Merge branch 'refactor' into main
Konstantin Baierer
5 years ago
commit
044ff0c5a2
@ -0,0 +1,28 @@
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version: 2
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jobs:
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build-python36:
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docker:
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- image: python:3.6
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steps:
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- checkout
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- restore_cache:
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keys:
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- model-cache
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- run: make models
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- save_cache:
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key: model-cache
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paths:
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models_eynollah.tar.gz
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models_eynollah
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- run: make install
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- run: make smoke-test
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workflows:
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version: 2
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build:
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jobs:
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- build-python36
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#- build-python37
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#- build-python38 # no tensorflow for python 3.8
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@ -1,2 +1,5 @@
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*.egg-info
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__pycache__
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sbb_newspapers_org_image/pylint.log
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models_eynollah*
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output.html
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@ -0,0 +1,2 @@
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pytest
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black
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@ -1,7 +1,7 @@
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# ocrd includes opencv, numpy, shapely, click
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ocrd >= 2.20.1
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seaborn >= 0.11.0
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keras >= 2.3.1
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scikit-learn >= 0.23.2
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tensorflow >= 1.15, < 2
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tensorflow-gpu >= 1.15, < 2
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imutils >= 0.5.3
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matplotlib
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@ -0,0 +1,107 @@
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import click
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from sbb_newspapers_org_image.eynollah import eynollah
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@click.command()
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@click.option(
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"--image", "-i", help="image filename", type=click.Path(exists=True, dir_okay=False)
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)
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@click.option(
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"--out",
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"-o",
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help="directory to write output xml data",
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type=click.Path(exists=True, file_okay=False),
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)
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@click.option(
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"--model",
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"-m",
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help="directory of models",
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type=click.Path(exists=True, file_okay=False),
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)
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@click.option(
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"--save_images",
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"-si",
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help="if a directory is given, images in documents will be cropped and saved there",
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type=click.Path(exists=True, file_okay=False),
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)
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@click.option(
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"--save_layout",
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"-sl",
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help="if a directory is given, plot of layout will be saved there",
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type=click.Path(exists=True, file_okay=False),
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)
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@click.option(
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"--save_deskewed",
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"-sd",
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help="if a directory is given, deskewed image will be saved there",
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type=click.Path(exists=True, file_okay=False),
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)
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@click.option(
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"--save_all",
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"-sa",
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help="if a directory is given, all plots needed for documentation will be saved there",
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type=click.Path(exists=True, file_okay=False),
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)
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@click.option(
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"--allow_enhancement",
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"-ae",
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is_flag=True,
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help="if this parameter set to true, this tool would check that input image need resizing and enhancement or not. If so output of resized and enhanced image and corresponding layout data will be written in out directory",
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)
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@click.option(
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"--curved_line",
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"-cl",
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is_flag=True,
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help="if this parameter set to true, this tool will try to return contoure of textlines instead of rectabgle bounding box of textline. This should be taken into account that with this option the tool need more time to do process.",
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)
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@click.option(
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"--full_layout",
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"-fl",
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is_flag=True,
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help="if this parameter set to true, this tool will try to return all elements of layout.",
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)
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@click.option(
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"--allow_scaling",
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"-as",
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is_flag=True,
