def __getitem__(self, idx):
img_path, gt = self.lines[idx]
img = cv2.imread(img_path, 0)
if img is None:
return None
if img.shape[0] != self.img_height:
if img.shape[0] < self.img_height and not self.warning:
self.warning = True
print("WARNING: upsampling image to fit size")
percent = float(self.img_height) / img.shape[0]
img = cv2.resize(img, (0, 0),
fx=percent,
fy=percent,
interpolation=cv2.INTER_CUBIC)
if img is None:
return None
if self.augmentation:
img = augmentation.apply_random_color_rotation(img)
img = augmentation.apply_tensmeyer_brightness(img)
img = grid_distortion.warp_image(img)
img = img.astype(np.float32)
img = img / 128.0 - 1.0
img = img[..., None]
if len(gt) == 0:
return None
gt_label = string_utils.str2label_single(gt, self.char_to_idx)
return {"line_img": img, "gt": gt, "gt_label": gt_label}
def __getitem__(self, idx):
gt_json_path, img_path = self.ids[idx]
gt_json = safe_load.json_state(gt_json_path)
if gt_json is None:
return None
# print('img_path: {}'.format(img_path))
org_img = cv2.imread(img_path, cv2.IMREAD_COLOR)
# print('img.size: {}'.format(org_img.shape))
# median = np.median(org_img, axis=(0,1))
# org_img = cv2.copyMakeBorder(org_img,100,100,100,100,cv2.BORDER_CONSTANT,value=median)
target_dim1 = int(np.random.uniform(self.rescale_range[0], self.rescale_range[1]))
s = target_dim1 / float(org_img.shape[1])
target_dim0 = int(org_img.shape[0]/float(org_img.shape[1]) * target_dim1)
org_img = cv2.resize(org_img,(target_dim1, target_dim0), interpolation = cv2.INTER_CUBIC)
gt = np.zeros((1,len(gt_json['corners']), 4), dtype=np.float32)
for j, gt_item in enumerate(gt_json['corners']):
x0 = gt_item[0]
x1 = gt_item[0]
y0 = gt_item[1]
y1 = gt_item[1]
gt[:,j,0] = x0 * s
gt[:,j,1] = y0 * s
gt[:,j,2] = x1 * s
gt[:,j,3] = y1 * s
if self.transform is not None:
out = self.transform({
"img": org_img,
"sol_gt": gt
})
org_img = out['img']
gt = out['sol_gt']
org_img = augmentation.apply_random_color_rotation(org_img)
org_img = augmentation.apply_tensmeyer_brightness(org_img)
org_img = augmentation.apply_random_blur(org_img)
img = org_img.transpose([2,1,0])[None,...]
img = img.astype(np.float32)
img = torch.from_numpy(img)
img = img / 128.0 - 1.0
if gt.shape[1] == 0:
gt = None
else:
gt = torch.from_numpy(gt)
return {
"img": img,
"sol_gt": gt
}
def __getitem__(self, idx):
gt_json_path, img_path = self.ids[idx]
gt_json = safe_load.json_state(gt_json_path)
if gt_json is None:
return None
org_img = cv2.imread(img_path)
target_dim1 = int(np.random.uniform(self.rescale_range[0], self.rescale_range[1]))
s = target_dim1 / float(org_img.shape[1])
target_dim0 = int(org_img.shape[0]/float(org_img.shape[1]) * target_dim1)
org_img = cv2.resize(org_img,(target_dim1, target_dim0), interpolation = cv2.INTER_CUBIC)
gt = np.zeros((1,len(gt_json), 4), dtype=np.float32)
for j, gt_item in enumerate(gt_json):
if 'sol' not in gt_item:
continue
x0 = gt_item['sol']['x0']
x1 = gt_item['sol']['x1']
y0 = gt_item['sol']['y0']
y1 = gt_item['sol']['y1']
gt[:,j,0] = x0 * s
gt[:,j,1] = y0 * s
gt[:,j,2] = x1 * s
gt[:,j,3] = y1 * s
if self.transform is not None:
out = self.transform({
"img": org_img,
"sol_gt": gt
})
org_img = out['img']
gt = out['sol_gt']
org_img = augmentation.apply_random_color_rotation(org_img)
org_img = augmentation.apply_tensmeyer_brightness(org_img)
img = org_img.transpose([2,1,0])[None,...]
