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* rknn: add text recognition * disable verbose
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. | ||
# | ||
# Licensed under the Apache License, Version 2.0 (the "License"); | ||
# you may not use this file except in compliance with the License. | ||
# You may obtain a copy of the License at | ||
# | ||
# http://www.apache.org/licenses/LICENSE-2.0 | ||
# | ||
# Unless required by applicable law or agreed to in writing, software | ||
# distributed under the License is distributed on an "AS IS" BASIS, | ||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
# See the License for the specific language governing permissions and | ||
# limitations under the License. | ||
""" | ||
This code is refered from: | ||
https://github.com/WenmuZhou/DBNet.pytorch/blob/master/post_processing/seg_detector_representer.py | ||
""" | ||
from __future__ import absolute_import | ||
from __future__ import division | ||
from __future__ import print_function | ||
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import numpy as np | ||
import cv2 | ||
# import paddle | ||
from shapely.geometry import Polygon | ||
import pyclipper | ||
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class DBPostProcess(object): | ||
""" | ||
The post process for Differentiable Binarization (DB). | ||
""" | ||
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def __init__(self, | ||
thresh=0.3, | ||
box_thresh=0.7, | ||
max_candidates=1000, | ||
unclip_ratio=2.0, | ||
use_dilation=False, | ||
score_mode="fast", | ||
**kwargs): | ||
self.thresh = thresh | ||
self.box_thresh = box_thresh | ||
self.max_candidates = max_candidates | ||
self.unclip_ratio = unclip_ratio | ||
self.min_size = 3 | ||
self.score_mode = score_mode | ||
assert score_mode in [ | ||
"slow", "fast" | ||
], "Score mode must be in [slow, fast] but got: {}".format(score_mode) | ||
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self.dilation_kernel = None if not use_dilation else np.array( | ||
[[1, 1], [1, 1]]) | ||
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def boxes_from_bitmap(self, pred, _bitmap, dest_width, dest_height): | ||
''' | ||
_bitmap: single map with shape (1, H, W), | ||
whose values are binarized as {0, 1} | ||
''' | ||
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bitmap = _bitmap | ||
height, width = bitmap.shape | ||
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outs = cv2.findContours((bitmap * 255).astype(np.uint8), cv2.RETR_LIST, | ||
cv2.CHAIN_APPROX_SIMPLE) | ||
if len(outs) == 3: | ||
img, contours, _ = outs[0], outs[1], outs[2] | ||
elif len(outs) == 2: | ||
contours, _ = outs[0], outs[1] | ||
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num_contours = min(len(contours), self.max_candidates) | ||
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boxes = [] | ||
scores = [] | ||
for index in range(num_contours): | ||
contour = contours[index] | ||
points, sside = self.get_mini_boxes(contour) | ||
if sside < self.min_size: | ||
continue | ||
points = np.array(points) | ||
if self.score_mode == "fast": | ||
score = self.box_score_fast(pred, points.reshape(-1, 2)) | ||
else: | ||
score = self.box_score_slow(pred, contour) | ||
if self.box_thresh > score: | ||
continue | ||
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box = self.unclip(points).reshape(-1, 1, 2) | ||
box, sside = self.get_mini_boxes(box) | ||
if sside < self.min_size + 2: | ||
continue | ||
box = np.array(box) | ||
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box[:, 0] = np.clip( | ||
np.round(box[:, 0] / width * dest_width), 0, dest_width) | ||
box[:, 1] = np.clip( | ||
np.round(box[:, 1] / height * dest_height), 0, dest_height) | ||
boxes.append(box.astype(np.int16)) | ||
scores.append(score) | ||
return np.array(boxes, dtype=np.int16), scores | ||
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def unclip(self, box): | ||
unclip_ratio = self.unclip_ratio | ||
poly = Polygon(box) | ||
distance = poly.area * unclip_ratio / poly.length | ||
offset = pyclipper.PyclipperOffset() | ||
offset.AddPath(box, pyclipper.JT_ROUND, pyclipper.ET_CLOSEDPOLYGON) | ||
expanded = np.array(offset.Execute(distance)) | ||
return expanded | ||
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def get_mini_boxes(self, contour): | ||
bounding_box = cv2.minAreaRect(contour) | ||
points = sorted(list(cv2.boxPoints(bounding_box)), key=lambda x: x[0]) | ||
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index_1, index_2, index_3, index_4 = 0, 1, 2, 3 | ||
if points[1][1] > points[0][1]: | ||
index_1 = 0 | ||
index_4 = 1 | ||
else: | ||
index_1 = 1 | ||
index_4 = 0 | ||
if points[3][1] > points[2][1]: | ||
index_2 = 2 | ||
index_3 = 3 | ||
else: | ||
index_2 = 3 | ||
index_3 = 2 | ||
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box = [ | ||
points[index_1], points[index_2], points[index_3], points[index_4] | ||
] | ||
return box, min(bounding_box[1]) | ||
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def box_score_fast(self, bitmap, _box): | ||
''' | ||
box_score_fast: use bbox mean score as the mean score | ||
''' | ||
h, w = bitmap.shape[:2] | ||
box = _box.copy() | ||
xmin = np.clip(np.floor(box[:, 0].min()).astype(np.int32), 0, w - 1) | ||
xmax = np.clip(np.ceil(box[:, 0].max()).astype(np.int32), 0, w - 1) | ||