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help="if this parameter set to true, this tool would check the scale and if needed it will scale it to perform better layout detection",
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)
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@click.option(
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"--headers_off",
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"-ho",
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is_flag=True,
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help="if this parameter set to true, this tool would ignore headers role in reading order",
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)
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def main(
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image,
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out,
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model,
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save_images,
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save_layout,
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save_deskewed,
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save_all,
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allow_enhancement,
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curved_line,
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full_layout,
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allow_scaling,
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headers_off,
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):
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eynollah(
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image,
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None,
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out,
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model,
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save_images,
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save_layout,
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save_deskewed,
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save_all,
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allow_enhancement,
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curved_line,
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full_layout,
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allow_scaling,
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headers_off,
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).run()
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if __name__ == "__main__":
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main()
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File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@ -0,0 +1,298 @@
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import cv2
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import numpy as np
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from shapely import geometry
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from .rotate import rotate_image, rotation_image_new
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def contours_in_same_horizon(cy_main_hor):
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X1 = np.zeros((len(cy_main_hor), len(cy_main_hor)))
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X2 = np.zeros((len(cy_main_hor), len(cy_main_hor)))
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X1[0::1, :] = cy_main_hor[:]
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X2 = X1.T
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X_dif = np.abs(X2 - X1)
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args_help = np.array(range(len(cy_main_hor)))
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all_args = []
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for i in range(len(cy_main_hor)):
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list_h = list(args_help[X_dif[i, :] <= 20])
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list_h.append(i)
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if len(list_h) > 1:
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all_args.append(list(set(list_h)))
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return np.unique(all_args)
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def find_contours_mean_y_diff(contours_main):
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M_main = [cv2.moments(contours_main[j]) for j in range(len(contours_main))]
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cy_main = [(M_main[j]["m01"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))]
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return np.mean(np.diff(np.sort(np.array(cy_main))))
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def find_features_of_contours(contours_main):
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areas_main = np.array([cv2.contourArea(contours_main[j]) for j in range(len(contours_main))])
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M_main = [cv2.moments(contours_main[j]) for j in range(len(contours_main))]
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cx_main = [(M_main[j]["m10"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))]
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cy_main = [(M_main[j]["m01"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))]
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x_min_main = np.array([np.min(contours_main[j][:, 0, 0]) for j in range(len(contours_main))])
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x_max_main = np.array([np.max(contours_main[j][:, 0, 0]) for j in range(len(contours_main))])
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y_min_main = np.array([np.min(contours_main[j][:, 0, 1]) for j in range(len(contours_main))])
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y_max_main = np.array([np.max(contours_main[j][:, 0, 1]) for j in range(len(contours_main))])
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return y_min_main, y_max_main, areas_main
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def return_contours_of_interested_region_and_bounding_box(region_pre_p, pixel):
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# pixels of images are identified by 5
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cnts_images = (region_pre_p[:, :, 0] == pixel) * 1
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cnts_images = cnts_images.astype(np.uint8)
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cnts_images = np.repeat(cnts_images[:, :, np.newaxis], 3, axis=2)
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imgray = cv2.cvtColor(cnts_images, cv2.COLOR_BGR2GRAY)
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ret, thresh = cv2.threshold(imgray, 0, 255, 0)
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contours_imgs, hiearchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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contours_imgs = return_parent_contours(contours_imgs, hiearchy)
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contours_imgs = filter_contours_area_of_image_tables(thresh, contours_imgs, hiearchy, max_area=1, min_area=0.0003)
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boxes = []
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for jj in range(len(contours_imgs)):
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x, y, w, h = cv2.boundingRect(contours_imgs[jj])
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boxes.append([int(x), int(y), int(w), int(h)])
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return contours_imgs, boxes
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def get_text_region_boxes_by_given_contours(contours):
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kernel = np.ones((5, 5), np.uint8)
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boxes = []
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contours_new = []
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for jj in range(len(contours)):
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x, y, w, h = cv2.boundingRect(contours[jj])
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boxes.append([x, y, w, h])
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contours_new.append(contours[jj])
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del contours
