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east_utils.py
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east_utils.py
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"""
This file contains snippets forked from, https://github.com/argman/EAST,
A tensorflow implementation of EAST text detector.
All credits for these snippets ascribe to argman.
"""
import numpy as np
import cv2
import lanms
def restore_rectangle_rbox(origin, geometry):
d = geometry[:, :4]
angle = geometry[:, 4]
# for angle > 0
origin_0 = origin[angle >= 0]
d_0 = d[angle >= 0]
angle_0 = angle[angle >= 0]
if origin_0.shape[0] > 0:
p = np.array([np.zeros(d_0.shape[0]), -d_0[:, 0] - d_0[:, 2],
d_0[:, 1] + d_0[:, 3], -d_0[:, 0] - d_0[:, 2],
d_0[:, 1] + d_0[:, 3], np.zeros(d_0.shape[0]),
np.zeros(d_0.shape[0]), np.zeros(d_0.shape[0]),
d_0[:, 3], -d_0[:, 2]])
p = p.transpose((1, 0)).reshape((-1, 5, 2)) # N*5*2
rotate_matrix_x = np.array([np.cos(angle_0), np.sin(angle_0)]).transpose((1, 0))
rotate_matrix_x = np.repeat(rotate_matrix_x, 5, axis=1).reshape(-1, 2, 5).transpose((0, 2, 1)) # N*5*2
rotate_matrix_y = np.array([-np.sin(angle_0), np.cos(angle_0)]).transpose((1, 0))
rotate_matrix_y = np.repeat(rotate_matrix_y, 5, axis=1).reshape(-1, 2, 5).transpose((0, 2, 1))
p_rotate_x = np.sum(rotate_matrix_x * p, axis=2)[:, :, np.newaxis] # N*5*1
p_rotate_y = np.sum(rotate_matrix_y * p, axis=2)[:, :, np.newaxis] # N*5*1
p_rotate = np.concatenate([p_rotate_x, p_rotate_y], axis=2) # N*5*2
p3_in_origin = origin_0 - p_rotate[:, 4, :]
new_p0 = p_rotate[:, 0, :] + p3_in_origin # N*2
new_p1 = p_rotate[:, 1, :] + p3_in_origin
new_p2 = p_rotate[:, 2, :] + p3_in_origin
new_p3 = p_rotate[:, 3, :] + p3_in_origin
new_p_0 = np.concatenate([new_p0[:, np.newaxis, :], new_p1[:, np.newaxis, :],
new_p2[:, np.newaxis, :], new_p3[:, np.newaxis, :]], axis=1) # N*4*2
else:
new_p_0 = np.zeros((0, 4, 2))
# for angle < 0
origin_1 = origin[angle < 0]
d_1 = d[angle < 0]
angle_1 = angle[angle < 0]
if origin_1.shape[0] > 0:
p = np.array([-d_1[:, 1] - d_1[:, 3], -d_1[:, 0] - d_1[:, 2],
np.zeros(d_1.shape[0]), -d_1[:, 0] - d_1[:, 2],
np.zeros(d_1.shape[0]), np.zeros(d_1.shape[0]),
-d_1[:, 1] - d_1[:, 3], np.zeros(d_1.shape[0]),
-d_1[:, 1], -d_1[:, 2]])
p = p.transpose((1, 0)).reshape((-1, 5, 2)) # N*5*2
rotate_matrix_x = np.array([np.cos(-angle_1), -np.sin(-angle_1)]).transpose((1, 0))
rotate_matrix_x = np.repeat(rotate_matrix_x, 5, axis=1).reshape(-1, 2, 5).transpose((0, 2, 1)) # N*5*2
rotate_matrix_y = np.array([np.sin(-angle_1), np.cos(-angle_1)]).transpose((1, 0))
rotate_matrix_y = np.repeat(rotate_matrix_y, 5, axis=1).reshape(-1, 2, 5).transpose((0, 2, 1))
p_rotate_x = np.sum(rotate_matrix_x * p, axis=2)[:, :, np.newaxis] # N*5*1
p_rotate_y = np.sum(rotate_matrix_y * p, axis=2)[:, :, np.newaxis] # N*5*1
p_rotate = np.concatenate([p_rotate_x, p_rotate_y], axis=2) # N*5*2
p3_in_origin = origin_1 - p_rotate[:, 4, :]
new_p0 = p_rotate[:, 0, :] + p3_in_origin # N*2
new_p1 = p_rotate[:, 1, :] + p3_in_origin