img = img.astype(np.float32)
img = torch.from_numpy(img)
img = img / 128.0 - 1.0
if gt.shape[1] == 0:
gt = None
else:
gt = torch.from_numpy(gt)
return {
"img": img,
"sol_gt": gt
}
def __getitem__(self, idx):
ids_idx, line_idx = self.detailed_ids[idx]
gt_json_path, img_path = self.ids[ids_idx]
gt_json = safe_load.json_state(gt_json_path)
positions = []
positions_xy = []
if 'lf' not in gt_json[line_idx]:
return None
for step in gt_json[line_idx]['lf']:
x0 = step['x0']
x1 = step['x1']
y0 = step['y0']
y1 = step['y1']
positions_xy.append((torch.Tensor([[x1, x0], [y1, y0]])))
dx = x0 - x1
dy = y0 - y1
d = math.sqrt(dx**2 + dy**2)
mx = (x0 + x1) / 2.0
my = (y0 + y1) / 2.0
#Not sure if this is right...
theta = -math.atan2(dx, -dy)
positions.append(torch.Tensor([mx, my, theta, d / 2, 1.0]))
img = cv2.imread(img_path)
if self.augmentation:
img = augmentation.apply_random_color_rotation(img)
img = augmentation.apply_tensmeyer_brightness(img)
img = img.astype(np.float32)
img = img.transpose()
img = img / 128.0 - 1.0
img = torch.from_numpy(img)
gt = gt_json[line_idx]['gt']
result = {
"img": img,
"lf_xyrs": positions,
"lf_xyxy": positions_xy,
"gt": gt
}
return result
def __getitem__(self, idx):
ids_idx, line_idx = self.detailed_ids[idx]
gt_json_path, img_path = self.ids[ids_idx]
gt_json = safe_load.json_state(gt_json_path)
if gt_json is None:
return None
if 'hw_path' not in gt_json[line_idx]:
return None
hw_path = gt_json[line_idx]['hw_path']
hw_path = hw_path.split("/")[-1:]
hw_path = "/".join(hw_path)
hw_folder = os.path.dirname(gt_json_path)
img = cv2.imread(os.path.join(hw_folder, hw_path))
if img is None:
return None
if img.shape[0] != self.img_height:
if img.shape[0] < self.img_height and not self.warning:
self.warning = True
print "WARNING: upsampling image to fit size"
percent = float(self.img_height) / img.shape[0]
img = cv2.resize(img, (0,0), fx=percent, fy=percent, interpolation = cv2.INTER_CUBIC)
if img is None:
return None
if self.augmentation:
img = augmentation.apply_random_color_rotation(img)
img = augmentation.apply_tensmeyer_brightness(img)
img = grid_distortion.warp_image(img)
img = img.astype(np.float32)
img = img / 128.0 - 1.0
gt = gt_json[line_idx]['gt']
if len(gt) == 0:
return None
gt_label = string_utils.str2label_single(gt, self.char_to_idx)
return {
"line_img": img,
"gt": gt,
"gt_label": gt_label
}
def getitem(self, index, scaleP=None, cropPoint=None):
if self.useRandomAugProb is not None and np.random.rand(
) < self.useRandomAugProb and scaleP is None and cropPoint is None:
return self.getRandomImage()
##ticFull=timeit.default_timer()
imagePath = self.images[index]['imagePath']
imageName = self.images[index]['imageName']
annotationPath = self.images[index]['annotationPath']
#print(annotationPath)
rescaled = self.images[index]['rescaled']
with open(annotationPath) as annFile:
annotations = json.loads(annFile.read())
##tic=timeit.default_timer()
np_img = cv2.imread(imagePath, 1 if self.color else 0) #/255.0
if np_img is None or np_img.shape[0] == 0:
print("ERROR, could not open " + imagePath)
return self.__getitem__((index + 1) % self.__len__())
if scaleP is None:
s = np.random.uniform(self.rescale_range[0], self.rescale_range[1])
else:
s = scaleP
partial_rescale = s / rescaled
if self.transform is None: #we're doing the whole image