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return boxes, contours_new
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def filter_contours_area_of_image(image, contours, hirarchy, max_area, min_area):
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found_polygons_early = list()
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jv = 0
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for c in contours:
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if len(c) < 3: # A polygon cannot have less than 3 points
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continue
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polygon = geometry.Polygon([point[0] for point in c])
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area = polygon.area
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if area >= min_area * np.prod(image.shape[:2]) and area <= max_area * np.prod(image.shape[:2]) and hirarchy[0][jv][3] == -1: # and hirarchy[0][jv][3]==-1 :
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found_polygons_early.append(np.array([[point] for point in polygon.exterior.coords], dtype=np.uint))
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jv += 1
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return found_polygons_early
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def filter_contours_area_of_image_interiors(image, contours, hirarchy, max_area, min_area):
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found_polygons_early = list()
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jv = 0
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for c in contours:
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if len(c) < 3: # A polygon cannot have less than 3 points
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continue
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polygon = geometry.Polygon([point[0] for point in c])
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area = polygon.area
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if area >= min_area * np.prod(image.shape[:2]) and area <= max_area * np.prod(image.shape[:2]) and hirarchy[0][jv][3] != -1:
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# print(c[0][0][1])
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found_polygons_early.append(np.array([point for point in polygon.exterior.coords], dtype=np.uint))
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jv += 1
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return found_polygons_early
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def filter_contours_area_of_image_tables(image, contours, hirarchy, max_area, min_area):
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found_polygons_early = list()
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jv = 0
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for c in contours:
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if len(c) < 3: # A polygon cannot have less than 3 points
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continue
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polygon = geometry.Polygon([point[0] for point in c])
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# area = cv2.contourArea(c)
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area = polygon.area
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##print(np.prod(thresh.shape[:2]))
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# Check that polygon has area greater than minimal area
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# print(hirarchy[0][jv][3],hirarchy )
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if area >= min_area * np.prod(image.shape[:2]) and area <= max_area * np.prod(image.shape[:2]): # and hirarchy[0][jv][3]==-1 :
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# print(c[0][0][1])
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found_polygons_early.append(np.array([[point] for point in polygon.exterior.coords], dtype=np.int32))
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jv += 1
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return found_polygons_early
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def find_new_features_of_contoures(contours_main):
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areas_main = np.array([cv2.contourArea(contours_main[j]) for j in range(len(contours_main))])
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M_main = [cv2.moments(contours_main[j]) for j in range(len(contours_main))]
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cx_main = [(M_main[j]["m10"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))]
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cy_main = [(M_main[j]["m01"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))]
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try:
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x_min_main = np.array([np.min(contours_main[j][:, 0, 0]) for j in range(len(contours_main))])
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argmin_x_main = np.array([np.argmin(contours_main[j][:, 0, 0]) for j in range(len(contours_main))])
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x_min_from_argmin = np.array([contours_main[j][argmin_x_main[j], 0, 0] for j in range(len(contours_main))])
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y_corr_x_min_from_argmin = np.array([contours_main[j][argmin_x_main[j], 0, 1] for j in range(len(contours_main))])
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x_max_main = np.array([np.max(contours_main[j][:, 0, 0]) for j in range(len(contours_main))])
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y_min_main = np.array([np.min(contours_main[j][:, 0, 1]) for j in range(len(contours_main))])
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y_max_main = np.array([np.max(contours_main[j][:, 0, 1]) for j in range(len(contours_main))])
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except:
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x_min_main = np.array([np.min(contours_main[j][:, 0]) for j in range(len(contours_main))])
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argmin_x_main = np.array([np.argmin(contours_main[j][:, 0]) for j in range(len(contours_main))])
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x_min_from_argmin = np.array([contours_main[j][argmin_x_main[j], 0] for j in range(len(contours_main))])
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y_corr_x_min_from_argmin = np.array([contours_main[j][argmin_x_main[j], 1] for j in range(len(contours_main))])
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x_max_main = np.array([np.max(contours_main[j][:, 0]) for j in range(len(contours_main))])
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y_min_main = np.array([np.min(contours_main[j][:, 1]) for j in range(len(contours_main))])
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y_max_main = np.array([np.max(contours_main[j][:, 1]) for j in range(len(contours_main))])
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# dis_x=np.abs(x_max_main-x_min_main)
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return cx_main, cy_main, x_min_main, x_max_main, y_min_main, y_max_main, y_corr_x_min_from_argmin
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def return_parent_contours(contours, hierarchy):
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contours_parent = [contours[i] for i in range(len(contours)) if hierarchy[0][i][3] == -1]
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return contours_parent
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def return_contours_of_interested_region(region_pre_p, pixel, min_area=0.0002):
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# pixels of images are identified by 5
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if len(region_pre_p.shape) == 3:
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cnts_images = (region_pre_p[:, :, 0] == pixel) * 1
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else:
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cnts_images = (region_pre_p[:, :] == pixel) * 1
|
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cnts_images = cnts_images.astype(np.uint8)
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cnts_images = np.repeat(cnts_images[:, :, np.newaxis], 3, axis=2)
