def __init__(self, target_size=768, viz=False, debug=False):
self.target_size = target_size
self.viz = viz
self.debug = debug
self.gaussianTransformer = GaussianTransformer(imgSize=1024,
region_threshold=0.35,
affinity_threshold=0.15)
class craft_base_dataset():
def __init__(self, target_size=768, viz=False, debug=False):
self.target_size = target_size
self.viz = viz
self.debug = debug
self.gaussianTransformer = GaussianTransformer(imgSize=1024, region_threshold=0.35, affinity_threshold=0.15)
self.count = 0
def load_image_gt_and_confidencemask(self, index):
'''
confidence mask
:param index:
:return:
'''
return None, None, None, None, None
def crop_image_by_bbox(self, image, box):
w = (int)(np.linalg.norm(box[0] - box[1]))
h = (int)(np.linalg.norm(box[0] - box[3]))
width = w
height = h
if h > w * 1.5:
width = h
height = w
M = cv2.getPerspectiveTransform(np.float32(box),
np.float32(
np.array([[width, 0], [width, height], [0, height], [0, 0]])))
else:
M = cv2.getPerspectiveTransform(np.float32(box),
np.float32(
np.array([[0, 0], [width, 0], [width, height], [0, height]])))
warped = cv2.warpPerspective(image, M, (width, height))
return warped, M
def get_confidence(self, real_len, pursedo_len):
if pursedo_len == 0:
return 0.
return (real_len - min(real_len, abs(real_len - pursedo_len))) / real_len
def inference_pursedo_bboxes(self, net, image, word_bbox, word, viz=False):
word_image, MM = self.crop_image_by_bbox(image, word_bbox)
real_word_without_space = word.replace('\s', '')
real_char_nums = len(real_word_without_space)
input = word_image.copy()
scale = 64.0 / input.shape[0]
input = cv2.resize(input, None, fx=scale, fy=scale)
img_torch = torch.from_numpy(imgproc.normalizeMeanVariance(input, mean=(0.485, 0.456, 0.406),
variance=(0.229, 0.224, 0.225)))
img_torch = img_torch.permute(2, 0, 1).unsqueeze(0)
img_torch = img_torch.type(torch.FloatTensor).cuda()
scores, _ = net(img_torch)
region_scores = scores[0, :, :, 0].cpu().data.numpy()
fmf = np.uint8(np.clip(region_scores, 0, 1) * 255)
bgr_region_scores = cv2.resize(region_scores, (input.shape[1], input.shape[0]))
bgr_region_scores = cv2.cvtColor(bgr_region_scores, cv2.COLOR_GRAY2BGR)
pursedo_bboxes = watershed(input, bgr_region_scores, False)
_tmp = []
for i in range(pursedo_bboxes.shape[0]):
if np.mean(pursedo_bboxes[i].ravel()) > 2:
_tmp.append(pursedo_bboxes[i])
else:
print("filter bboxes", pursedo_bboxes[i])
pursedo_bboxes = np.array(_tmp, np.float32)
if pursedo_bboxes.shape[0] > 1:
index = np.argsort(pursedo_bboxes[:, 0, 0])
pursedo_bboxes = pursedo_bboxes[index]
confidence = self.get_confidence(real_char_nums, len(pursedo_bboxes))
bboxes = []
if confidence <= 0.5:
width = input.shape[1]
height = input.shape[0]
width_per_char = width / len(word)
for i, char in enumerate(word):
if char == ' ':
continue
left = i * width_per_char
right = (i + 1) * width_per_char
bbox = np.array([[left, 0], [right, 0], [right, height],
[left, height]])
bboxes.append(bbox)
bboxes = np.array(bboxes, np.float32)
confidence = 0.5
else:
bboxes = pursedo_bboxes
if False:
_tmp_bboxes = np.int32(bboxes.copy())
_tmp_bboxes[:, :, 0] = np.clip(_tmp_bboxes[:, :, 0], 0, input.shape[1])
_tmp_bboxes[:, :, 1] = np.clip(_tmp_bboxes[:, :, 1], 0, input.shape[0])
for bbox in _tmp_bboxes:
cv2.polylines(np.uint8(input), [np.reshape(bbox, (-1, 1, 2))], True, (255, 0, 0))
region_scores_color = cv2.applyColorMap(np.uint8(region_scores), cv2.COLORMAP_JET)
region_scores_color = cv2.resize(region_scores_color, (input.shape[1], input.shape[0]))
