results (dict): A dict containing the necessary information and
data for augmentation.
Returns:
dict: A dict containing the processed data and information.
"""
for key in self.keys:
if isinstance(results[key], list):
results[key] = [
mmcv.imnormalize(v, self.mean, self.std, self.to_rgb)
for v in results[key]
]
else:
results[key] = mmcv.imnormalize(results[key], self.mean,
self.std, self.to_rgb)
results['img_norm_cfg'] = dict(
mean=self.mean, std=self.std, to_rgb=self.to_rgb)
return results
def __call__(self, results):
"""Call function to normalize images.
Args:
results (dict): Result dict from loading pipeline.
Returns:
dict: Normalized results, 'img_norm_cfg' key is added into
result dict.
"""
if 'img' in results:
results['img'] = mmcv.imnormalize(results['img'], self.mean,
self.std, self.to_rgb)
if 'img2' in results:
results['img1'] = mmcv.imnormalize(results['img1'], self.mean,
self.std, self.to_rgb)
results['img2'] = mmcv.imnormalize(results['img2'], self.mean,
self.std, self.to_rgb)
# sys.exit('ppppp')
results['img_norm_cfg'] = dict(mean=self.mean,
std=self.std,
to_rgb=self.to_rgb)
return results
def __call__(self, img, scale, flip=False, keep_ratio=True, hsv_h=0, hsv_s=0, hsv_v=0, noisy_mode=None, blur_mode=None):
# Augment colorspace
if hsv_h+hsv_s+hsv_v > 5:
# SV augmentation by 50%
img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) # hue, sat, val
H = img_hsv[:, :, 0].astype(np.float32) # hue
S = img_hsv[:, :, 1].astype(np.float32) # saturation
V = img_hsv[:, :, 2].astype(np.float32) # value
a = random.uniform(-1, 1) * hsv_h + 1
b = random.uniform(-1, 1) * hsv_s + 1
c = random.uniform(-1, 1) * hsv_v + 1
H *= a
S *= b
V *= c
img_hsv[:, :, 0] = H if a < 1 else H.clip(None, 255)
img_hsv[:, :, 1] = S if b < 1 else S.clip(None, 255)
img_hsv[:, :, 2] = V if c < 1 else V.clip(None, 255)
cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=img)
# Add noise
if noisy_mode is not None:
img = self.add_noise(img, noisy_mode)
# Blur
if blur_mode is not None:
img = self.opencv_blur(img, blur_mode)
if keep_ratio:
img, scale_factor = mmcv.imrescale(img, scale, return_scale=True)
else:
img, w_scale, h_scale = mmcv.imresize(
img, scale, return_scale=True)
scale_factor = np.array([w_scale, h_scale, w_scale, h_scale],
dtype=np.float32)
img_shape = img.shape
img = mmcv.imnormalize(img, self.mean, self.std, self.to_rgb)
if flip:
img = mmcv.imflip(img)
if self.size_divisor is not None:
img = mmcv.impad_to_multiple(img, self.size_divisor)
pad_shape = img.shape
else:
pad_shape = img_shape
img = img.transpose(2, 0, 1)
return img, img_shape, pad_shape, scale_factor
def _imrenormalize(img, img_norm_cfg, new_img_norm_cfg):
"""Re-normalize the image."""
img_norm_cfg = img_norm_cfg.copy()
new_img_norm_cfg = new_img_norm_cfg.copy()
for k, v in img_norm_cfg.items():
if (k == 'mean' or k == 'std') and not isinstance(v, np.ndarray):
img_norm_cfg[k] = np.array(v, dtype=img.dtype)
# reverse cfg
if 'to_rgb' in img_norm_cfg:
img_norm_cfg['to_bgr'] = img_norm_cfg['to_rgb']
img_norm_cfg.pop('to_rgb')
for k, v in new_img_norm_cfg.items():
if (k == 'mean' or k == 'std') and not isinstance(v, np.ndarray):
new_img_norm_cfg[k] = np.array(v, dtype=img.dtype)
img = mmcv.imdenormalize(img, **img_norm_cfg)
img = mmcv.imnormalize(img, **new_img_norm_cfg)
return img
def __call__(self, results):
"""Call function to normalize images.
Args:
results (dict): Result dict from loading pipeline.
Returns:
dict: Normalized results, 'img_norm_cfg' key is added into
result dict.