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imgray = cv2.cvtColor(cnts_images, cv2.COLOR_BGR2GRAY)
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ret, thresh = cv2.threshold(imgray, 0, 255, 0)
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contours_imgs, hiearchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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contours_imgs = return_parent_contours(contours_imgs, hiearchy)
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contours_imgs = filter_contours_area_of_image_tables(thresh, contours_imgs, hiearchy, max_area=1, min_area=min_area)
|
||||
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return contours_imgs
|
||||
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||||
def get_textregion_contours_in_org_image(cnts, img, slope_first):
|
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cnts_org = []
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# print(cnts,'cnts')
|
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for i in range(len(cnts)):
|
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img_copy = np.zeros(img.shape)
|
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img_copy = cv2.fillPoly(img_copy, pts=[cnts[i]], color=(1, 1, 1))
|
||||
|
||||
# plt.imshow(img_copy)
|
||||
# plt.show()
|
||||
|
||||
# print(img.shape,'img')
|
||||
img_copy = rotation_image_new(img_copy, -slope_first)
|
||||
##print(img_copy.shape,'img_copy')
|
||||
# plt.imshow(img_copy)
|
||||
# plt.show()
|
||||
|
||||
img_copy = img_copy.astype(np.uint8)
|
||||
imgray = cv2.cvtColor(img_copy, cv2.COLOR_BGR2GRAY)
|
||||
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
||||
|
||||
cont_int, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
||||
|
||||
cont_int[0][:, 0, 0] = cont_int[0][:, 0, 0] + np.abs(img_copy.shape[1] - img.shape[1])
|
||||
cont_int[0][:, 0, 1] = cont_int[0][:, 0, 1] + np.abs(img_copy.shape[0] - img.shape[0])
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# print(np.shape(cont_int[0]))
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||||
cnts_org.append(cont_int[0])
|
||||
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||||
# print(cnts_org,'cnts_org')
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||||
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# sys.exit()
|
||||
# self.y_shift = np.abs(img_copy.shape[0] - img.shape[0])
|
||||
# self.x_shift = np.abs(img_copy.shape[1] - img.shape[1])
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||||
return cnts_org
|
||||
|
||||
def return_contours_of_interested_textline(region_pre_p, pixel):
|
||||
|
||||
# pixels of images are identified by 5
|
||||
if len(region_pre_p.shape) == 3:
|
||||
cnts_images = (region_pre_p[:, :, 0] == pixel) * 1
|
||||
else:
|
||||
cnts_images = (region_pre_p[:, :] == pixel) * 1
|
||||
cnts_images = cnts_images.astype(np.uint8)
|
||||
cnts_images = np.repeat(cnts_images[:, :, np.newaxis], 3, axis=2)
|
||||
imgray = cv2.cvtColor(cnts_images, cv2.COLOR_BGR2GRAY)
|
||||
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
||||
contours_imgs, hiearchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
||||
|
||||
contours_imgs = return_parent_contours(contours_imgs, hiearchy)
|
||||
contours_imgs = filter_contours_area_of_image_tables(thresh, contours_imgs, hiearchy, max_area=1, min_area=0.000000003)
|
||||
return contours_imgs
|
||||
|
||||
def return_bonding_box_of_contours(cnts):
|
||||
boxes_tot = []
|
||||
for i in range(len(cnts)):
|
||||
x, y, w, h = cv2.boundingRect(cnts[i])
|
||||
|
||||
box = [x, y, w, h]
|
||||
boxes_tot.append(box)
|
||||
return boxes_tot
|
||||
|
||||
def return_contours_of_image(image):
|
||||
|
||||
if len(image.shape) == 2:
|
||||
image = np.repeat(image[:, :, np.newaxis], 3, axis=2)
|
||||
image = image.astype(np.uint8)
|
||||
else:
|
||||
image = image.astype(np.uint8)
|
||||
imgray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
|
||||
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
||||
contours, hierachy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
||||
return contours, hierachy
|
||||
|
||||
def return_contours_of_interested_region_by_min_size(region_pre_p, pixel, min_size=0.00003):
|
||||
|
||||
# pixels of images are identified by 5
|
||||
if len(region_pre_p.shape) == 3:
|
||||
cnts_images = (region_pre_p[:, :, 0] == pixel) * 1
|
||||
else:
|
||||
cnts_images = (region_pre_p[:, :] == pixel) * 1
|
||||
cnts_images = cnts_images.astype(np.uint8)
|
||||
cnts_images = np.repeat(cnts_images[:, :, np.newaxis], 3, axis=2)
|
||||
imgray = cv2.cvtColor(cnts_images, cv2.COLOR_BGR2GRAY)
|
||||
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
||||
|
||||
contours_imgs, hiearchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
||||
|
||||
contours_imgs = return_parent_contours(contours_imgs, hiearchy)
|
||||
contours_imgs = filter_contours_area_of_image_tables(thresh, contours_imgs, hiearchy, max_area=1, min_area=min_size)
|
||||
|
||||
return contours_imgs
|
||||
|
||||
def return_contours_of_interested_region_by_size(region_pre_p, pixel, min_area, max_area):
|
||||
|
||||
# pixels of images are identified by 5
|
||||
if len(region_pre_p.shape) == 3:
|
||||
cnts_images = (region_pre_p[:, :, 0] == pixel) * 1
|
||||
else:
|
||||
cnts_images = (region_pre_p[:, :] == pixel) * 1
|
||||
cnts_images = cnts_images.astype(np.uint8)
|
||||
cnts_images = np.repeat(cnts_images[:, :, np.newaxis], 3, axis=2)
|
||||
imgray = cv2.cvtColor(cnts_images, cv2.COLOR_BGR2GRAY)
|
||||
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
||||
contours_imgs, hiearchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
||||
|
||||
contours_imgs = return_parent_contours(contours_imgs, hiearchy)
|
||||
contours_imgs = filter_contours_area_of_image_tables(thresh, contours_imgs, hiearchy, max_area=max_area, min_area=min_area)
|
||||
|
||||
img_ret = np.zeros((region_pre_p.shape[0], region_pre_p.shape[1], 3))
|
||||
img_ret = cv2.fillPoly(img_ret, pts=contours_imgs, color=(1, 1, 1))
|
||||
return img_ret[:, :, 0]
|
||||
|
||||
@ -0,0 +1,501 @@
|
||||
import numpy as np
|
||||
import cv2
|
||||
from .contour import (
|
||||
find_new_features_of_contoures,
|
||||
return_contours_of_image,
|
||||
return_parent_contours,
|
||||
)
|
||||
|
||||
def adhere_drop_capital_region_into_cprresponding_textline(
|
||||
text_regions_p,
|
||||
polygons_of_drop_capitals,
|
||||
contours_only_text_parent,
|
||||
contours_only_text_parent_h,
|
||||
all_box_coord,
|
||||
all_box_coord_h,
|
||||
all_found_texline_polygons,
|
||||
all_found_texline_polygons_h,
|
||||
kernel=None,
|
||||
curved_line=False,
|
||||
):
|
||||
# print(np.shape(all_found_texline_polygons),np.shape(all_found_texline_polygons[3]),'all_found_texline_polygonsshape')
|
||||
# print(all_found_texline_polygons[3])
|
||||
cx_m, cy_m, _, _, _, _, _ = find_new_features_of_contoures(contours_only_text_parent)
|
||||
cx_h, cy_h, _, _, _, _, _ = find_new_features_of_contoures(contours_only_text_parent_h)
|
||||
cx_d, cy_d, _, _, y_min_d, y_max_d, _ = find_new_features_of_contoures(polygons_of_drop_capitals)
|
||||
|
||||
img_con_all = np.zeros((text_regions_p.shape[0], text_regions_p.shape[1], 3))
|
||||
for j_cont in range(len(contours_only_text_parent)):
|
||||
img_con_all[all_box_coord[j_cont][0] : all_box_coord[j_cont][1], all_box_coord[j_cont][2] : all_box_coord[j_cont][3], 0] = (j_cont + 1) * 3
|
||||
# img_con_all=cv2.fillPoly(img_con_all,pts=[contours_only_text_parent[j_cont]],color=((j_cont+1)*3,(j_cont+1)*3,(j_cont+1)*3))
|
||||
|
||||
# plt.imshow(img_con_all[:,:,0])
|
||||
# plt.show()
|
||||
# img_con_all=cv2.dilate(img_con_all, kernel, iterations=3)
|
||||
|
||||
# plt.imshow(img_con_all[:,:,0])
|
||||
# plt.show()
|
||||
# print(np.unique(img_con_all[:,:,0]))
|
||||
for i_drop in range(len(polygons_of_drop_capitals)):
|
||||
# print(i_drop,'i_drop')
|
||||
img_con_all_copy = np.copy(img_con_all)
|
||||
img_con = np.zeros((text_regions_p.shape[0], text_regions_p.shape[1], 3))
|
||||
img_con = cv2.fillPoly(img_con, pts=[polygons_of_drop_capitals[i_drop]], color=(1, 1, 1))
|
||||
|
||||
# plt.imshow(img_con[:,:,0])
|
||||
# plt.show()
|
||||
##img_con=cv2.dilate(img_con, kernel, iterations=30)
|
||||
|
||||
# plt.imshow(img_con[:,:,0])
|
||||
# plt.show()
|
||||
|
||||
# print(np.unique(img_con[:,:,0]))
|
||||
|
||||
img_con_all_copy[:, :, 0] = img_con_all_copy[:, :, 0] + img_con[:, :, 0]
|
||||
|
||||
img_con_all_copy[:, :, 0][img_con_all_copy[:, :, 0] == 1] = 0