target = self.gaussianTransformer.generate_region(region_scores_color.shape, [_tmp_bboxes])
target_color = cv2.applyColorMap(target, cv2.COLORMAP_JET)
viz_image = np.hstack([input[:, :, ::-1], region_scores_color, target_color])
cv2.imshow("crop_image", viz_image)
cv2.waitKey()
bboxes /= scale
try:
for j in range(len(bboxes)):
ones = np.ones((4, 1))
tmp = np.concatenate([bboxes[j], ones], axis=-1)
I = np.matrix(MM).I
ori = np.matmul(I, tmp.transpose(1, 0)).transpose(1, 0)
bboxes[j] = ori[:, :2]
except Exception as e:
print(e, gt_path)
# for j in range(len(bboxes)):
# ones = np.ones((4, 1))
# tmp = np.concatenate([bboxes[j], ones], axis=-1)
# I = np.matrix(MM).I
# ori = np.matmul(I, tmp.transpose(1, 0)).transpose(1, 0)
# bboxes[j] = ori[:, :2]
bboxes[:, :, 1] = np.clip(bboxes[:, :, 1], 0., image.shape[0] - 1)
bboxes[:, :, 0] = np.clip(bboxes[:, :, 0], 0., image.shape[1] - 1)
return bboxes, region_scores, confidence
def resizeGt(self, gtmask):
return cv2.resize(gtmask, (self.target_size // 2, self.target_size // 2))
def get_imagename(self, index):
return None
def saveInput(self, imagename, image, region_scores, affinity_scores, confidence_mask):
boxes, polys = craft_utils.getDetBoxes(region_scores / 255, affinity_scores / 255, 0.7, 0.4, 0.4, False)
boxes = np.array(boxes, np.int32) * 2
if len(boxes) > 0:
np.clip(boxes[:, :, 0], 0, image.shape[1])
np.clip(boxes[:, :, 1], 0, image.shape[0])
for box in boxes:
cv2.polylines(image, [np.reshape(box, (-1, 1, 2))], True, (0, 0, 255))
target_gaussian_heatmap_color = imgproc.cvt2HeatmapImg(region_scores / 255)
target_gaussian_affinity_heatmap_color = imgproc.cvt2HeatmapImg(affinity_scores / 255)
confidence_mask_gray = imgproc.cvt2HeatmapImg(confidence_mask)
gt_scores = np.hstack([target_gaussian_heatmap_color, target_gaussian_affinity_heatmap_color])
confidence_mask_gray = np.hstack([np.zeros_like(confidence_mask_gray), confidence_mask_gray])
output = np.concatenate([gt_scores, confidence_mask_gray],
axis=0)
output = np.hstack([image, output])
outpath = os.path.join(os.path.join(os.path.dirname(__file__) + '/output'), "%s_input.jpg" % imagename)
#print(outpath)
if not os.path.exists(os.path.dirname(outpath)):
os.mkdir(os.path.dirname(outpath))
cv2.imwrite(outpath, output)
def saveImage(self, imagename, image, bboxes, affinity_bboxes, region_scores, affinity_scores, confidence_mask):
output_image = np.uint8(image.copy())
output_image = cv2.cvtColor(output_image, cv2.COLOR_RGB2BGR)
if len(bboxes) > 0:
affinity_bboxes = np.int32(affinity_bboxes)
for i in range(affinity_bboxes.shape[0]):
cv2.polylines(output_image, [np.reshape(affinity_bboxes[i], (-1, 1, 2))], True, (255, 0, 0))
for i in range(len(bboxes)):
_bboxes = np.int32(bboxes[i])
for j in range(_bboxes.shape[0]):
cv2.polylines(output_image, [np.reshape(_bboxes[j], (-1, 1, 2))], True, (0, 0, 255))
target_gaussian_heatmap_color = imgproc.cvt2HeatmapImg(region_scores / 255)
target_gaussian_affinity_heatmap_color = imgproc.cvt2HeatmapImg(affinity_scores / 255)
heat_map = np.concatenate([target_gaussian_heatmap_color, target_gaussian_affinity_heatmap_color], axis=1)
confidence_mask_gray = imgproc.cvt2HeatmapImg(confidence_mask)
output = np.concatenate([output_image, heat_map, confidence_mask_gray], axis=1)
outpath = os.path.join(os.path.join(os.path.dirname(os.path.abspath(__file__)) + '/output'), imagename)
if not os.path.exists(os.path.dirname(outpath)):
os.mkdir(os.path.dirname(outpath))
cv2.imwrite(outpath, output)
def pull_item(self, index):