"""
for key in results.get('img_fields', ['img']):
results[key] = mmcv.imnormalize(results[key], self.mean, self.std,
self.to_rgb)
results['img_norm_cfg'] = dict(
mean=self.mean, std=self.std, to_rgb=self.to_rgb)
return results
def __call__(self, img, scale, flip=False, keep_ratio=True):
# 1. rescale/resize the image to expected size
if keep_ratio:
# Resize image while keeping the aspect ratio.
# The image will be rescaled as large as possible within the scale.
img, scale_factor = mmcv.imrescale(
img=img,
scale=scale,
return_scale=True,
interpolation='bilinear',
)
else:
# Resize image to a given size ignoring the aspect ratio.
img, w_scale, h_scale = mmcv.imresize(
img=img,
size=scale,
return_scale=True,
interpolation='bilinear',
)
scale_factor = np.array(
[w_scale, h_scale, w_scale, h_scale], dtype=np.float32
)
# 2. normalize the image
img_shape = img.shape
img = mmcv.imnormalize(img, self.mean, self.std, self.to_rgb)
# 3. flip the image (if needed)
if flip:
img = mmcv.imflip(img)
# 4. pad the image if size_divisor is not None.
# size_divisor=32 means sizes are multiplier of 32.
if self.size_divisor is not None:
img = mmcv.impad_to_multiple(img, self.size_divisor)
pad_shape = img.shape
else:
pad_shape = img_shape
# 5. transpose to (c, h, w)
img = img.transpose(2, 0, 1)
return img, img_shape, pad_shape, scale_factor
def __call__(self,
img,
scale,
flip=False,
crop_info=None,
keep_ratio=True):
# image jittering
try:
img = Image.fromarray(img)
except:
print(img)
if hasattr(self, 'random_color'):
img = self.random_color(img)
if hasattr(self, 'random_contrast'):
img = self.random_contrast(img)
if hasattr(self, 'random_sharpness'):
img = self.random_sharpness(img)
img = np.array(img)
if keep_ratio:
img, scale_factor = mmcv.imrescale(img, scale, return_scale=True)
else:
img, w_scale, h_scale = mmcv.imresize(img,
scale,
return_scale=True)
scale_factor = np.array([w_scale, h_scale, w_scale, h_scale],
dtype=np.float32)
img_shape = img.shape
img = mmcv.imnormalize(img, self.mean, self.std, self.to_rgb)
if flip:
img = mmcv.imflip(img)
if crop_info is not None:
# if crop, no need to pad
cx1, cy1, cx2, cy2 = crop_info
img = img[cy1:cy2, cx1:cx2]
pad_shape = img.shape
# pad and set pad_shape
if crop_info is None and self.size_divisor is not None:
img = mmcv.impad_to_multiple(img, self.size_divisor)
pad_shape = img.shape
else:
pad_shape = img_shape
img = img.transpose(2, 0, 1)
return img, img_shape, pad_shape, scale_factor
def __call__(self, img, scale, flip=False, keep_ratio=True):
if keep_ratio:
img, scale_factor = mmcv.imrescale(img, scale, return_scale=True)# scale 包括一个长边一个短边，顺序无所谓
else:# 做识别 不需要保持ratio不变，resize到 224
img, w_scale, h_scale = mmcv.imresize(
img, scale, return_scale=True)
scale_factor = np.array([w_scale, h_scale, w_scale, h_scale],
dtype=np.float32)
img_shape = img.shape
img = mmcv.imnormalize(img, self.mean, self.std, self.to_rgb)
if flip:
img = mmcv.imflip(img)
if self.size_divisor is not None:
img = mmcv.impad_to_multiple(img, self.size_divisor)# pad 边，为了每条边都能被divisor这个数字整除，比如 32
pad_shape = img.shape # pad后的图片形状
else:
pad_shape = img_shape
img = img.transpose(2, 0, 1)
return img, img_shape, pad_shape, scale_factor
def __call__(self, img, scale, flip=False, keep_ratio=True):
if keep_ratio:
img, scale_factor = mmcv.imrescale(img, scale, return_scale=True)
else:
img, w_scale, h_scale = mmcv.imresize(
img, scale, return_scale=True)
scale_factor = np.array(
[w_scale, h_scale, w_scale, h_scale], dtype=np.float32)
img_shape = img.shape
img = mmcv.imnormalize(img, self.mean, self.std, self.to_rgb) # (x - mean) / std , brg2rgb
if flip:
img = mmcv.imflip(img) # flip images
if self.size_divisor is not None:
img = mmcv.impad_to_multiple(img, self.size_divisor) # padding image to make sure divided by divisor.