|
||||
|
||||
kherej_ghesmat = np.unique(img_con_all_copy[:, :, 0]) / 3
|
||||
res_summed_pixels = np.unique(img_con_all_copy[:, :, 0]) % 3
|
||||
region_with_intersected_drop = kherej_ghesmat[res_summed_pixels == 1]
|
||||
# region_with_intersected_drop=region_with_intersected_drop/3
|
||||
region_with_intersected_drop = region_with_intersected_drop.astype(np.uint8)
|
||||
|
||||
# print(len(region_with_intersected_drop),'region_with_intersected_drop1')
|
||||
if len(region_with_intersected_drop) == 0:
|
||||
img_con_all_copy = np.copy(img_con_all)
|
||||
img_con = cv2.dilate(img_con, kernel, iterations=4)
|
||||
|
||||
img_con_all_copy[:, :, 0] = img_con_all_copy[:, :, 0] + img_con[:, :, 0]
|
||||
|
||||
img_con_all_copy[:, :, 0][img_con_all_copy[:, :, 0] == 1] = 0
|
||||
|
||||
kherej_ghesmat = np.unique(img_con_all_copy[:, :, 0]) / 3
|
||||
res_summed_pixels = np.unique(img_con_all_copy[:, :, 0]) % 3
|
||||
region_with_intersected_drop = kherej_ghesmat[res_summed_pixels == 1]
|
||||
# region_with_intersected_drop=region_with_intersected_drop/3
|
||||
region_with_intersected_drop = region_with_intersected_drop.astype(np.uint8)
|
||||
# print(np.unique(img_con_all_copy[:,:,0]))
|
||||
if curved_line:
|
||||
|
||||
if len(region_with_intersected_drop) > 1:
|
||||
sum_pixels_of_intersection = []
|
||||
for i in range(len(region_with_intersected_drop)):
|
||||
# print((region_with_intersected_drop[i]*3+1))
|
||||
sum_pixels_of_intersection.append(((img_con_all_copy[:, :, 0] == (region_with_intersected_drop[i] * 3 + 1)) * 1).sum())
|
||||
# print(sum_pixels_of_intersection)
|
||||
region_final = region_with_intersected_drop[np.argmax(sum_pixels_of_intersection)] - 1
|
||||
|
||||
# print(region_final,'region_final')
|
||||
# cx_t,cy_t ,_, _, _ ,_,_= find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
|
||||
try:
|
||||
cx_t, cy_t, _, _, _, _, _ = find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
|
||||
# print(all_box_coord[j_cont])
|
||||
# print(cx_t)
|
||||
# print(cy_t)
|
||||
# print(cx_d[i_drop])
|
||||
# print(cy_d[i_drop])
|
||||
y_lines = np.array(cy_t) # all_box_coord[int(region_final)][0]+np.array(cy_t)
|
||||
|
||||
# print(y_lines)
|
||||
|
||||
y_lines[y_lines < y_min_d[i_drop]] = 0
|
||||
# print(y_lines)
|
||||
|
||||
arg_min = np.argmin(np.abs(y_lines - y_min_d[i_drop]))
|
||||
# print(arg_min)
|
||||
|
||||
cnt_nearest = np.copy(all_found_texline_polygons[int(region_final)][arg_min])
|
||||
cnt_nearest[:, 0, 0] = all_found_texline_polygons[int(region_final)][arg_min][:, 0, 0] # +all_box_coord[int(region_final)][2]
|
||||
cnt_nearest[:, 0, 1] = all_found_texline_polygons[int(region_final)][arg_min][:, 0, 1] # +all_box_coord[int(region_final)][0]
|
||||
|
||||
img_textlines = np.zeros((text_regions_p.shape[0], text_regions_p.shape[1], 3))
|
||||
img_textlines = cv2.fillPoly(img_textlines, pts=[cnt_nearest], color=(255, 255, 255))
|
||||
img_textlines = cv2.fillPoly(img_textlines, pts=[polygons_of_drop_capitals[i_drop]], color=(255, 255, 255))
|
||||
|
||||
img_textlines = img_textlines.astype(np.uint8)
|
||||
imgray = cv2.cvtColor(img_textlines, cv2.COLOR_BGR2GRAY)
|
||||
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
||||
|
||||
contours_combined, hierachy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
||||
|
||||
# print(len(contours_combined),'len textlines mixed')
|
||||
areas_cnt_text = np.array([cv2.contourArea(contours_combined[j]) for j in range(len(contours_combined))])
|
||||
|
||||
contours_biggest = contours_combined[np.argmax(areas_cnt_text)]
|
||||
|
||||
# print(np.shape(contours_biggest))
|
||||
# print(contours_biggest[:])
|
||||
# contours_biggest[:,0,0]=contours_biggest[:,0,0]#-all_box_coord[int(region_final)][2]
|
||||
# contours_biggest[:,0,1]=contours_biggest[:,0,1]#-all_box_coord[int(region_final)][0]
|
||||
|
||||
# contours_biggest=contours_biggest.reshape(np.shape(contours_biggest)[0],np.shape(contours_biggest)[2])
|
||||
|
||||
all_found_texline_polygons[int(region_final)][arg_min] = contours_biggest
|
||||
|
||||
except:
|
||||
# print('gordun1')
|
||||
pass
|
||||
elif len(region_with_intersected_drop) == 1:
|
||||
region_final = region_with_intersected_drop[0] - 1
|
||||
|
||||
# areas_main=np.array([cv2.contourArea(all_found_texline_polygons[int(region_final)][0][j] ) for j in range(len(all_found_texline_polygons[int(region_final)]))])
|
||||
|
||||
# cx_t,cy_t ,_, _, _ ,_,_= find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
|
||||
|
||||
cx_t, cy_t, _, _, _, _, _ = find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
|
||||
# print(all_box_coord[j_cont])
|
||||
# print(cx_t)
|
||||
# print(cy_t)
|
||||
# print(cx_d[i_drop])
|
||||
# print(cy_d[i_drop])
|
||||
y_lines = np.array(cy_t) # all_box_coord[int(region_final)][0]+np.array(cy_t)
|
||||
|
||||
y_lines[y_lines < y_min_d[i_drop]] = 0
|
||||
# print(y_lines)
|
||||
|
||||
arg_min = np.argmin(np.abs(y_lines - y_min_d[i_drop]))
|
||||
# print(arg_min)
|
||||
|
||||
cnt_nearest = np.copy(all_found_texline_polygons[int(region_final)][arg_min])
|
||||
cnt_nearest[:, 0, 0] = all_found_texline_polygons[int(region_final)][arg_min][:, 0, 0] # +all_box_coord[int(region_final)][2]
|
||||
cnt_nearest[:, 0, 1] = all_found_texline_polygons[int(region_final)][arg_min][:, 0, 1] # +all_box_coord[int(region_final)][0]
|
||||
|
||||
img_textlines = np.zeros((text_regions_p.shape[0], text_regions_p.shape[1], 3))
|
||||
img_textlines = cv2.fillPoly(img_textlines, pts=[cnt_nearest], color=(255, 255, 255))
|
||||
img_textlines = cv2.fillPoly(img_textlines, pts=[polygons_of_drop_capitals[i_drop]], color=(255, 255, 255))
|
||||
|
||||
img_textlines = img_textlines.astype(np.uint8)
|
||||
|
||||
# plt.imshow(img_textlines)
|
||||
# plt.show()
|
||||
imgray = cv2.cvtColor(img_textlines, cv2.COLOR_BGR2GRAY)
|
||||
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
||||
|
||||
contours_combined, hierachy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
||||
|
||||
# print(len(contours_combined),'len textlines mixed')
|
||||
areas_cnt_text = np.array([cv2.contourArea(contours_combined[j]) for j in range(len(contours_combined))])
|
||||
|
||||
contours_biggest = contours_combined[np.argmax(areas_cnt_text)]
|
||||
|
||||
# print(np.shape(contours_biggest))
|
||||
# print(contours_biggest[:])
|
||||
# contours_biggest[:,0,0]=contours_biggest[:,0,0]#-all_box_coord[int(region_final)][2]
|
||||
# contours_biggest[:,0,1]=contours_biggest[:,0,1]#-all_box_coord[int(region_final)][0]
|
||||
# print(np.shape(contours_biggest),'contours_biggest')
|
||||
# print(np.shape(all_found_texline_polygons[int(region_final)][arg_min]))
|
||||
##contours_biggest=contours_biggest.reshape(np.shape(contours_biggest)[0],np.shape(contours_biggest)[2])
|
||||
all_found_texline_polygons[int(region_final)][arg_min] = contours_biggest
|
||||
|
||||
# print(cx_t,'print')
|
||||
try:
|
||||
# print(all_found_texline_polygons[j_cont][0])
|
||||
cx_t, cy_t, _, _, _, _, _ = find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
|
||||
# print(all_box_coord[j_cont])
|
||||
# print(cx_t)
|
||||
# print(cy_t)
|
||||
# print(cx_d[i_drop])
|
||||
# print(cy_d[i_drop])
|
||||
y_lines = all_box_coord[int(region_final)][0] + np.array(cy_t)
|
||||
|
||||
y_lines[y_lines < y_min_d[i_drop]] = 0
|
||||
# print(y_lines)
|
||||
|
||||
arg_min = np.argmin(np.abs(y_lines - y_min_d[i_drop]))
|
||||
# print(arg_min)
|
||||
|
||||
cnt_nearest = np.copy(all_found_texline_polygons[int(region_final)][arg_min])
|
||||
cnt_nearest[:, 0, 0] = all_found_texline_polygons[int(region_final)][arg_min][:, 0, 0] # +all_box_coord[int(region_final)][2]
|
||||
cnt_nearest[:, 0, 1] = all_found_texline_polygons[int(region_final)][arg_min][:, 0, 1] # +all_box_coord[int(region_final)][0]
|
||||
|
||||
img_textlines = np.zeros((text_regions_p.shape[0], text_regions_p.shape[1], 3))
|
||||
img_textlines = cv2.fillPoly(img_textlines, pts=[cnt_nearest], color=(255, 255, 255))
|
||||
img_textlines = cv2.fillPoly(img_textlines, pts=[polygons_of_drop_capitals[i_drop]], color=(255, 255, 255))
|
||||
|
||||
img_textlines = img_textlines.astype(np.uint8)
|
||||
imgray = cv2.cvtColor(img_textlines, cv2.COLOR_BGR2GRAY)
|
||||
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
||||
|
||||
contours_combined, hierachy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
||||
|
||||
# print(len(contours_combined),'len textlines mixed')
|
||||