#this is a check to be sure we don't send too big images through
pixel_count = partial_rescale * partial_rescale * np_img.shape[
0] * np_img.shape[1]
if pixel_count > self.pixel_count_thresh:
partial_rescale = math.sqrt(partial_rescale * partial_rescale *
self.pixel_count_thresh /
pixel_count)
print('{} exceed thresh: {}: {}, new {}: {}'.format(
imageName, s, pixel_count, rescaled * partial_rescale,
partial_rescale * partial_rescale * np_img.shape[0] *
np_img.shape[1]))
s = rescaled * partial_rescale
max_dim = partial_rescale * max(np_img.shape[0], np_img.shape[1])
if max_dim > self.max_dim_thresh:
partial_rescale = partial_rescale * (self.max_dim_thresh /
max_dim)
print('{} exceed thresh: {}: {}, new {}: {}'.format(
imageName, s, max_dim, rescaled * partial_rescale,
partial_rescale * max(np_img.shape[0], np_img.shape[1])))
s = rescaled * partial_rescale
##tic=timeit.default_timer()
#np_img = cv2.resize(np_img,(target_dim1, target_dim0), interpolation = cv2.INTER_CUBIC)
np_img = cv2.resize(np_img, (0, 0),
fx=partial_rescale,
fy=partial_rescale,
interpolation=cv2.INTER_CUBIC)
if not self.color:
np_img = np_img[..., None] #add 'color' channel
##print('resize: {} [{}, {}]'.format(timeit.default_timer()-tic,np_img.shape[0],np_img.shape[1]))
bbs, line_gts, point_gts, pixel_gt, numClasses, numNeighbors, pairs = self.parseAnn(
np_img, annotations, s, imagePath)
if self.coordConv: #add absolute position information
xs = 255 * np.arange(np_img.shape[1]) / (np_img.shape[1])
xs = np.repeat(xs[None, :, None], np_img.shape[0], axis=0)
ys = 255 * np.arange(np_img.shape[0]) / (np_img.shape[0])
ys = np.repeat(ys[:, None, None], np_img.shape[1], axis=1)
np_img = np.concatenate(
(np_img, xs.astype(np_img.dtype), ys.astype(np_img.dtype)),
axis=2)
##ticTr=timeit.default_timer()
if self.transform is not None:
pairs = None
out, cropPoint = self.transform(
{
"img": np_img,
"bb_gt": bbs,
"bb_auxs": numNeighbors,
"line_gt": line_gts,
"point_gt": point_gts,
"pixel_gt": pixel_gt,
}, cropPoint)
np_img = out['img']
bbs = out['bb_gt']
numNeighbors = out['bb_auxs']
#if 'table_points' in out['point_gt']:
# table_points = out['point_gt']['table_points']
#else:
# table_points=None
point_gts = out['point_gt']
pixel_gt = out['pixel_gt']
#start_of_line = out['line_gt']['start_of_line']
#end_of_line = out['line_gt']['end_of_line']
line_gts = out['line_gt']
##tic=timeit.default_timer()
if self.color:
np_img[:, :, :3] = augmentation.apply_random_color_rotation(
np_img[:, :, :3])
np_img[:, :, :3] = augmentation.apply_tensmeyer_brightness(
np_img[:, :, :3])
else:
np_img[:, :, 0:1] = augmentation.apply_tensmeyer_brightness(
np_img[:, :, 0:1])
##print('augmentation: {}'.format(timeit.default_timer()-tic))
##print('transfrm: {} [{}, {}]'.format(timeit.default_timer()-ticTr,org_img.shape[0],org_img.shape[1]))
#if len(np_img.shape)==2:
# img=np_img[None,None,:,:] #add "color" channel and batch
#else:
img = np_img.transpose(
[2, 0, 1])[None,
...] #from [row,col,color] to [batch,color,row,col]
img = img.astype(np.float32)
img = torch.from_numpy(img)
img = 1.0 - img / 128.0 #ideally the median value would be 0
#img = 1.0 - img / 255.0 #this way ink is on, page is off
if pixel_gt is not None:
pixel_gt = pixel_gt.transpose([2, 0, 1])[None, ...]