pad_shape = img.shape
else:
pad_shape = img_shape
img = img.transpose(2, 0, 1) # HWC to CHW
return img, img_shape, pad_shape, scale_factor
def onnx_infer(session, input_path, shape=(896, 512)):
if isinstance(input_path, str):
raw_img = mmcv.imread(input_path)
else:
raw_img = input_path
ori_shape = raw_img.shape
one_img = mmcv.imresize(raw_img, shape)
mean = np.array((123.675, 116.28, 103.53), dtype=np.float32)
std = np.array((58.395, 57.12, 57.375), dtype=np.float32)
one_img = mmcv.imnormalize(one_img, mean, std, to_rgb=True)
one_img = np.expand_dims(one_img.transpose(2, 0, 1), axis=0)
result = session.run(None, {"input": one_img})
seg_preds = np.concatenate(result[:5], axis=1)[0]
cate_preds = [np.reshape(res, (res.shape[0], -1, res.shape[-1])) for res in result[5:]]
cate_preds = np.concatenate(cate_preds, axis=1)[0]
# print(seg_preds.shape, cate_preds.shape)
result = get_seg(seg_preds, cate_preds, ori_shape)
return result
def __call__(self, img, scale, flip=False, keep_ratio=True):
if keep_ratio:
img, scale_factor = mmcv.imrescale(img, scale, return_scale=True)
else:
img, w_scale, h_scale = mmcv.imresize(
img, scale, return_scale=True)
scale_factor = np.array([w_scale, h_scale, w_scale, h_scale],
dtype=np.float32)
img_shape = img.shape
img = mmcv.imnormalize(img, self.mean, self.std, self.to_rgb)
if flip:
img = mmcv.imflip(img)
if self.size_divisor is not None:
img = mmcv.impad_to_multiple(img, self.size_divisor)
pad_shape = img.shape
else:
pad_shape = img_shape
img = img.transpose(2, 0, 1)
return img, img_shape, pad_shape, scale_factor
def __call__(self, results):
els = ['img']
if 'ref_img' in results:
els += ['ref_img']
if 'flow' in results:
# 2 channel flow is raw no need to normalize used for warping solely
if results['flow'].shape[-1] == 3:
els += ['flow']
if 'depth' in results:
els += ['depth']
if 'ref_depth' in results:
els += ['ref_depth']
for el in els:
results[el] = mmcv.imnormalize(
results[el], self.mean, self.std, self.to_rgb)
results['img_norm_cfg'] = dict(
mean=self.mean, std=self.std, to_rgb=self.to_rgb)
return results
def preprocess_example_input(input_config):
"""Prepare an example input image for `generate_inputs_and_wrap_model`.
Args:
input_config (dict): customized config describing the example input.
Example:
input_config: {
'input_shape':[1,3,224,224],
'input_path': 'demo/demo.jpg',
'normalize_cfg': {
'mean': [123.675, 116.28, 103.53],
'std': [58.395, 57.12, 57.375]
}
}
Returns:
tuple: (one_img, one_meta), tensor of the example input image and meta
information for the example input image.
"""
input_path = input_config['input_path']
input_shape = input_config['input_shape']
one_img = mmcv.imread(input_path)
if 'normalize_cfg' in input_config.keys():
normalize_cfg = input_config['normalize_cfg']
mean = np.array(normalize_cfg['mean'], dtype=np.float32)
std = np.array(normalize_cfg['std'], dtype=np.float32)
one_img = mmcv.imnormalize(one_img, mean, std)
one_img = mmcv.imresize(one_img, input_shape[2:]).transpose(2, 0, 1)
one_img = torch.from_numpy(one_img).unsqueeze(0).float().requires_grad_(
True)
(_, C, H, W) = input_shape
one_meta = {
'img_shape': (H, W, C),
'ori_shape': (H, W, C),
'pad_shape': (H, W, C),
'filename': '<demo>.png',
'scale_factor': 1.0,
'flip': False
}
return one_img, one_meta
def single_call(self, results, img_ref):
if results['keep_ratio']:
img_ref = mmcv.imrescale(
img_ref, results['scale'], return_scale=False)
else:
img_ref = mmcv.imresize(
img_ref, results['scale'], return_scale=False)
if results['flip']:
img_ref = mmcv.imflip(img_ref)
if results['img_norm_cfg']:
img_norm_cfg = results['img_norm_cfg']
img_ref = mmcv.imnormalize(
img_ref, img_norm_cfg['mean'],
img_norm_cfg['std'],
img_norm_cfg['to_rgb'])
if 'crop_coords' in results:
crds = results['crop_coords']
img_ref = img_ref[crds[0]:crds[1], crds[2]:crds[3], :]