areas_cnt_text = np.array([cv2.contourArea(contours_combined[j]) for j in range(len(contours_combined))])
|
||||
|
||||
contours_biggest = contours_combined[np.argmax(areas_cnt_text)]
|
||||
|
||||
# print(np.shape(contours_biggest))
|
||||
# print(contours_biggest[:])
|
||||
contours_biggest[:, 0, 0] = contours_biggest[:, 0, 0] # -all_box_coord[int(region_final)][2]
|
||||
contours_biggest[:, 0, 1] = contours_biggest[:, 0, 1] # -all_box_coord[int(region_final)][0]
|
||||
|
||||
##contours_biggest=contours_biggest.reshape(np.shape(contours_biggest)[0],np.shape(contours_biggest)[2])
|
||||
all_found_texline_polygons[int(region_final)][arg_min] = contours_biggest
|
||||
# all_found_texline_polygons[int(region_final)][arg_min]=contours_biggest
|
||||
|
||||
except:
|
||||
pass
|
||||
else:
|
||||
pass
|
||||
|
||||
##cx_t,cy_t ,_, _, _ ,_,_= find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
|
||||
###print(all_box_coord[j_cont])
|
||||
###print(cx_t)
|
||||
###print(cy_t)
|
||||
###print(cx_d[i_drop])
|
||||
###print(cy_d[i_drop])
|
||||
##y_lines=all_box_coord[int(region_final)][0]+np.array(cy_t)
|
||||
|
||||
##y_lines[y_lines<y_min_d[i_drop]]=0
|
||||
###print(y_lines)
|
||||
|
||||
##arg_min=np.argmin(np.abs(y_lines-y_min_d[i_drop]) )
|
||||
###print(arg_min)
|
||||
|
||||
##cnt_nearest=np.copy(all_found_texline_polygons[int(region_final)][arg_min])
|
||||
##cnt_nearest[:,0,0]=all_found_texline_polygons[int(region_final)][arg_min][:,0,0]#+all_box_coord[int(region_final)][2]
|
||||
##cnt_nearest[:,0,1]=all_found_texline_polygons[int(region_final)][arg_min][:,0,1]#+all_box_coord[int(region_final)][0]
|
||||
|
||||
##img_textlines=np.zeros((text_regions_p.shape[0],text_regions_p.shape[1],3))
|
||||
##img_textlines=cv2.fillPoly(img_textlines,pts=[cnt_nearest],color=(255,255,255))
|
||||
##img_textlines=cv2.fillPoly(img_textlines,pts=[polygons_of_drop_capitals[i_drop] ],color=(255,255,255))
|
||||
|
||||
##img_textlines=img_textlines.astype(np.uint8)
|
||||
|
||||
##plt.imshow(img_textlines)
|
||||
##plt.show()
|
||||
##imgray = cv2.cvtColor(img_textlines, cv2.COLOR_BGR2GRAY)
|
||||
##ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
||||
|
||||
##contours_combined,hierachy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
|
||||
|
||||
##print(len(contours_combined),'len textlines mixed')
|
||||
##areas_cnt_text=np.array([cv2.contourArea(contours_combined[j]) for j in range(len(contours_combined))])
|
||||
|
||||
##contours_biggest=contours_combined[np.argmax(areas_cnt_text)]
|
||||
|
||||
###print(np.shape(contours_biggest))
|
||||
###print(contours_biggest[:])
|
||||
##contours_biggest[:,0,0]=contours_biggest[:,0,0]#-all_box_coord[int(region_final)][2]
|
||||
##contours_biggest[:,0,1]=contours_biggest[:,0,1]#-all_box_coord[int(region_final)][0]
|
||||
|
||||
##contours_biggest=contours_biggest.reshape(np.shape(contours_biggest)[0],np.shape(contours_biggest)[2])
|
||||
##all_found_texline_polygons[int(region_final)][arg_min]=contours_biggest
|
||||
|
||||
else:
|
||||
if len(region_with_intersected_drop) > 1:
|
||||
sum_pixels_of_intersection = []
|
||||
for i in range(len(region_with_intersected_drop)):
|
||||
# print((region_with_intersected_drop[i]*3+1))
|
||||
sum_pixels_of_intersection.append(((img_con_all_copy[:, :, 0] == (region_with_intersected_drop[i] * 3 + 1)) * 1).sum())
|
||||
# print(sum_pixels_of_intersection)
|
||||
region_final = region_with_intersected_drop[np.argmax(sum_pixels_of_intersection)] - 1
|
||||
|
||||
# print(region_final,'region_final')
|
||||
# cx_t,cy_t ,_, _, _ ,_,_= find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
|
||||
try:
|
||||
cx_t, cy_t, _, _, _, _, _ = find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
|
||||
# print(all_box_coord[j_cont])
|
||||
# print(cx_t)
|
||||
# print(cy_t)
|
||||
# print(cx_d[i_drop])
|
||||
# print(cy_d[i_drop])
|
||||
y_lines = all_box_coord[int(region_final)][0] + np.array(cy_t)
|
||||
|
||||
# print(y_lines)
|
||||
|
||||
y_lines[y_lines < y_min_d[i_drop]] = 0
|
||||
# print(y_lines)
|
||||
|
||||
arg_min = np.argmin(np.abs(y_lines - y_min_d[i_drop]))
|
||||
# print(arg_min)
|
||||
|
||||
cnt_nearest = np.copy(all_found_texline_polygons[int(region_final)][arg_min])
|
||||
cnt_nearest[:, 0] = all_found_texline_polygons[int(region_final)][arg_min][:, 0] + all_box_coord[int(region_final)][2]
|
||||
cnt_nearest[:, 1] = all_found_texline_polygons[int(region_final)][arg_min][:, 1] + all_box_coord[int(region_final)][0]
|
||||
|
||||
img_textlines = np.zeros((text_regions_p.shape[0], text_regions_p.shape[1], 3))
|
||||
img_textlines = cv2.fillPoly(img_textlines, pts=[cnt_nearest], color=(255, 255, 255))
|
||||
img_textlines = cv2.fillPoly(img_textlines, pts=[polygons_of_drop_capitals[i_drop]], color=(255, 255, 255))
|
||||
|
||||
img_textlines = img_textlines.astype(np.uint8)
|
||||
imgray = cv2.cvtColor(img_textlines, cv2.COLOR_BGR2GRAY)
|
||||
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
||||
|
||||
contours_combined, hierachy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
||||
|
||||
# print(len(contours_combined),'len textlines mixed')
|
||||
areas_cnt_text = np.array([cv2.contourArea(contours_combined[j]) for j in range(len(contours_combined))])
|
||||
|
||||
contours_biggest = contours_combined[np.argmax(areas_cnt_text)]
|
||||
|
||||
# print(np.shape(contours_biggest))
|
||||
# print(contours_biggest[:])
|
||||
contours_biggest[:, 0, 0] = contours_biggest[:, 0, 0] - all_box_coord[int(region_final)][2]
|
||||
contours_biggest[:, 0, 1] = contours_biggest[:, 0, 1] - all_box_coord[int(region_final)][0]
|
||||
|
||||
contours_biggest = contours_biggest.reshape(np.shape(contours_biggest)[0], np.shape(contours_biggest)[2])
|
||||
|
||||
all_found_texline_polygons[int(region_final)][arg_min] = contours_biggest
|
||||
|
||||
except:
|
||||
# print('gordun1')
|
||||
pass
|
||||
elif len(region_with_intersected_drop) == 1:
|
||||
region_final = region_with_intersected_drop[0] - 1
|
||||
|
||||
# areas_main=np.array([cv2.contourArea(all_found_texline_polygons[int(region_final)][0][j] ) for j in range(len(all_found_texline_polygons[int(region_final)]))])
|
||||
|
||||
# cx_t,cy_t ,_, _, _ ,_,_= find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
|
||||
|
||||
# print(cx_t,'print')
|
||||
try:
|
||||
# print(all_found_texline_polygons[j_cont][0])
|
||||
cx_t, cy_t, _, _, _, _, _ = find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
|
||||
# print(all_box_coord[j_cont])
|
||||
# print(cx_t)
|
||||
# print(cy_t)
|
||||
# print(cx_d[i_drop])
|
||||
# print(cy_d[i_drop])
|
||||
y_lines = all_box_coord[int(region_final)][0] + np.array(cy_t)
|
||||
|
||||
y_lines[y_lines < y_min_d[i_drop]] = 0
|
||||
# print(y_lines)
|
||||
|
||||
arg_min = np.argmin(np.abs(y_lines - y_min_d[i_drop]))
|
||||
# print(arg_min)
|
||||
|
||||
cnt_nearest = np.copy(all_found_texline_polygons[int(region_final)][arg_min])
|
||||
cnt_nearest[:, 0] = all_found_texline_polygons[int(region_final)][arg_min][:, 0] + all_box_coord[int(region_final)][2]
|
||||
cnt_nearest[:, 1] = all_found_texline_polygons[int(region_final)][arg_min][:, 1] + all_box_coord[int(region_final)][0]
|
||||
|
||||
img_textlines = np.zeros((text_regions_p.shape[0], text_regions_p.shape[1], 3))
|
||||
img_textlines = cv2.fillPoly(img_textlines, pts=[cnt_nearest], color=(255, 255, 255))
|
||||
img_textlines = cv2.fillPoly(img_textlines, pts=[polygons_of_drop_capitals[i_drop]], color=(255, 255, 255))
|
||||
|
||||
img_textlines = img_textlines.astype(np.uint8)
|
||||
imgray = cv2.cvtColor(img_textlines, cv2.COLOR_BGR2GRAY)
|
||||
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
||||
|
||||
contours_combined, hierachy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
||||
|
||||
# print(len(contours_combined),'len textlines mixed')
|
||||
areas_cnt_text = np.array([cv2.contourArea(contours_combined[j]) for j in range(len(contours_combined))])
|
||||
|
||||
contours_biggest = contours_combined[np.argmax(areas_cnt_text)]
|
||||
|
||||
# print(np.shape(contours_biggest))
|
||||
# print(contours_biggest[:])
|
||||
contours_biggest[:, 0, 0] = contours_biggest[:, 0, 0] - all_box_coord[int(region_final)][2]
|
||||
contours_biggest[:, 0, 1] = contours_biggest[:, 0, 1] - all_box_coord[int(region_final)][0]
|
||||
|
||||
contours_biggest = contours_biggest.reshape(np.shape(contours_biggest)[0], np.shape(contours_biggest)[2])
|
||||
all_found_texline_polygons[int(region_final)][arg_min] = contours_biggest
|
||||