pixel_gt = torch.from_numpy(pixel_gt)
#start_of_line = None if start_of_line is None or start_of_line.shape[1] == 0 else torch.from_numpy(start_of_line)
#end_of_line = None if end_of_line is None or end_of_line.shape[1] == 0 else torch.from_numpy(end_of_line)
for name in line_gts:
line_gts[name] = None if line_gts[name] is None or line_gts[
name].shape[1] == 0 else torch.from_numpy(line_gts[name])
#import pdb; pdb.set_trace()
#bbs = None if bbs.shape[1] == 0 else torch.from_numpy(bbs)
bbs = convertBBs(bbs, self.rotate, numClasses)
if len(numNeighbors) > 0:
numNeighbors = torch.tensor(numNeighbors)[None, :] #add batch dim
else:
numNeighbors = None
#start_of_line = convertLines(start_of_line,numClasses)
#end_of_line = convertLines(end_of_line,numClasses)
for name in point_gts:
#if table_points is not None:
#table_points = None if table_points.shape[1] == 0 else torch.from_numpy(table_points)
if point_gts[name] is not None:
point_gts[name] = None if point_gts[name].shape[
1] == 0 else torch.from_numpy(point_gts[name])
##print('__getitem__: '+str(timeit.default_timer()-ticFull))
if self.only_types is None:
return {
"img": img,
"bb_gt": bbs,
"num_neighbors": numNeighbors,
"line_gt": line_gts,
"point_gt": point_gts,
"pixel_gt": pixel_gt,
"imgName": imageName,
"scale": s,
"cropPoint": cropPoint,
"pairs": pairs
}
else:
if 'boxes' not in self.only_types or not self.only_types['boxes']:
bbs = None
line_gt = {}
if 'line' in self.only_types:
for ent in self.only_types['line']:
if type(ent) == list:
toComb = []
for inst in ent[1:]:
einst = line_gts[inst]
if einst is not None:
toComb.append(einst)
if len(toComb) > 0:
comb = torch.cat(toComb, dim=1)
line_gt[ent[0]] = comb
else:
line_gt[ent[0]] = None
else:
line_gt[ent] = line_gts[ent]
point_gt = {}
if 'point' in self.only_types:
for ent in self.only_types['point']:
if type(ent) == list:
toComb = []
for inst in ent[1:]:
einst = point_gts[inst]
if einst is not None:
toComb.append(einst)
if len(toComb) > 0:
comb = torch.cat(toComb, dim=1)
point_gt[ent[0]] = comb
else:
line_gt[ent[0]] = None
else:
point_gt[ent] = point_gts[ent]
pixel_gtR = None
#for ent in self.only_types['pixel']:
# if type(ent)==list:
# comb = ent[1]
# for inst in ent[2:]:
# comb = (comb + inst)==2 #:eq(2) #pixel-wise AND
# pixel_gt[ent[0]]=comb
# else:
# pixel_gt[ent]=eval(ent)
if 'pixel' in self.only_types: # and self.only_types['pixel'][0]=='table_pixels':
pixel_gtR = pixel_gt
return {
"img": img,
"bb_gt": bbs,
"num_neighbors": numNeighbors,
"line_gt": line_gt,
"point_gt": point_gt,
"pixel_gt": pixel_gtR,
"imgName": imageName,
"scale": s,
"cropPoint": cropPoint,
"pairs": pairs,
}
def getitem(self, index, scaleP=None, cropPoint=None):
##ticFull=timeit.default_timer()
imagePath = self.images[index]['imagePath']
imageName = self.images[index]['imageName']
annotationPath = self.images[index]['annotationPath']
#print(annotationPath)
rescaled = self.images[index]['rescaled']
with open(annotationPath) as annFile:
annotations = json.loads(annFile.read())
##tic=timeit.default_timer()
np_img = cv2.imread(imagePath, 1 if self.color else 0) #/255.0
if np_img is None or np_img.shape[0] == 0:
print("ERROR, could not open " + imagePath)
return self.__getitem__((index + 1) % self.__len__())
if scaleP is None:
s = np.random.uniform(self.rescale_range[0], self.rescale_range[1])
else:
s = scaleP
partial_rescale = s / rescaled
if self.transform is None: #we're doing the whole image
#this is a check to be sure we don't send too big images through