# if self.get_imagename(index) == 'img_59.jpg':
# pass
# else:
# return [], [], [], [], np.array([0])
image, character_bboxes, words, confidence_mask, confidences = self.load_image_gt_and_confidencemask(index)
if len(confidences) == 0:
confidences = 1.0
else:
confidences = np.array(confidences).mean()
region_scores = np.zeros((image.shape[0], image.shape[1]), dtype=np.float32)
affinity_scores = np.zeros((image.shape[0], image.shape[1]), dtype=np.float32)
affinity_bboxes = []
if len(character_bboxes) > 0:
region_scores = self.gaussianTransformer.generate_region(region_scores.shape, character_bboxes)
affinity_scores, affinity_bboxes = self.gaussianTransformer.generate_affinity(region_scores.shape,
character_bboxes,
words)
if self.viz:
self.saveImage(self.get_imagename(index), image.copy(), character_bboxes, affinity_bboxes, region_scores,
affinity_scores,
confidence_mask)
random_transforms = [image, region_scores, affinity_scores, confidence_mask]
random_transforms = random_crop(random_transforms, (self.target_size, self.target_size), character_bboxes)
random_transforms = random_horizontal_flip(random_transforms)
random_transforms = random_rotate(random_transforms)
cvimage, region_scores, affinity_scores, confidence_mask = random_transforms
region_scores = self.resizeGt(region_scores)
affinity_scores = self.resizeGt(affinity_scores)
confidence_mask = self.resizeGt(confidence_mask)
if self.viz:
self.saveInput(self.get_imagename(index), cvimage, region_scores, affinity_scores, confidence_mask)
image = Image.fromarray(cvimage)
image = image.convert('RGB')
#image = transforms.ColorJitter(brightness=32.0 / 255, saturation=0.5)(image)
#밝기, 채 변화시키기
image = imgproc.normalizeMeanVariance(np.array(image), mean=(0.485, 0.456, 0.406),
variance=(0.229, 0.224, 0.225))
image_tensor = tf.convert_to_tensor(image, np.float32)
#image_tensor = tf.transpose(image_tensor,[2,0,1])
region_scores_tensor = tf.convert_to_tensor(region_scores / 255, np.float32)
affinity_scores_tensor = tf.convert_to_tensor(affinity_scores/255, np.float32)
confidence_mask_tensor = tf.convert_to_tensor(confidence_mask / 255, np.float32)
#print(confidences)
#self.count += 1
return image_tensor, region_scores_tensor, affinity_scores_tensor, confidence_mask_tensor, confidences
def generate_data(self):
for i in range(0,len(self)):
index = self.random_index_list[i]
output = self.pull_item(index)
self.count += 1
if i == len(self)-1:
i=0
yield output
def __init__(self, target_size=768, viz=False, debug=False):
self.target_size = target_size
self.viz = viz
self.debug = debug
self.gaussianTransformer = GaussianTransformer(imgSize=512)
class craft_base_dataset(data.Dataset):
def __init__(self, target_size=768, viz=False, debug=False):
self.target_size = target_size
self.viz = viz
self.debug = debug
self.gaussianTransformer = GaussianTransformer(imgSize=512,
distanceRatio=1.7)
def load_image_gt_and_confidencemask(self, index):
return None, None, None, None
def crop_image_by_bbox(self, image, box):
w = (int)(np.linalg.norm(box[0] - box[1]))
h = (int)(np.linalg.norm(box[0] - box[3]))
width = w
height = h
if h > w * 1.5:
width = h
height = w
M = cv2.getPerspectiveTransform(
np.float32(box),
np.float32(
np.array([[width, 0], [width, height], [0, height], [0,
0]])))
else:
M = cv2.getPerspectiveTransform(
np.float32(box),
np.float32(
np.array([[0, 0], [width, 0], [width, height], [0,
height]])))
warped = cv2.warpPerspective(image, M, (width, height))
return warped, M
def get_confidence(self, real_len, pursedo_len):
if pursedo_len == 0:
return 0.