ymin = np.clip(np.floor(box[:, 1].min()).astype(np.int32), 0, h - 1) | ||
ymax = np.clip(np.ceil(box[:, 1].max()).astype(np.int32), 0, h - 1) | ||
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mask = np.zeros((ymax - ymin + 1, xmax - xmin + 1), dtype=np.uint8) | ||
box[:, 0] = box[:, 0] - xmin | ||
box[:, 1] = box[:, 1] - ymin | ||
cv2.fillPoly(mask, box.reshape(1, -1, 2).astype(np.int32), 1) | ||
return cv2.mean(bitmap[ymin:ymax + 1, xmin:xmax + 1], mask)[0] | ||
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def box_score_slow(self, bitmap, contour): | ||
''' | ||
box_score_slow: use polyon mean score as the mean score | ||
''' | ||
h, w = bitmap.shape[:2] | ||
contour = contour.copy() | ||
contour = np.reshape(contour, (-1, 2)) | ||
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xmin = np.clip(np.min(contour[:, 0]), 0, w - 1) | ||
xmax = np.clip(np.max(contour[:, 0]), 0, w - 1) | ||
ymin = np.clip(np.min(contour[:, 1]), 0, h - 1) | ||
ymax = np.clip(np.max(contour[:, 1]), 0, h - 1) | ||
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mask = np.zeros((ymax - ymin + 1, xmax - xmin + 1), dtype=np.uint8) | ||
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contour[:, 0] = contour[:, 0] - xmin | ||
contour[:, 1] = contour[:, 1] - ymin | ||
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cv2.fillPoly(mask, contour.reshape(1, -1, 2).astype(np.int32), 1) | ||
return cv2.mean(bitmap[ymin:ymax + 1, xmin:xmax + 1], mask)[0] | ||
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def __call__(self, outs_dict, shape_list): | ||
pred = outs_dict['maps'] | ||
# if isinstance(pred, paddle.Tensor): | ||
# pred = pred.numpy() | ||
pred = pred[:, 0, :, :] | ||
segmentation = pred > self.thresh | ||
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boxes_batch = [] | ||
for batch_index in range(pred.shape[0]): | ||
src_h, src_w, ratio_h, ratio_w = shape_list[batch_index] | ||
if self.dilation_kernel is not None: | ||
mask = cv2.dilate( | ||
np.array(segmentation[batch_index]).astype(np.uint8), | ||
self.dilation_kernel) | ||
else: | ||
mask = segmentation[batch_index] | ||
boxes, scores = self.boxes_from_bitmap(pred[batch_index], mask, | ||
src_w, src_h) | ||
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boxes_batch.append({'points': boxes}) | ||
return boxes_batch | ||
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class DistillationDBPostProcess(object): | ||
def __init__(self, | ||
model_name=["student"], | ||
key=None, | ||
thresh=0.3, | ||
box_thresh=0.6, | ||
max_candidates=1000, | ||
unclip_ratio=1.5, | ||
use_dilation=False, | ||
score_mode="fast", | ||
**kwargs): | ||
self.model_name = model_name | ||
self.key = key | ||
self.post_process = DBPostProcess( | ||
thresh=thresh, | ||
box_thresh=box_thresh, | ||
max_candidates=max_candidates, | ||
unclip_ratio=unclip_ratio, | ||
use_dilation=use_dilation, | ||
score_mode=score_mode) | ||
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def __call__(self, predicts, shape_list): | ||
results = {} | ||
for k in self.model_name: | ||
results[k] = self.post_process(predicts[k], shape_list=shape_list) | ||
return results | ||
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class DetPostProcess(object): | ||
def __init__(self) -> None: | ||
pass | ||
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def order_points_clockwise(self, pts): | ||
""" | ||
reference from: https://github.com/jrosebr1/imutils/blob/master/imutils/perspective.py | ||
# sort the points based on their x-coordinates | ||
""" | ||
xSorted = pts[np.argsort(pts[:, 0]), :] | ||
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# grab the left-most and right-most points from the sorted | ||
# x-roodinate points | ||
leftMost = xSorted[:2, :] | ||
rightMost = xSorted[2:, :] | ||
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# now, sort the left-most coordinates according to their | ||
# y-coordinates so we can grab the top-left and bottom-left | ||
# points, respectively | ||
leftMost = leftMost[np.argsort(leftMost[:, 1]), :] | ||
(tl, bl) = leftMost | ||
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rightMost = rightMost[np.argsort(rightMost[:, 1]), :] | ||
(tr, br) = rightMost | ||
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rect = np.array([tl, tr, br, bl], dtype="float32") | ||
return rect | ||
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def clip_det_res(self, points, img_height, img_width): | ||
for pno in range(points.shape[0]): | ||
points[pno, 0] = int(min(max(points[pno, 0], 0), img_width - 1)) | ||
points[pno, 1] = int(min(max(points[pno, 1], 0), img_height - 1)) | ||
return points | ||
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def filter_tag_det_res(self, dt_boxes, image_shape): | ||
img_height, img_width = image_shape[0:2] | ||
dt_boxes_new = [] | ||
for box in dt_boxes: | ||
box = self.order_points_clockwise(box) | ||
box = self.clip_det_res(box, img_height, img_width) | ||
rect_width = int(np.linalg.norm(box[0] - box[1])) | ||
rect_height = int(np.linalg.norm(box[0] - box[3])) | ||
if rect_width <= 3 or rect_height <= 3: | ||
continue | ||
dt_boxes_new.append(box) | ||
dt_boxes = np.array(dt_boxes_new) | ||
return dt_boxes |
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