pixel_count = partial_rescale * partial_rescale * np_img.shape[
0] * np_img.shape[1]
if pixel_count > self.pixel_count_thresh:
partial_rescale = math.sqrt(partial_rescale * partial_rescale *
self.pixel_count_thresh /
pixel_count)
print('{} exceed thresh: {}: {}, new {}: {}'.format(
imageName, s, pixel_count, rescaled * partial_rescale,
partial_rescale * partial_rescale * np_img.shape[0] *
np_img.shape[1]))
s = rescaled * partial_rescale
max_dim = partial_rescale * max(np_img.shape[0], np_img.shape[1])
if max_dim > self.max_dim_thresh:
partial_rescale = partial_rescale * (self.max_dim_thresh /
max_dim)
print('{} exceed thresh: {}: {}, new {}: {}'.format(
imageName, s, max_dim, rescaled * partial_rescale,
partial_rescale * max(np_img.shape[0], np_img.shape[1])))
s = rescaled * partial_rescale
##tic=timeit.default_timer()
#np_img = cv2.resize(np_img,(target_dim1, target_dim0), interpolation = cv2.INTER_CUBIC)
np_img = cv2.resize(np_img, (0, 0),
fx=partial_rescale,
fy=partial_rescale,
interpolation=cv2.INTER_CUBIC)
if not self.color:
np_img = np_img[..., None] #add 'color' channel
##print('resize: {} [{}, {}]'.format(timeit.default_timer()-tic,np_img.shape[0],np_img.shape[1]))
##tic=timeit.default_timer()
bbs, ids, numClasses, trans = self.parseAnn(annotations, s)
#start_of_line, end_of_line = getStartEndGT(annotations['byId'].values(),s)
#Try:
# table_points, table_pixels = self.getTables(
# fieldBBs,
# s,
# np_img.shape[0],
# np_img.shape[1],
# annotations['samePairs'])
#Except Exception as inst:
# if imageName not in self.errors:
# table_points=None
# table_pixels=None
# print(inst)
# print('Table error on: '+imagePath)
# self.errors.append(imageName)
#pixel_gt = table_pixels
##ticTr=timeit.default_timer()
if self.transform is not None:
out, cropPoint = self.transform(
{
"img": np_img,
"bb_gt": bbs,
'bb_auxs': ids,
#"line_gt": {
# "start_of_line": start_of_line,
# "end_of_line": end_of_line
# },
#"point_gt": {
# "table_points": table_points
# },
#"pixel_gt": pixel_gt,
},
cropPoint)
np_img = out['img']
bbs = out['bb_gt']
ids = out['bb_auxs']
##tic=timeit.default_timer()
if np_img.shape[2] == 3:
np_img = augmentation.apply_random_color_rotation(np_img)
np_img = augmentation.apply_tensmeyer_brightness(np_img)
else:
np_img = augmentation.apply_tensmeyer_brightness(np_img)
##print('augmentation: {}'.format(timeit.default_timer()-tic))
##print('transfrm: {} [{}, {}]'.format(timeit.default_timer()-ticTr,org_img.shape[0],org_img.shape[1]))
pairs = set()
#import pdb;pdb.set_trace()
numNeighbors = [0] * len(ids)
for index1, id in enumerate(ids): #updated
responseBBIdList = self.getResponseBBIdList(id, annotations)
for bbId in responseBBIdList:
try:
index2 = ids.index(bbId)
#adjMatrix[min(index1,index2),max(index1,index2)]=1
pairs.add((min(index1, index2), max(index1, index2)))
numNeighbors[index1] += 1
except ValueError:
pass
#ones = torch.ones(len(pairs))
#if len(pairs)>0:
# pairs = torch.LongTensor(list(pairs)).t()
#else:
# pairs = torch.LongTensor(pairs)
#adjMatrix = torch.sparse.FloatTensor(pairs,ones,(len(ids),len(ids))) # This is an upper diagonal matrix as pairings are bi-directional
#if len(np_img.shape)==2:
# img=np_img[None,None,:,:] #add "color" channel and batch
#else:
img = np_img.transpose(
[2, 0, 1])[None,
...] #from [row,col,color] to [batch,color,row,col]
img = img.astype(np.float32)
img = torch.from_numpy(img)
img = 1.0 - img / 128.0 #ideally the median value would be 0
#if pixel_gt is not None:
# pixel_gt = pixel_gt.transpose([2,0,1])[None,...]
# pixel_gt = torch.from_numpy(pixel_gt)
#start_of_line = None if start_of_line is None or start_of_line.shape[1] == 0 else torch.from_numpy(start_of_line)
#end_of_line = None if end_of_line is None or end_of_line.shape[1] == 0 else torch.from_numpy(end_of_line)
bbs = convertBBs(bbs, self.rotate, numClasses)
if len(numNeighbors) > 0:
numNeighbors = torch.tensor(numNeighbors)[None, :] #add batch dim
else:
numNeighbors = None
#if table_points is not None:
# table_points = None if table_points.shape[1] == 0 else torch.from_numpy(table_points)
return {
"img": img,
"bb_gt": bbs,
"num_neighbors": numNeighbors,
"adj": pairs, #adjMatrix,
"imgName": imageName,
"scale": s,
"cropPoint": cropPoint,
"transcription": [trans[id] for id in ids if id in trans]
}
def getitem(self, index, scaleP=None, cropPoint=None):
##ticFull=timeit.default_timer()
imagePath = self.images[index]['imagePath']
imageName = self.images[index]['imageName']
annotationPath = self.images[index]['annotationPath']
#print(annotationPath)
rescaled = self.images[index]['rescaled']
with open(annotationPath) as annFile:
annotations = json.loads(annFile.read())
##tic=timeit.default_timer()
np_img = img_f.imread(imagePath, 1 if self.color else 0) #*255.0
if np_img.max() < 200:
np_img *= 255
if np_img is None or np_img.shape[0] == 0:
print("ERROR, could not open " + imagePath)
return self.__getitem__((index + 1) % self.__len__())
if scaleP is None:
s = np.random.uniform(self.rescale_range[0], self.rescale_range[1])
else:
s = scaleP
partial_rescale = s / rescaled
if self.transform is None: #we're doing the whole image
#this is a check to be sure we don't send too big images through
pixel_count = partial_rescale * partial_rescale * np_img.shape[
0] * np_img.shape[1]
if pixel_count > self.pixel_count_thresh:
partial_rescale = math.sqrt(partial_rescale * partial_rescale *
self.pixel_count_thresh /
pixel_count)
print('{} exceed thresh: {}: {}, new {}: {}'.format(
imageName, s, pixel_count, rescaled * partial_rescale,
partial_rescale * partial_rescale * np_img.shape[0] *
np_img.shape[1]))
s = rescaled * partial_rescale
max_dim = partial_rescale * max(np_img.shape[0], np_img.shape[1])
if max_dim > self.max_dim_thresh:
partial_rescale = partial_rescale * (self.max_dim_thresh /
max_dim)
print('{} exceed thresh: {}: {}, new {}: {}'.format(
imageName, s, max_dim, rescaled * partial_rescale,
partial_rescale * max(np_img.shape[0], np_img.shape[1])))
s = rescaled * partial_rescale
##tic=timeit.default_timer()
#np_img = img_f.resize(np_img,(target_dim1, target_dim0))
np_img = img_f.resize(
np_img,
(0, 0),
fx=partial_rescale,
fy=partial_rescale,
)
if len(np_img.shape) == 2:
np_img = np_img[..., None] #add 'color' channel
if self.color and np_img.shape[2] == 1:
np_img = np.repeat(np_img, 3, axis=2)
##print('resize: {} [{}, {}]'.format(timeit.default_timer()-tic,np_img.shape[0],np_img.shape[1]))
##tic=timeit.default_timer()
bbs, ids, numClasses, trans, groups, metadata, form_metadata = self.parseAnn(
annotations, s)
#trans = {i:v for i,v in enumerate(trans)}
#metadata = {i:v for i,v in enumerate(metadata)}
#start_of_line, end_of_line = getStartEndGT(annotations['byId'].values(),s)
#Try:
# table_points, table_pixels = self.getTables(
# fieldBBs,
# s,
# np_img.shape[0],
# np_img.shape[1],
# annotations['samePairs'])
#Except Exception as inst:
# if imageName not in self.errors:
# table_points=None
# table_pixels=None
# print(inst)
# print('Table error on: '+imagePath)
# self.errors.append(imageName)
#pixel_gt = table_pixels
##ticTr=timeit.default_timer()
if self.questions: #we need to do questions before crop to have full context
#we have to relationships to get questions
pairs = set()
for index1, id in enumerate(ids): #updated
responseBBIdList = self.getResponseBBIdList(id, annotations)
for bbId in responseBBIdList:
try:
index2 = ids.index(bbId)
pairs.add((min(index1, index2), max(index1, index2)))
except ValueError:
pass
groups_adj = set()
if groups is not None:
for n0, n1 in pairs:
g0 = -1
g1 = -1
for i, ns in enumerate(groups):
if n0 in ns:
g0 = i
if g1 != -1:
break
if n1 in ns:
g1 = i
if g0 != -1:
break
if g0 != g1:
groups_adj.add((min(g0, g1), max(g0, g1)))
questions_and_answers = self.makeQuestions(bbs, trans, groups,
groups_adj)
else:
questions_and_answers = None
if self.transform is not None:
if 'word_boxes' in form_metadata:
word_bbs = form_metadata['word_boxes']
dif_f = bbs.shape[2] - word_bbs.shape[1]
blank = np.zeros([word_bbs.shape[0], dif_f])
prep_word_bbs = np.concatenate([word_bbs, blank], axis=1)[None,
...]