# all_found_texline_polygons[int(region_final)][arg_min]=contours_biggest
|
||||
|
||||
except:
|
||||
pass
|
||||
else:
|
||||
pass
|
||||
|
||||
#####for i_drop in range(len(polygons_of_drop_capitals)):
|
||||
#####for j_cont in range(len(contours_only_text_parent)):
|
||||
#####img_con=np.zeros((text_regions_p.shape[0],text_regions_p.shape[1],3))
|
||||
#####img_con=cv2.fillPoly(img_con,pts=[polygons_of_drop_capitals[i_drop] ],color=(255,255,255))
|
||||
#####img_con=cv2.fillPoly(img_con,pts=[contours_only_text_parent[j_cont]],color=(255,255,255))
|
||||
|
||||
#####img_con=img_con.astype(np.uint8)
|
||||
######imgray = cv2.cvtColor(img_con, cv2.COLOR_BGR2GRAY)
|
||||
######ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
||||
|
||||
######contours_new,hierachy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
|
||||
|
||||
#####contours_new,hir_new=return_contours_of_image(img_con)
|
||||
#####contours_new_parent=return_parent_contours( contours_new,hir_new)
|
||||
######plt.imshow(img_con)
|
||||
######plt.show()
|
||||
#####try:
|
||||
#####if len(contours_new_parent)==1:
|
||||
######print(all_found_texline_polygons[j_cont][0])
|
||||
#####cx_t,cy_t ,_, _, _ ,_,_= find_new_features_of_contoures(all_found_texline_polygons[j_cont])
|
||||
######print(all_box_coord[j_cont])
|
||||
######print(cx_t)
|
||||
######print(cy_t)
|
||||
######print(cx_d[i_drop])
|
||||
######print(cy_d[i_drop])
|
||||
#####y_lines=all_box_coord[j_cont][0]+np.array(cy_t)
|
||||
|
||||
######print(y_lines)
|
||||
|
||||
#####arg_min=np.argmin(np.abs(y_lines-y_min_d[i_drop]) )
|
||||
######print(arg_min)
|
||||
|
||||
#####cnt_nearest=np.copy(all_found_texline_polygons[j_cont][arg_min])
|
||||
#####cnt_nearest[:,0]=all_found_texline_polygons[j_cont][arg_min][:,0]+all_box_coord[j_cont][2]
|
||||
#####cnt_nearest[:,1]=all_found_texline_polygons[j_cont][arg_min][:,1]+all_box_coord[j_cont][0]
|
||||
|
||||
#####img_textlines=np.zeros((text_regions_p.shape[0],text_regions_p.shape[1],3))
|
||||
#####img_textlines=cv2.fillPoly(img_textlines,pts=[cnt_nearest],color=(255,255,255))
|
||||
#####img_textlines=cv2.fillPoly(img_textlines,pts=[polygons_of_drop_capitals[i_drop] ],color=(255,255,255))
|
||||
|
||||
#####img_textlines=img_textlines.astype(np.uint8)
|
||||
#####imgray = cv2.cvtColor(img_textlines, cv2.COLOR_BGR2GRAY)
|
||||
#####ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
||||
|
||||
#####contours_combined,hierachy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
|
||||
|
||||
#####areas_cnt_text=np.array([cv2.contourArea(contours_combined[j]) for j in range(len(contours_combined))])
|
||||
|
||||
#####contours_biggest=contours_combined[np.argmax(areas_cnt_text)]
|
||||
|
||||
######print(np.shape(contours_biggest))
|
||||
######print(contours_biggest[:])
|
||||
#####contours_biggest[:,0,0]=contours_biggest[:,0,0]-all_box_coord[j_cont][2]
|
||||
#####contours_biggest[:,0,1]=contours_biggest[:,0,1]-all_box_coord[j_cont][0]
|
||||
|
||||
#####all_found_texline_polygons[j_cont][arg_min]=contours_biggest
|
||||
######print(contours_biggest)
|
||||
######plt.imshow(img_textlines[:,:,0])
|
||||
######plt.show()
|
||||
#####else:
|
||||
#####pass
|
||||
#####except:
|
||||
#####pass
|
||||
return all_found_texline_polygons
|
||||
|
||||
def filter_small_drop_capitals_from_no_patch_layout(layout_no_patch, layout1):
|
||||
|
||||
drop_only = (layout_no_patch[:, :, 0] == 4) * 1
|
||||
contours_drop, hir_on_drop = return_contours_of_image(drop_only)
|
||||
contours_drop_parent = return_parent_contours(contours_drop, hir_on_drop)
|
||||
|
||||
areas_cnt_text = np.array([cv2.contourArea(contours_drop_parent[j]) for j in range(len(contours_drop_parent))])
|
||||
areas_cnt_text = areas_cnt_text / float(drop_only.shape[0] * drop_only.shape[1])
|
||||
|
||||
contours_drop_parent = [contours_drop_parent[jz] for jz in range(len(contours_drop_parent)) if areas_cnt_text[jz] > 0.001]
|
||||
|
||||
areas_cnt_text = [areas_cnt_text[jz] for jz in range(len(areas_cnt_text)) if areas_cnt_text[jz] > 0.001]
|
||||
|
||||
contours_drop_parent_final = []
|
||||
|
||||
for jj in range(len(contours_drop_parent)):
|
||||
x, y, w, h = cv2.boundingRect(contours_drop_parent[jj])
|
||||
# boxes.append([int(x), int(y), int(w), int(h)])
|
||||
|
||||
iou_of_box_and_contoure = float(drop_only.shape[0] * drop_only.shape[1]) * areas_cnt_text[jj] / float(w * h) * 100
|
||||
height_to_weight_ratio = h / float(w)
|
||||
weigh_to_height_ratio = w / float(h)
|
||||
|
||||
if iou_of_box_and_contoure > 60 and weigh_to_height_ratio < 1.2 and height_to_weight_ratio < 2:
|
||||
map_of_drop_contour_bb = np.zeros((layout1.shape[0], layout1.shape[1]))
|
||||
map_of_drop_contour_bb[y : y + h, x : x + w] = layout1[y : y + h, x : x + w]
|
||||
|
||||
if (((map_of_drop_contour_bb == 1) * 1).sum() / float(((map_of_drop_contour_bb == 5) * 1).sum()) * 100) >= 15:
|
||||
contours_drop_parent_final.append(contours_drop_parent[jj])
|
||||
|
||||
layout_no_patch[:, :, 0][layout_no_patch[:, :, 0] == 4] = 0
|
||||
|
||||
layout_no_patch = cv2.fillPoly(layout_no_patch, pts=contours_drop_parent_final, color=(4, 4, 4))
|
||||
|
||||
return layout_no_patch
|
||||
|
||||
@ -0,0 +1,3 @@
|
||||
|
||||
def isNaN(num):
|
||||
return num != num
|
||||
@ -0,0 +1,252 @@
|
||||
import numpy as np
|
||||
import cv2
|
||||
from scipy.signal import find_peaks
|
||||
from scipy.ndimage import gaussian_filter1d
|
||||
|
||||
|
||||
from .contour import find_new_features_of_contoures, return_contours_of_interested_region
|
||||
from .resize import resize_image
|
||||
from .rotate import rotate_image
|
||||
|
||||
def get_marginals(text_with_lines, text_regions, num_col, slope_deskew, kernel=None):
|
||||
mask_marginals=np.zeros((text_with_lines.shape[0],text_with_lines.shape[1]))
|
||||
mask_marginals=mask_marginals.astype(np.uint8)
|
||||
|
||||
|
||||
text_with_lines=text_with_lines.astype(np.uint8)
|
||||
##text_with_lines=cv2.erode(text_with_lines,self.kernel,iterations=3)
|
||||
|
||||
text_with_lines_eroded=cv2.erode(text_with_lines,kernel,iterations=5)
|
||||
|
||||
if text_with_lines.shape[0]<=1500:
|
||||
pass
|
||||
elif text_with_lines.shape[0]>1500 and text_with_lines.shape[0]<=1800:
|
||||
text_with_lines=resize_image(text_with_lines,int(text_with_lines.shape[0]*1.5),text_with_lines.shape[1])
|
||||
text_with_lines=cv2.erode(text_with_lines,kernel,iterations=5)
|
||||
text_with_lines=resize_image(text_with_lines,text_with_lines_eroded.shape[0],text_with_lines_eroded.shape[1])
|
||||
else:
|
||||
text_with_lines=resize_image(text_with_lines,int(text_with_lines.shape[0]*1.8),text_with_lines.shape[1])
|
||||
text_with_lines=cv2.erode(text_with_lines,kernel,iterations=7)
|
||||
text_with_lines=resize_image(text_with_lines,text_with_lines_eroded.shape[0],text_with_lines_eroded.shape[1])
|
||||
|
||||
|
||||
text_with_lines_y=text_with_lines.sum(axis=0)
|
||||
text_with_lines_y_eroded=text_with_lines_eroded.sum(axis=0)
|
||||
|
||||
thickness_along_y_percent=text_with_lines_y_eroded.max()/(float(text_with_lines.shape[0]))*100
|
||||
|
||||
#print(thickness_along_y_percent,'thickness_along_y_percent')
|
||||
|
||||
if thickness_along_y_percent<30:
|
||||
min_textline_thickness=8
|
||||
elif thickness_along_y_percent>=30 and thickness_along_y_percent<50:
|
||||
min_textline_thickness=20
|
||||
else:
|
||||
min_textline_thickness=40
|
||||
|
||||
|
||||
|
||||
if thickness_along_y_percent>=14:
|
||||
|
||||
text_with_lines_y_rev=-1*text_with_lines_y[:]
|
||||
#print(text_with_lines_y)
|
||||
#print(text_with_lines_y_rev)
|
||||
|
||||
|
||||
|
||||
|
||||
#plt.plot(text_with_lines_y)
|
||||
#plt.show()
|
||||
|
||||
|
||||
text_with_lines_y_rev=text_with_lines_y_rev-np.min(text_with_lines_y_rev)
|
||||
|
||||
#plt.plot(text_with_lines_y_rev)
|
||||
#plt.show()
|
||||
sigma_gaus=1
|
||||
region_sum_0= gaussian_filter1d(text_with_lines_y, sigma_gaus)
|
||||
|
||||
region_sum_0_rev=gaussian_filter1d(text_with_lines_y_rev, sigma_gaus)
|
||||
|
||||
#plt.plot(region_sum_0_rev)
|
||||
#plt.show()
|
||||
region_sum_0_updown=region_sum_0[len(region_sum_0)::-1]
|
||||
|
||||
first_nonzero=(next((i for i, x in enumerate(region_sum_0) if x), None))
|
||||
last_nonzero=(next((i for i, x in enumerate(region_sum_0_updown) if x), None))
|
||||
|
||||
|
||||
last_nonzero=len(region_sum_0)-last_nonzero
|
||||
|
||||
##img_sum_0_smooth_rev=-region_sum_0
|
||||
|
||||
|
||||
mid_point=(last_nonzero+first_nonzero)/2.