if img_ref.shape != results['pad_shape']:
img_ref = mmcv.impad(img_ref, results['pad_shape'][:2])
return img_ref
def get_img(img_meta):
img = cv2.imread(img_meta[0]['filename'])
# Resize
sf = img_meta[0]['scale_factor']
img, scale_factor = mmcv.imrescale(img, sf, True)
# Normalize
m = img_meta[0]['img_norm_cfg']['mean']
s = img_meta[0]['img_norm_cfg']['std']
t = img_meta[0]['img_norm_cfg']['to_rgb']
img = mmcv.imnormalize(img, m, s, t)
# Pad
sd = 32 # size_divisor
img = mmcv.impad_to_multiple(img, 32, 0)
# H x W x C -> C x H x W and expand an dim
img = torch.from_numpy(np.transpose(img, (2, 0, 1))).expand(1, -1, -1, -1)
return img
def processing_one_image(file_path):
img_meta = {}
img_meta['filename'] = file_path
img_meta['ori_filename'] = file_path
img_meta['flip'] = False
# 1. Read image
file_client = mmcv.FileClient(backend='disk')
img_bytes = file_client.get(file_path)
orig_img = mmcv.imfrombytes(img_bytes, flag='color') # BGR order
img_meta['ori_shape'] = orig_img.shape
# 2. Resize
test_scale = (1333, 800)
img, scale_factor = mmcv.imrescale(orig_img, test_scale, return_scale=True)
# the w_scale and h_scale has minor difference
# a real fix should be done in the mmcv.imrescale in the future
new_h, new_w = img.shape[:2]
h, w = orig_img.shape[:2]
w_scale = new_w / w
h_scale = new_h / h
scale_factor = np.array([w_scale, h_scale, w_scale, h_scale],
dtype=np.float32)
img_meta['scale_factor'] = scale_factor
img_meta['img_shape'] = img.shape
# 3. Normalize
# mean = np.array([102.9801, 115.9465, 122.7717], dtype=np.float32)
# std = np.array([1.0, 1.0, 1.0], dtype=np.float32)
mean = np.array([103.53, 116.28, 123.675], dtype=np.float32)
std = np.array([1.0, 1.0, 1.0], dtype=np.float32)
to_rgb = False
img = mmcv.imnormalize(img, mean, std, to_rgb)
img_meta['img_norm_cfg'] = dict(mean=mean, std=std, to_rgb=to_rgb)
# 4. Pad
img = mmcv.impad_to_multiple(img, divisor=32, pad_val=0)
img_meta["pad_shape"] = img.shape
# 5. ToTensor
img = torch.from_numpy(img.transpose(2, 0, 1))
return img, img_meta
def __call__(self, results):
"""
#for Debugging
img = Image.fromarray(results['img'].astype('uint8'), 'RGB')
#img = Image.new('L', (1024, 1024), 0)
for bx in results['gt_bboxes']:
bx = ((bx[0], bx[1]), (bx[2], bx[3]), bx[4])
#print(bx)
box = cv2.boxPoints(bx)
#print(box)
ImageDraw.Draw(img).polygon(box, outline=255)
print('/content/gdrive/My Drive/Arirang/data/image_test/' + results['img_info']['file_name'])
img.save('/content/gdrive/My Drive/Arirang/data/image_test/' + results['img_info']['file_name'])
self.count += 1
if self.count > 10:
print(data['img'])
"""
results['img'] = mmcv.imnormalize(results['img'], self.mean, self.std,
self.to_rgb)
results['img_norm_cfg'] = dict(mean=self.mean,
std=self.std,
to_rgb=self.to_rgb)
return results
def __call__(self, results):
results['img'] = mmcv.imnormalize(results['img'], self.mean, self.std,
self.to_rgb)
results['img_norm_cfg'] = dict(
mean=self.mean, std=self.std, to_rgb=self.to_rgb)
return results
def imnormalize_column(img, img_shape, gt_bboxes, gt_label, gt_num):
"""imnormalize operation for image"""
img_data = mmcv.imnormalize(img, [123.675, 116.28, 103.53],
[58.395, 57.12, 57.375], True)
img_data = img_data.astype(np.float32)
return (img_data, img_shape, gt_bboxes, gt_label, gt_num)
def pytorch2onnx(model,
input_img,
input_shape,
opset_version=11,
show=False,
output_file='tmp.onnx',
verify=False,
normalize_cfg=None):
model.cpu().eval()
# read image
one_img = mmcv.imread(input_img)
if normalize_cfg:
one_img = mmcv.imnormalize(one_img, normalize_cfg['mean'],
normalize_cfg['std'])
one_img = mmcv.imresize(one_img, input_shape[2:]).transpose(2, 0, 1)
one_img = torch.from_numpy(one_img).unsqueeze(0).float()
(_, C, H, W) = input_shape
one_meta = {
'img_shape': (H, W, C),
'ori_shape': (H, W, C),
'pad_shape': (H, W, C),
'filename': '<demo>.png',
'scale_factor': 1.0,