return (real_len -
min(real_len, abs(real_len - pursedo_len))) / real_len
def inference_pursedo_bboxes(self, net, img, word, viz=False):
net.eval()
real_word_without_space = word.replace('\s', '')
real_char_nums = len(real_word_without_space)
input = img.copy()
scale = 64.0 / input.shape[0]
input = cv2.resize(input, None, fx=scale, fy=scale)
img_torch = torch.from_numpy(
imgproc.normalizeMeanVariance(input,
mean=(0.485, 0.456, 0.406),
variance=(0.229, 0.224, 0.225)))
img_torch = img_torch.permute(2, 0, 1).unsqueeze(0)
img_torch = img_torch.type(torch.FloatTensor).cuda()
scores, _ = net(img_torch)
region_scores = scores[0, :, :, 0].cpu().data.numpy()
region_scores = np.uint8(np.clip(region_scores, 0, 1) * 255)
bgr_region_scores = cv2.cvtColor(region_scores, cv2.COLOR_GRAY2BGR)
pursedo_bboxes = watershed2(bgr_region_scores)
if pursedo_bboxes.shape[0] > 1:
index = np.argsort(pursedo_bboxes[:, 0, 0])
pursedo_bboxes = pursedo_bboxes[index]
confidence = self.get_confidence(real_char_nums, len(pursedo_bboxes))
bboxes = []
if confidence <= 0.5:
width = input.shape[1]
height = input.shape[0]
width_per_char = width / len(word)
for i, char in enumerate(word):
if char == ' ':
continue
left = i * width_per_char
right = (i + 1) * width_per_char
bbox = np.array([[left, 0], [right, 0], [right, height],
[left, height]])
bboxes.append(bbox)
bboxes = np.array(bboxes, np.float32)
confidence = 0.5
else:
bboxes = pursedo_bboxes
bboxes *= 2
if viz:
_tmp_bboxes = np.int32(bboxes.copy())
for bbox in _tmp_bboxes:
cv2.polylines(np.uint8(input), [np.reshape(bbox, (-1, 1, 2))],
True, (255, 0, 0))
region_scores_color = cv2.applyColorMap(np.uint8(region_scores),
cv2.COLORMAP_JET)
region_scores_color = cv2.resize(region_scores_color,
(input.shape[1], input.shape[0]))
viz_image = np.hstack([input[:, :, ::-1], region_scores_color])
# cv2.imshow("crop_image", viz_image)
# cv2.waitKey()
bboxes /= scale
return bboxes, region_scores, confidence
def resizeGt(self, gtmask):
return cv2.resize(gtmask,
(self.target_size // 2, self.target_size // 2))
def get_imagename(self, index):
return None
def saveInput(self, imagename, image, region_scores, affinity_scores,
confidence_mask):
target_gaussian_heatmap_color = imgproc.cvt2HeatmapImg(region_scores /
255)
target_gaussian_affinity_heatmap_color = imgproc.cvt2HeatmapImg(
affinity_scores / 255)
confidence_mask_gray = imgproc.cvt2HeatmapImg(confidence_mask)
gt_scores = np.hstack([
target_gaussian_heatmap_color,
target_gaussian_affinity_heatmap_color
])
confidence_mask_gray = np.hstack(
[np.zeros_like(confidence_mask_gray), confidence_mask_gray])
output = np.concatenate([gt_scores, confidence_mask_gray], axis=0)
output = np.hstack([image, output])
outpath = os.path.join(
os.path.join(os.path.dirname(__file__) + '/output'),
"%s_input.jpg" % imagename)
if not os.path.exists(os.path.dirname(outpath)):
os.mkdir(os.path.dirname(outpath))
cv2.imwrite(outpath, output)
def saveImage(self, imagename, image, bboxes, affinity_bboxes,
region_scores, affinity_scores, confidence_mask):
output_image = np.uint8(image.copy())