new_p2 = p_rotate[:, 2, :] + p3_in_origin
new_p3 = p_rotate[:, 3, :] + p3_in_origin
new_p_1 = np.concatenate([new_p0[:, np.newaxis, :], new_p1[:, np.newaxis, :],
new_p2[:, np.newaxis, :], new_p3[:, np.newaxis, :]], axis=1) # N*4*2
else:
new_p_1 = np.zeros((0, 4, 2))
return np.concatenate([new_p_0, new_p_1])
def restore_rectangle(origin, geometry):
return restore_rectangle_rbox(origin, geometry)
def detect(score_map, geo_map, score_map_thresh=0.8, box_thresh=0.1, nms_thres=0.2):
'''
restore text boxes from score map and geo map
:param score_map:
:param geo_map:
:param score_map_thresh: threshhold for score map
:param box_thresh: threshhold for boxes
:param nms_thres: threshold for nms
:return:
'''
if len(score_map.shape) == 4:
score_map = score_map[0, :, :, 0]
geo_map = geo_map[0, :, :, ]
# filter the score map
xy_text = np.argwhere(score_map > score_map_thresh)
# sort the text boxes via the y axis
xy_text = xy_text[np.argsort(xy_text[:, 0])]
# restore
text_box_restored = restore_rectangle(xy_text[:, ::-1]*4, geo_map[xy_text[:, 0], xy_text[:, 1], :]) # N*4*2
print('{} text boxes before nms'.format(text_box_restored.shape[0]))
boxes = np.zeros((text_box_restored.shape[0], 9), dtype=np.float32)
boxes[:, :8] = text_box_restored.reshape((-1, 8))
boxes[:, 8] = score_map[xy_text[:, 0], xy_text[:, 1]]
# boxes = nms_locality.nms_locality(boxes.astype(np.float64), nms_thres)
boxes = lanms.merge_quadrangle_n9(boxes.astype('float32'), nms_thres)
if boxes.shape[0] == 0:
return None
# here we filter some low score boxes by the average score map, this is different from the orginal paper
for i, box in enumerate(boxes):
mask = np.zeros_like(score_map, dtype=np.uint8)
cv2.fillPoly(mask, box[:8].reshape((-1, 4, 2)).astype(np.int32) // 4, 1)
boxes[i, 8] = cv2.mean(score_map, mask)[0]
boxes = boxes[boxes[:, 8] > box_thresh]
return boxes
def sort_poly(p):
min_axis = np.argmin(np.sum(p, axis=1))
p = p[[min_axis, (min_axis+1)%4, (min_axis+2)%4, (min_axis+3)%4]]
if abs(p[0, 0] - p[1, 0]) > abs(p[0, 1] - p[1, 1]):
return p
else:
return p[[0, 3, 2, 1]]
def resize_image(im, max_side_len=2400):
'''
resize image to a size multip4le of 32 which is required by the network
:param im: the resized image
:param max_side_len: limit of max image size to avoid out of memory in gpu
:return: the resized image and the resize ratio
'''
h, w, _ = im.shape
resize_w = w
resize_h = h
# limit the max side
if max(resize_h, resize_w) > max_side_len:
ratio = float(max_side_len) / resize_h if resize_h > resize_w else float(max_side_len) / resize_w
else:
ratio = 1.
resize_h = int(resize_h * ratio)
resize_w = int(resize_w * ratio)
resize_h = resize_h if resize_h % 32 == 0 else (resize_h // 32 - 1) * 32
resize_w = resize_w if resize_w % 32 == 0 else (resize_w // 32 - 1) * 32
resize_h = max(32, resize_h)
resize_w = max(32, resize_w)
im = cv2.resize(im, (int(resize_w), int(resize_h)))
ratio_h = resize_h / float(h)
ratio_w = resize_w / float(w)
return im, (ratio_h, ratio_w)