'flip': False
}
# onnx.export does not support kwargs
origin_forward = model.forward
model.forward = partial(model.forward,
img_metas=[[one_meta]],
return_loss=False)
# pytorch has some bug in pytorch1.3, we have to fix it
# by replacing these existing op
register_extra_symbolics(opset_version)
torch.onnx.export(model, ([one_img]),
output_file,
export_params=True,
keep_initializers_as_inputs=True,
verbose=show,
opset_version=opset_version)
model.forward = origin_forward
print(f'Successfully exported ONNX model: {output_file}')
if verify:
# check by onnx
onnx_model = onnx.load(output_file)
onnx.checker.check_model(onnx_model)
# check the numerical value
# get pytorch output
pytorch_result = model([one_img], [[one_meta]], return_loss=False)
# get onnx output
input_all = [node.name for node in onnx_model.graph.input]
input_initializer = [
node.name for node in onnx_model.graph.initializer
]
net_feed_input = list(set(input_all) - set(input_initializer))
assert (len(net_feed_input) == 1)
sess = rt.InferenceSession(output_file)
from mmdet.core import bbox2result
det_bboxes, det_labels = sess.run(
None, {net_feed_input[0]: one_img.detach().numpy()})
# only compare a part of result
bbox_results = bbox2result(det_bboxes, det_labels, 1)
onnx_results = bbox_results[0]
assert np.allclose(
pytorch_result[0][:, 4], onnx_results[:, 4]
), 'The outputs are different between Pytorch and ONNX'
print('The numerical values are same between Pytorch and ONNX')
def infer(show_score_thr=0.3):
args = parse_args()
model = get_model(args)
img_meta = {'filename': 'temp.jpg',
'ori_shape': (800, 800, 3),
'img_shape': (800, 800, 3),
'pad_shape': (800, 800, 3),
'scale_factor': np.array([1.000, 1.000, 1.000, 1.000]).astype(np.float32),
'flip': False,
'img_norm_cfg': {'mean': np.array([123.675, 116.28, 103.53]).astype(np.float32),
'std': np.array([58.395, 57.12, 57.375]).astype(np.float32),
'to_rgb': True}}
file_name_list = os.listdir(args.img_file_dir)
results = {}
ik = 0
for file_name in file_name_list:
if os.path.splitext(file_name)[1] not in ['.jpg', '.png', '.bmp', '.gif']:
continue
# ---patched image---
img_meta['filename'] = file_name
img = mmcv.imread(args.img_file_dir + file_name)
img = mmcv.imnormalize(img, img_meta['img_norm_cfg']['mean'], img_meta['img_norm_cfg']['std'])
img = torch.from_numpy(img).cuda()
img_shape = (img.size()[0], img.size()[1], img.size()[2])
img_meta['ori_shape'] = img_shape
img_meta['img_shape'] = img_shape
img_meta['pad_shape'] = img_shape
img = img.permute(2, 0, 1)
img_p = img.unsqueeze(0)
# ----clean image----
img_meta['filename'] = file_name
img_file_dir2 = args.img_file_dir.replace('_p', '')
img = mmcv.imread(img_file_dir2 + file_name)
img = mmcv.imnormalize(img, img_meta['img_norm_cfg']['mean'], img_meta['img_norm_cfg']['std'])
img = torch.from_numpy(img).cuda()
img_shape = (img.size()[0], img.size()[1], img.size()[2])
img_meta['ori_shape'] = img_shape
img_meta['img_shape'] = img_shape
img_meta['pad_shape'] = img_shape
img = img.permute(2, 0, 1)
img_c = img.unsqueeze(0)
# pdb.set_trace()
with torch.no_grad():
result_p = model(return_loss=False, rescale=True, img=[img_p], img_metas=[[img_meta]])
result_c = model(return_loss=False, rescale=True, img=[img_c], img_metas=[[img_meta]])
if isinstance(result_p, tuple):
bbox_results, mask_results = result_p
encoded_mask_results = encode_mask_results(mask_results)
result_p = bbox_results, encoded_mask_results
bbox_results, mask_results = result_c
encoded_mask_results = encode_mask_results(mask_results)
result_c = bbox_results, encoded_mask_results
# from mmdet.models.detectors.base import
# model.module.show_result(
# img=img_file_dir2 + file_name,
# # img_show=True,
# result=result_c,
# show=True,
#
# score_thr=show_score_thr)
result_above_confidence_num_p = 0
result_above_confidence_num_c = 0
result_p = np.concatenate(result_p)