output_image = cv2.cvtColor(output_image, cv2.COLOR_RGB2BGR)
if len(bboxes) > 0:
# affinity_bboxes = affinity_bboxes * 2
affinity_bboxes = np.int32(affinity_bboxes)
for i in range(affinity_bboxes.shape[0]):
cv2.polylines(output_image,
[np.reshape(affinity_bboxes[i],
(-1, 1, 2))], True, (255, 0, 0))
for i in range(len(bboxes)):
_bboxes = np.int32(bboxes[i])
for j in range(_bboxes.shape[0]):
cv2.polylines(output_image,
[np.reshape(_bboxes[j],
(-1, 1, 2))], True, (0, 0, 255))
target_gaussian_heatmap_color = imgproc.cvt2HeatmapImg(region_scores /
255)
target_gaussian_affinity_heatmap_color = imgproc.cvt2HeatmapImg(
affinity_scores / 255)
confidence_mask_gray = imgproc.cvt2HeatmapImg(confidence_mask)
output = np.concatenate([
output_image, target_gaussian_heatmap_color,
target_gaussian_affinity_heatmap_color, confidence_mask_gray
],
axis=1)
# output = np.hstack([image, output])
outpath = os.path.join(
os.path.join(os.path.dirname(__file__) + '/output'), imagename)
if not os.path.exists(os.path.dirname(outpath)):
os.mkdir(os.path.dirname(outpath))
cv2.imwrite(outpath, output)
def pull_item(self, index):
image, character_bboxes, words, confidence_mask = self.load_image_gt_and_confidencemask(
index)
region_scores = np.zeros((image.shape[0], image.shape[1]),
dtype=np.float32)
affinity_scores = np.zeros((image.shape[0], image.shape[1]),
dtype=np.float32)
affinity_bboxes = []
if len(character_bboxes) > 0:
region_scores = self.gaussianTransformer.generate_region(
image.shape, character_bboxes)
affinity_scores, affinity_bboxes = self.gaussianTransformer.generate_affinity(
image.shape, character_bboxes, words)
if self.viz:
self.saveImage(self.get_imagename(index), image.copy(),
character_bboxes, affinity_bboxes, region_scores,
affinity_scores, confidence_mask)
random_transforms = [
image, region_scores, affinity_scores, confidence_mask
]
random_transforms = random_horizontal_flip(random_transforms)
random_transforms = random_rotate(random_transforms)
random_transforms = random_crop(random_transforms,
(self.target_size, self.target_size))
cvimage, region_scores, affinity_scores, confidence_mask = random_transforms
region_scores = self.resizeGt(region_scores)
affinity_scores = self.resizeGt(affinity_scores)
confidence_mask = self.resizeGt(confidence_mask)
if self.viz:
self.saveInput(self.get_imagename(index), cvimage, region_scores,
affinity_scores, confidence_mask)
image = Image.fromarray(cvimage)
image = image.convert('RGB')
image = transforms.ColorJitter(brightness=32.0 / 255,
saturation=0.5)(image)
# image = cvimage
image = imgproc.normalizeMeanVariance(np.array(image),
mean=(0.485, 0.456, 0.406),
variance=(0.229, 0.224, 0.225))
image = torch.from_numpy(image).float().permute(2, 0, 1)
region_scores_torch = torch.from_numpy(region_scores / 255).float()
affinity_scores_torch = torch.from_numpy(affinity_scores / 255).float()
confidence_mask_torch = torch.from_numpy(confidence_mask).float()
return image.double(), region_scores_torch.double(
), affinity_scores_torch.double(), confidence_mask_torch.double()