crop_bbs = np.concatenate([bbs, prep_word_bbs], axis=1)
crop_ids = ids + [
'word{}'.format(i) for i in range(word_bbs.shape[0])
]
else:
crop_bbs = bbs
crop_ids = ids
out, cropPoint = self.transform(
{
"img": np_img,
"bb_gt": crop_bbs,
'bb_auxs': crop_ids,
#'word_bbs':form_metadata['word_boxes'] if 'word_boxes' in form_metadata else None
#"line_gt": {
# "start_of_line": start_of_line,
# "end_of_line": end_of_line
# },
#"point_gt": {
# "table_points": table_points
# },
#"pixel_gt": pixel_gt,
},
cropPoint)
np_img = out['img']
if 'word_boxes' in form_metadata:
saw_word = False
word_index = -1
for i, ii in enumerate(out['bb_auxs']):
if not saw_word:
if type(ii) is str and 'word' in ii:
saw_word = True
word_index = i
else:
assert 'word' in ii
bbs = out['bb_gt'][:, :word_index]
ids = out['bb_auxs'][:word_index]
form_metadata['word_boxes'] = out['bb_gt'][0, word_index:, :8]
word_ids = out['bb_auxs'][word_index:]
form_metadata['word_trans'] = [
form_metadata['word_trans'][int(id[4:])] for id in word_ids
]
else:
bbs = out['bb_gt']
ids = out['bb_auxs']
if questions_and_answers is not None:
questions = []
answers = []
questions_and_answers = [
(q, a, qids) for q, a, qids in questions_and_answers
if all((i in ids) for i in qids)
]
if questions_and_answers is not None:
if len(questions_and_answers) > self.questions:
questions_and_answers = random.sample(questions_and_answers,
k=self.questions)
if len(questions_and_answers) > 0:
questions, answers, _ = zip(*questions_and_answers)
else:
return self.getitem((index + 1) % len(self))
else:
questions = answers = None
##tic=timeit.default_timer()
if np_img.shape[2] == 3:
np_img = augmentation.apply_random_color_rotation(np_img)
np_img = augmentation.apply_tensmeyer_brightness(
np_img, **self.aug_params)
else:
np_img = augmentation.apply_tensmeyer_brightness(
np_img, **self.aug_params)
##print('augmentation: {}'.format(timeit.default_timer()-tic))
newGroups = []
for group in groups:
newGroup = [ids.index(bbId) for bbId in group if bbId in ids]
if len(newGroup) > 0:
newGroups.append(newGroup)
#print(len(newGroups)-1,newGroup)
groups = newGroups
##print('transfrm: {} [{}, {}]'.format(timeit.default_timer()-ticTr,org_img.shape[0],org_img.shape[1]))
pairs = set()
#import pdb;pdb.set_trace()
numNeighbors = [0] * len(ids)
for index1, id in enumerate(ids): #updated
responseBBIdList = self.getResponseBBIdList(id, annotations)
for bbId in responseBBIdList:
try:
index2 = ids.index(bbId)
#adjMatrix[min(index1,index2),max(index1,index2)]=1
pairs.add((min(index1, index2), max(index1, index2)))
numNeighbors[index1] += 1
except ValueError:
pass
#ones = torch.ones(len(pairs))
#if len(pairs)>0:
# pairs = torch.LongTensor(list(pairs)).t()
#else:
# pairs = torch.LongTensor(pairs)
#adjMatrix = torch.sparse.FloatTensor(pairs,ones,(len(ids),len(ids))) # This is an upper diagonal matrix as pairings are bi-directional
#if len(np_img.shape)==2:
# img=np_img[None,None,:,:] #add "color" channel and batch
#else:
img = np_img.transpose(
[2, 0, 1])[None,
...] #from [row,col,color] to [batch,color,row,col]
img = img.astype(np.float32)
img = torch.from_numpy(img)
img = 1.0 - img / 128.0 #ideally the median value would be 0
#if pixel_gt is not None:
# pixel_gt = pixel_gt.transpose([2,0,1])[None,...]
# pixel_gt = torch.from_numpy(pixel_gt)
#start_of_line = None if start_of_line is None or start_of_line.shape[1] == 0 else torch.from_numpy(start_of_line)
#end_of_line = None if end_of_line is None or end_of_line.shape[1] == 0 else torch.from_numpy(end_of_line)
bbs = convertBBs(bbs, self.rotate, numClasses)
if 'word_boxes' in form_metadata:
form_metadata['word_boxes'] = convertBBs(
form_metadata['word_boxes'][None, ...], self.rotate, 0)[0, ...]
if len(numNeighbors) > 0:
numNeighbors = torch.tensor(numNeighbors)[None, :] #add batch dim
else:
numNeighbors = None
#if table_points is not None:
# table_points = None if table_points.shape[1] == 0 else torch.from_numpy(table_points)
groups_adj = set()
if groups is not None:
for n0, n1 in pairs:
g0 = -1
g1 = -1
for i, ns in enumerate(groups):
if n0 in ns:
g0 = i
if g1 != -1:
break
if n1 in ns:
g1 = i
if g0 != -1:
break
if g0 != g1:
groups_adj.add((min(g0, g1), max(g0, g1)))
for group in groups:
for i in group:
assert (i < bbs.shape[1])
targetIndexToGroup = {}
for groupId, bbIds in enumerate(groups):
targetIndexToGroup.update({bbId: groupId for bbId in bbIds})
transcription = [trans[id] for id in ids]
return {
"img": img,
"bb_gt": bbs,
"num_neighbors": numNeighbors,
"adj": pairs, #adjMatrix,
"imgName": imageName,
"scale": s,
"cropPoint": cropPoint,
"transcription": transcription,
"metadata": [metadata[id] for id in ids if id in metadata],
"form_metadata": form_metadata,
"gt_groups": groups,
"targetIndexToGroup": targetIndexToGroup,
"gt_groups_adj": groups_adj,
"questions": questions,
"answers": answers
}
def __getitem__(self, idx):
gt_json_path, img_path = self.ids[idx]
gt_json = safe_load.json_state(gt_json_path)
if gt_json is None:
return None
org_img = cv2.imread(img_path)
target_dim1 = int(
np.random.uniform(self.rescale_range[0], self.rescale_range[1]))
s = target_dim1 / float(org_img.shape[1])
target_dim0 = int(org_img.shape[0] / float(org_img.shape[1]) *
target_dim1)
org_img = cv2.resize(org_img, (target_dim1, target_dim0),
interpolation=cv2.INTER_CUBIC)
gt = np.zeros((1, len(gt_json), 4), dtype=np.float32)
positions = []
positions_xy = []
for j, gt_item in enumerate(gt_json):
if 'sol' not in gt_item:
continue
x0 = gt_item['sol']['x0'] * s
x1 = gt_item['sol']['x1'] * s
y0 = gt_item['sol']['y0'] * s
y1 = gt_item['sol']['y1'] * s
positions_xy.append([(torch.Tensor([[x1, x0], [y1, y0]]))])
dx = x0 - x1
dy = y0 - y1
d = math.sqrt(dx**2 + dy**2)
mx = (x0 + x1) / 2.0
my = (y0 + y1) / 2.0
# Not sure if this is right...
theta = -math.atan2(dx, -dy)
positions.append([torch.Tensor([mx, my, theta, d / 2, 1.0])])
gt[:, j, 0] = x0
gt[:, j, 1] = y0
gt[:, j, 2] = x1
gt[:, j, 3] = y1
if self.transform is not None:
out = self.transform({"img": org_img, "sol_gt": gt})
org_img = out['img']
gt = out['sol_gt']
org_img = augmentation.apply_random_color_rotation(org_img)
org_img = augmentation.apply_tensmeyer_brightness(org_img)
img = org_img.transpose([2, 1, 0])[None, ...]
img = img.astype(np.float32)
img = torch.from_numpy(img)
img = img / 128.0 - 1.0
if gt.shape[1] == 0:
gt = None
else:
gt = torch.from_numpy(gt)
return {
"scale": s,
"img_path": img_path,
"img": img,
"sol_gt": gt,
"lf_xyrs": positions,
"lf_xyxy": positions_xy,
}