result_c = np.concatenate(result_c)
for ir in range(len(result_p)):
if result_p[ir, 4] > show_score_thr:
result_above_confidence_num_p = result_above_confidence_num_p + 1
for ir in range(len(result_c)):
if result_c[ir, 4] > show_score_thr:
result_above_confidence_num_c = result_above_confidence_num_c + 1
# result_above_confidence = >show_score_thr
# results[file_name] = np.concatenate(result)
if result_above_confidence_num_c == 0: # can't find any object in clean img
bb_score = 0
print('i=',ik)
print(file_name)
ik += 1
else:
bb_score = 1 - min(result_above_confidence_num_c, result_above_confidence_num_p) / result_above_confidence_num_c
results[file_name] = bb_score
json_name = args.out_json_name
with open(json_name, 'w') as f_obj:
json.dump(results, f_obj)
return results
def pre_process_img(one_img, mean, std, to_RGB):
one_img = mmcv.imnormalize(one_img, mean, std, to_RGB)
one_img = one_img.transpose(2, 0, 1)
return one_img
def infer(show_score_thr=0.3):
args = parse_args()
# model = get_model(args)
model = init_detector(args.config, args.checkpoint, device='cuda:0')
img_meta = {
'filename': 'temp.jpg',
'ori_shape': (480, 640, 3),
'img_shape': (800, 1067, 3),
'pad_shape': (800, 1088, 3),
'scale_factor': np.array([1.000, 1.000, 1.000,
1.000]).astype(np.float32),
'flip': False,
'img_norm_cfg': {
'mean': np.array([123.675, 116.28, 103.53]).astype(np.float32),
'std': np.array([58.395, 57.12, 57.375]).astype(np.float32),
'to_rgb': True
}
}
file_name_list = os.listdir(args.img_file_dir)
results = {}
ik = 0
for file_name in file_name_list:
if os.path.splitext(file_name)[1] not in [
'.jpg', '.png', '.bmp', '.gif'
]:
continue
# ---patched image---
img_meta['filename'] = file_name
img = mmcv.imread(args.img_file_dir + file_name)
img = mmcv.imnormalize(img, img_meta['img_norm_cfg']['mean'],
img_meta['img_norm_cfg']['std'])
img = torch.from_numpy(img).cuda()
img_shape = (img.size()[0], img.size()[1], img.size()[2])
img_meta['ori_shape'] = img_shape
img_meta['img_shape'] = img_shape
img_meta['pad_shape'] = img_shape
img = img.permute(2, 0, 1)
img_p = img.unsqueeze(0)
# ----clean image----
img_meta['filename'] = file_name
img_file_dir2 = args.img_file_dir.replace('_p', '')
img = mmcv.imread(img_file_dir2 + file_name)
img = mmcv.imnormalize(img, img_meta['img_norm_cfg']['mean'],
img_meta['img_norm_cfg']['std'])
img = torch.from_numpy(img).cuda()
img_shape = (img.size()[0], img.size()[1], img.size()[2])
img_meta['ori_shape'] = img_shape
img_meta['img_shape'] = img_shape
img_meta['pad_shape'] = img_shape
img = img.permute(2, 0, 1)
img_c = img.unsqueeze(0)
result_p = inference_detector(model, args.img_file_dir + file_name)
result_c = inference_detector(model, img_file_dir2 + file_name)
if isinstance(result_p, tuple):
bbox_results, mask_results = result_p
encoded_mask_results = encode_mask_results(mask_results)
result_p = bbox_results, encoded_mask_results
bbox_results, mask_results = result_c
encoded_mask_results = encode_mask_results(mask_results)
result_c = bbox_results, encoded_mask_results
result_above_confidence_num_p = 0
result_above_confidence_num_c = 0
result_p = np.concatenate(result_p)
result_c = np.concatenate(result_c)
for ir in range(len(result_p)):
if result_p[ir, 4] > show_score_thr:
result_above_confidence_num_p = result_above_confidence_num_p + 1
for ir in range(len(result_c)):
if result_c[ir, 4] > show_score_thr:
result_above_confidence_num_c = result_above_confidence_num_c + 1
'''
# -----------------------orig
img_meta['filename'] = file_name
img = mmcv.imread(args.img_file_dir + file_name)
img = mmcv.imnormalize(img, img_meta['img_norm_cfg']['mean'], img_meta['img_norm_cfg']['std'])
img = torch.from_numpy(img).cuda()
img_shape = (img.size()[0], img.size()[1], img.size()[2])
img_meta['ori_shape'] = img_shape
img_meta['img_shape'] = img_shape
img_meta['pad_shape'] = img_shape
