class TextDetector:
"""
Detect text from an image
"""
def __init__(self, text_proposal_detector):
self.text_proposal_detector = text_proposal_detector
self.text_proposal_connector = TextProposalConnector()
def detect(self, im, sq=False):
"""
Detecting texts from an image
:return: the bounding boxes of the detected texts
"""
text_proposals, scores, sides = self.text_proposal_detector.detect(
im, cfg.MEAN)
keep_inds = np.where(scores > cfg.TEXT_PROPOSALS_MIN_SCORE)[0]
text_proposals, scores, sides = text_proposals[keep_inds], scores[
keep_inds], sides[keep_inds]
sorted_indices = np.argsort(scores.ravel())[::-1]
text_proposals, scores, sides = text_proposals[sorted_indices], scores[
sorted_indices], sides[sorted_indices]
# nms for text proposals
keep_inds = nms(np.hstack((text_proposals, scores)),
cfg.TEXT_PROPOSALS_NMS_THRESH)
text_proposals, scores, sides = text_proposals[keep_inds], scores[
keep_inds], sides[keep_inds]
if sq:
return text_proposals
scores = normalize(scores)
text_lines = self.text_proposal_connector.get_text_lines(
text_proposals, scores, im.shape[:2], sides)
keep_inds = self.filter_boxes(text_lines)
text_lines = text_lines[keep_inds]
# nms for text lines
if text_lines.shape[0] != 0:
keep_inds = nms(text_lines, cfg.TEXT_LINE_NMS_THRESH)
text_lines = text_lines[keep_inds]
return text_lines
def filter_boxes(self, boxes):
heights = boxes[:, 3] - boxes[:, 1] + 1
widths = boxes[:, 2] - boxes[:, 0] + 1
scores = boxes[:, -1]
return np.where(
(widths / heights > cfg.MIN_RATIO) & (scores > cfg.LINE_MIN_SCORE)
& (widths > (cfg.TEXT_PROPOSALS_WIDTH * cfg.MIN_NUM_PROPOSALS)))[0]
class TextDetector:
"""
Detect text from an image
"""
def __init__(self, text_proposal_detector):
self.text_proposal_detector=text_proposal_detector
self.text_proposal_connector=TextProposalConnector()
def detect(self, im):
"""
Detecting texts from an image
:return: the bounding boxes of the detected texts
"""
text_proposals, scores=self.text_proposal_detector.detect(im, cfg.MEAN)
keep_inds=np.where(scores>cfg.TEXT_PROPOSALS_MIN_SCORE)[0]
text_proposals, scores=text_proposals[keep_inds], scores[keep_inds]
sorted_indices=np.argsort(scores.ravel())[::-1]
text_proposals, scores=text_proposals[sorted_indices], scores[sorted_indices]
# nms for text proposals
keep_inds=nms(np.hstack((text_proposals, scores)), cfg.TEXT_PROPOSALS_NMS_THRESH)
text_proposals, scores=text_proposals[keep_inds], scores[keep_inds]
scores=normalize(scores)
text_lines=self.text_proposal_connector.get_text_lines(text_proposals, scores, im.shape[:2])
keep_inds=self.filter_boxes(text_lines)
text_lines=text_lines[keep_inds]
# nms for text lines
if text_lines.shape[0]!=0:
keep_inds=nms(text_lines, cfg.TEXT_LINE_NMS_THRESH)
text_lines=text_lines[keep_inds]
return text_lines
def filter_boxes(self, boxes):
heights=boxes[:, 3]-boxes[:, 1]+1
widths=boxes[:, 2]-boxes[:, 0]+1
scores=boxes[:, -1]
return np.where((widths/heights>cfg.MIN_RATIO) & (scores>cfg.LINE_MIN_SCORE) &
(widths>(cfg.TEXT_PROPOSALS_WIDTH*cfg.MIN_NUM_PROPOSALS)))[0]
def connect_proposal(text_proposals, scores, im_size):
cp = TextProposalConnector()
line = cp.get_text_lines(text_proposals, scores, im_size)
return line
def __init__(self, text_proposal_detector):
self.text_proposal_detector = text_proposal_detector
self.text_proposal_connector = TextProposalConnector()
def __init__(self, text_proposal_detector):
self.text_proposal_detector=text_proposal_detector
self.text_proposal_connector=TextProposalConnector()
def __init__(self):
"""
pass
"""
self.text_proposal_connector=TextProposalConnector()
class LineParser:
"""
parse line in image
"""
def __init__(self, tags='', im_shape=None):
self.tags = tags
self.boxes_connector = TextProposalConnector()
self.im_shape = im_shape
def get_all_boxes(self, tags='', decode_utf8=True):
"""given a label tages, parse all chars contained in it"""
if decode_utf8:
tags = tags.decode('utf-8')
single_char = [char for char in tags.split(';') if char[-1] != '#']
char_boxes = np.zeros((len(single_char), 4), dtype=np.float32)
char_labels = []
for ind, char in enumerate(single_char):
try:
char_boxes[ind] = np.array([
float(char.split(':')[0]),
float(char.split(':')[1]),
float(char.split(':')[2]),
float(char.split(':')[3])
],
dtype=np.float32)
char_labels.append(char.split(':')[-1])
except:
char_labels.append('')
return char_boxes, char_labels
def filter_boxes(self, boxes, boxes_label):
heights = boxes[:, 3] - boxes[:, 1] + 1
widths = boxes[:, 2] - boxes[:, 0] + 1
keep_inds = np.where((widths > Config.BOXES_WIDTH)
& (heights > Config.BOXES_HEIGHT))[0]
boxes = boxes[keep_inds]
boxes_label = [boxes_label[ind] for ind in keep_inds]
return boxes, boxes_label
def filter_lines(self, lines, lines_label):
heights = lines[:, 3] - lines[:, 1] + 1
widths = lines[:, 2] - lines[:, 0] + 1
keep_inds = np.where((widths / heights > Config.MIN_RATIO) & (
widths > (Config.BOXES_WIDTH * Config.MIN_NUM_BOXES)))[0]
lines = lines[keep_inds]
lines_label = [lines_label[ind] for ind in keep_inds]
return lines, lines_label
def gen_line(self):
char_boxes, char_labels = self.get_all_boxes(
self.tags, decode_utf8=Config.decode_utf8)
char_boxes, char_labels = self.filter_boxes(char_boxes, char_labels)
lines, lines_label = self.boxes_connector.get_text_lines(
char_boxes, char_labels, self.im_shape[:2])
lines, lines_label = self.filter_lines(lines, lines_label)
return lines, lines_label
def display_boxes(self, im, boxes, labels):
from matplotlib.font_manager import FontProperties
ChineseFont = FontProperties(
fname='/media/home/netease/PyProjects/test/fonts/SIMYOU.TTF')
im = im[:, :, (2, 1, 0)]
fig, ax = plt.subplots(figsize=(12, 12))
ax.imshow(im, aspect='equal')
for i, box in enumerate(boxes):
bbox = boxes[i, :4]
ax.add_patch(
plt.Rectangle((bbox[0], bbox[1]),
bbox[2] - bbox[0],
bbox[3] - bbox[1],
fill=False,
edgecolor='red',
linewidth=3.5))
ax.text(bbox[0],
bbox[1] - 2,
u'{:s}'.format(labels[i]),
bbox=dict(alpha=0.5),
fontsize=14,
color='white',
fontproperties=ChineseFont)
plt.axis('off')
plt.tight_layout()
plt.draw()
plt.show()
def __init__(self, tags='', im_shape=None):
self.tags = tags
self.boxes_connector = TextProposalConnector()
self.im_shape = im_shape