# -----------------------orig
result = inference_detector(model, args.img_file_dir + file_name)
img = model.show_result(args.img_file_dir + file_name, result, score_thr=0.3, show=False)
mmcv.imwrite(img, args.img_file_dir + file_name[:-4] + "_out.png")
results[file_name] = np.concatenate(result)'''
if result_above_confidence_num_c == 0: # can't find any object in clean img
bb_score = 0
print('i=', ik)
print(file_name)
ik += 1
else:
bb_score = 1 - min(
result_above_confidence_num_c,
result_above_confidence_num_p) / result_above_confidence_num_c
results[file_name] = bb_score
import json
json_name = args.out_json_name
with open(json_name, 'w') as f_obj:
json.dump(results, f_obj)
return results
def infer(show_score_thr=0.3):
args = parse_args()
model = get_model(args)
img_meta = {
'filename': 'temp.jpg',
'ori_shape': (800, 800, 3),
'img_shape': (800, 800, 3),
'pad_shape': (800, 800, 3),
'scale_factor': np.array([1.000, 1.000, 1.000,
1.000]).astype(np.float32),
'flip': False,
'img_norm_cfg': {
'mean': np.array([123.675, 116.28, 103.53]).astype(np.float32),
'std': np.array([58.395, 57.12, 57.375]).astype(np.float32),
'to_rgb': True
}
}
file_name_list = os.listdir(args.img_file_dir)
results = {}
ik = 0
for file_name in file_name_list:
if os.path.splitext(file_name)[1] not in [
'.jpg', '.png', '.bmp', '.gif'
]:
continue
# # ---patched image---
# img_meta['filename'] = file_name
# img = mmcv.imread(args.img_file_dir + file_name)
# img = mmcv.imnormalize(img, img_meta['img_norm_cfg']['mean'], img_meta['img_norm_cfg']['std'])
# img = torch.from_numpy(img).cuda()
# img_shape = (img.size()[0], img.size()[1], img.size()[2])
# img_meta['ori_shape'] = img_shape
# img_meta['img_shape'] = img_shape
# img_meta['pad_shape'] = img_shape
# img = img.permute(2, 0, 1)
# img_p = img.unsqueeze(0)
# ----clean image----
img_meta['filename'] = file_name
# img_file_dir2 = args.img_file_dir.replace('_p', '')
img = mmcv.imread(os.path.join(args.img_file_dir, file_name))
img = mmcv.imnormalize(img, img_meta['img_norm_cfg']['mean'],
img_meta['img_norm_cfg']['std'])
img = torch.from_numpy(img).cuda()
img_shape = (img.size()[0], img.size()[1], img.size()[2])
img_meta['ori_shape'] = img_shape
img_meta['img_shape'] = img_shape
img_meta['pad_shape'] = img_shape
img = img.permute(2, 0, 1)
img_c = img.unsqueeze(0)
# pdb.set_trace()
with torch.no_grad():
result_c = model(return_loss=False,
rescale=True,
img=[img_c],
img_metas=[[img_meta]])
if isinstance(result_c, tuple):
bbox_results, mask_results = result_c
encoded_mask_results = encode_mask_results(mask_results)
result_c = bbox_results, encoded_mask_results
result_above_confidence_num_c = 0
result_c = np.concatenate(result_c)
for ir in range(len(result_c)):
if result_c[ir, 4] > show_score_thr:
result_above_confidence_num_c = result_above_confidence_num_c + 1
# print(result_above_confidence_num_c)
if result_above_confidence_num_c == 0: # can't find any object in clean img
bb_score = 0
print('i=', ik)
print(file_name)
os.remove(os.path.join(args.img_file_dir, file_name))
ik += 1
if result_above_confidence_num_c == 1: # can't find any object in clean img
bb_score = 0
print('i=', ik)
print(file_name)
os.remove(os.path.join(args.img_file_dir, file_name))
ik += 1
if result_above_confidence_num_c == 2: # can't find any object in clean img
bb_score = 0
print('i=', ik)
print(file_name)
os.remove(os.path.join(args.img_file_dir, file_name))
ik += 1
return results
def __call__(self,
img_group,
scale,
crop_history=None,
flip=False,
keep_ratio=True,
div_255=False,
is_flow=False):
if self.resize_crop or self.rescale_crop:
img_group, crop_quadruple = self.op_crop(img_group)
img_shape = img_group[0].shape
scale_factor = None
else:
# 1. rescale
if keep_ratio:
tuple_list = [
mmcv.imrescale(img, scale, return_scale=True)
for img in img_group
]
img_group, scale_factors = list(zip(*tuple_list))
scale_factor = scale_factors[0]
else:
tuple_list = [
mmcv.imresize(img, scale, return_scale=True)
for img in img_group
]
img_group, w_scales, h_scales = list(zip(*tuple_list))
scale_factor = np.array(
[w_scales[0], h_scales[0], w_scales[0], h_scales[0]],
dtype=np.float32)
# 2. crop (if necessary)
if crop_history is not None:
self.op_crop = GroupCrop(crop_history)
if self.op_crop is not None:
img_group, crop_quadruple = self.op_crop(img_group,
is_flow=is_flow)
else:
crop_quadruple = None
img_shape = img_group[0].shape
# 3. flip
if flip:
img_group = [mmcv.imflip(img) for img in img_group]
if is_flow:
for i in range(0, len(img_group), 2):
img_group[i] = mmcv.iminvert(img_group[i])
# 4a. div_255
if div_255:
img_group = [
mmcv.imnormalize(img, 0, 255, False) for img in img_group
]
# 4. normalize
img_group = [
mmcv.imnormalize(img, self.mean, self.std, self.to_rgb)
for img in img_group
]
# 5. pad
if self.size_divisor is not None:
img_group = [
mmcv.impad_to_multiple(img, self.size_divisor)
for img in img_group
]
pad_shape = img_group[0].shape
else:
pad_shape = img_shape
if is_flow:
assert len(img_group[0].shape) == 2
img_group = [
np.stack((flow_x, flow_y), axis=2)
for flow_x, flow_y in zip(img_group[0::2], img_group[1::2])
]
# 6. transpose
img_group = [img.transpose(2, 0, 1) for img in img_group]
# Stack into numpy.array
img_group = np.stack(img_group, axis=0)
return img_group, img_shape, pad_shape, scale_factor, crop_quadruple
def __call__(self,
img_group,
scale,
crop_history=None,
flip=False,
keep_ratio=True,
div_255=False,
is_flow=False,
interpolation='bilinear',
normalize=True,
more_aug=False):
# 1. rescale
if keep_ratio:
tuple_list = [
mmcv.imrescale(img,
scale,
return_scale=True,
interpolation=interpolation)
for img in img_group
]
img_group, scale_factors = list(zip(*tuple_list))
scale_factor = scale_factors[0]
else:
tuple_list = [
mmcv.imresize(img,
scale,
return_scale=True,
interpolation=interpolation) for img in img_group
]
img_group, w_scales, h_scales = list(zip(*tuple_list))
scale_factor = np.array(
[w_scales[0], h_scales[0], w_scales[0], h_scales[0]],
dtype=np.float32)
# 2. crop (if necessary)
if crop_history is not None:
self.op_crop = GroupCrop(crop_history,
input_size=self.crop_size,
resize=True)
if self.op_crop is not None and isinstance(
self.op_crop, (GroupCrop, GroupMultiScaleCrop)):
img_group, crop_quadruple = self.op_crop(
img_group, is_flow=is_flow, interpolation=interpolation)
elif self.op_crop is not None:
img_group, crop_quadruple = self.op_crop(img_group,
is_flow=is_flow)
else:
crop_quadruple = None
img_shape = img_group[0].shape
if more_aug:
seq = iaa.Sequential([
iaa.GaussianBlur(sigma=np.random.uniform(0, 5)),
iaa.AdditiveGaussianNoise(loc=0,
scale=(0.0, 0.05 * 255),
per_channel=0.5),
])
img_group = seq(images=np.array(img_group))
# 3. flip
if flip:
img_group = [mmcv.imflip(img) for img in img_group]
if is_flow:
for i in range(0, len(img_group), 2):
img_group[i] = mmcv.iminvert(img_group[i])
# 4a. div_255
if div_255:
img_group = [
mmcv.imnormalize(img, 0, 255, False) for img in img_group
]
# 4. normalize
if normalize:
img_group = [
mmcv.imnormalize(img, self.mean, self.std, self.to_rgb)
for img in img_group
]
# 5. pad
if self.size_divisor is not None:
img_group = [
mmcv.impad_to_multiple(img, self.size_divisor)
for img in img_group
]
pad_shape = img_group[0].shape
else:
pad_shape = img_shape
if is_flow:
assert len(img_group[0].shape) == 2
img_group = [
np.stack((flow_x, flow_y), axis=2)
for flow_x, flow_y in zip(img_group[0::2], img_group[1::2])
]
# 6. transpose
if len(img_shape) == 2:
img_group = [img[:, :, np.newaxis] for img in img_group]
img_group = [img.transpose(2, 0, 1) for img in img_group]
# Stack into numpy.array
img_group = np.stack(img_group, axis=0)
return img_group, img_shape, pad_shape, scale_factor, crop_quadruple