|
||||
|
||||
|
||||
one_third_right=(last_nonzero-mid_point)/3.0
|
||||
one_third_left=(mid_point-first_nonzero)/3.0
|
||||
|
||||
#img_sum_0_smooth_rev=img_sum_0_smooth_rev-np.min(img_sum_0_smooth_rev)
|
||||
|
||||
|
||||
|
||||
|
||||
peaks, _ = find_peaks(text_with_lines_y_rev, height=0)
|
||||
|
||||
|
||||
peaks=np.array(peaks)
|
||||
|
||||
|
||||
#print(region_sum_0[peaks])
|
||||
##plt.plot(region_sum_0)
|
||||
##plt.plot(peaks,region_sum_0[peaks],'*')
|
||||
##plt.show()
|
||||
#print(first_nonzero,last_nonzero,peaks)
|
||||
peaks=peaks[(peaks>first_nonzero) & ((peaks<last_nonzero))]
|
||||
|
||||
#print(first_nonzero,last_nonzero,peaks)
|
||||
|
||||
|
||||
#print(region_sum_0[peaks]<10)
|
||||
####peaks=peaks[region_sum_0[peaks]<25 ]
|
||||
|
||||
#print(region_sum_0[peaks])
|
||||
peaks=peaks[region_sum_0[peaks]<min_textline_thickness ]
|
||||
#print(peaks)
|
||||
#print(first_nonzero,last_nonzero,one_third_right,one_third_left)
|
||||
|
||||
if num_col==1:
|
||||
peaks_right=peaks[peaks>mid_point]
|
||||
peaks_left=peaks[peaks<mid_point]
|
||||
if num_col==2:
|
||||
peaks_right=peaks[peaks>(mid_point+one_third_right)]
|
||||
peaks_left=peaks[peaks<(mid_point-one_third_left)]
|
||||
|
||||
|
||||
try:
|
||||
point_right=np.min(peaks_right)
|
||||
except:
|
||||
point_right=last_nonzero
|
||||
|
||||
|
||||
try:
|
||||
point_left=np.max(peaks_left)
|
||||
except:
|
||||
point_left=first_nonzero
|
||||
|
||||
|
||||
|
||||
|
||||
#print(point_left,point_right)
|
||||
#print(text_regions.shape)
|
||||
if point_right>=mask_marginals.shape[1]:
|
||||
point_right=mask_marginals.shape[1]-1
|
||||
|
||||
try:
|
||||
mask_marginals[:,point_left:point_right]=1
|
||||
except:
|
||||
mask_marginals[:,:]=1
|
||||
|
||||
#print(mask_marginals.shape,point_left,point_right,'nadosh')
|
||||
mask_marginals_rotated=rotate_image(mask_marginals,-slope_deskew)
|
||||
|
||||
#print(mask_marginals_rotated.shape,'nadosh')
|
||||
mask_marginals_rotated_sum=mask_marginals_rotated.sum(axis=0)
|
||||
|
||||
mask_marginals_rotated_sum[mask_marginals_rotated_sum!=0]=1
|
||||
index_x=np.array(range(len(mask_marginals_rotated_sum)))+1
|
||||
|
||||
index_x_interest=index_x[mask_marginals_rotated_sum==1]
|
||||
|
||||
min_point_of_left_marginal=np.min(index_x_interest)-16
|
||||
max_point_of_right_marginal=np.max(index_x_interest)+16
|
||||
|
||||
if min_point_of_left_marginal<0:
|
||||
min_point_of_left_marginal=0
|
||||
if max_point_of_right_marginal>=text_regions.shape[1]:
|
||||
max_point_of_right_marginal=text_regions.shape[1]-1
|
||||
|
||||
|
||||
#print(np.min(index_x_interest) ,np.max(index_x_interest),'minmaxnew')
|
||||
#print(mask_marginals_rotated.shape,text_regions.shape,'mask_marginals_rotated')
|
||||
#plt.imshow(mask_marginals)
|
||||
#plt.show()
|
||||
|
||||
#plt.imshow(mask_marginals_rotated)
|
||||
#plt.show()
|
||||
|
||||
text_regions[(mask_marginals_rotated[:,:]!=1) & (text_regions[:,:]==1)]=4
|
||||
|
||||
#plt.imshow(text_regions)
|
||||
#plt.show()
|
||||
|
||||
pixel_img=4
|
||||
min_area_text=0.00001
|
||||
polygons_of_marginals=return_contours_of_interested_region(text_regions,pixel_img,min_area_text)
|
||||
|
||||
cx_text_only,cy_text_only ,x_min_text_only,x_max_text_only, y_min_text_only ,y_max_text_only,y_cor_x_min_main=find_new_features_of_contoures(polygons_of_marginals)
|
||||
|
||||
text_regions[(text_regions[:,:]==4)]=1
|
||||
|
||||
marginlas_should_be_main_text=[]
|
||||
|
||||
x_min_marginals_left=[]
|
||||
x_min_marginals_right=[]
|
||||
|
||||
for i in range(len(cx_text_only)):
|
||||
|
||||
x_width_mar=abs(x_min_text_only[i]-x_max_text_only[i])
|
||||
y_height_mar=abs(y_min_text_only[i]-y_max_text_only[i])
|
||||
#print(x_width_mar,y_height_mar,y_height_mar/x_width_mar,'y_height_mar')
|
||||
if x_width_mar>16 and y_height_mar/x_width_mar<18:
|
||||
marginlas_should_be_main_text.append(polygons_of_marginals[i])
|
||||
if x_min_text_only[i]<(mid_point-one_third_left):
|
||||
x_min_marginals_left_new=x_min_text_only[i]
|
||||
if len(x_min_marginals_left)==0:
|
||||
x_min_marginals_left.append(x_min_marginals_left_new)
|
||||
else:
|
||||
x_min_marginals_left[0]=min(x_min_marginals_left[0],x_min_marginals_left_new)
|
||||
else:
|
||||
x_min_marginals_right_new=x_min_text_only[i]
|
||||
if len(x_min_marginals_right)==0:
|
||||
x_min_marginals_right.append(x_min_marginals_right_new)
|
||||
else:
|
||||
x_min_marginals_right[0]=min(x_min_marginals_right[0],x_min_marginals_right_new)
|
||||
|
||||
if len(x_min_marginals_left)==0:
|
||||
x_min_marginals_left=[0]
|
||||
if len(x_min_marginals_right)==0:
|
||||
x_min_marginals_right=[text_regions.shape[1]-1]
|
||||
|
||||
|
||||
|
||||
|
||||
#print(x_min_marginals_left[0],x_min_marginals_right[0],'margo')
|
||||
|
||||
#print(marginlas_should_be_main_text,'marginlas_should_be_main_text')
|
||||
text_regions=cv2.fillPoly(text_regions, pts =marginlas_should_be_main_text, color=(4,4))
|
||||
|
||||
#print(np.unique(text_regions))
|
||||
|
||||
#text_regions[:,:int(x_min_marginals_left[0])][text_regions[:,:int(x_min_marginals_left[0])]==1]=0
|
||||
#text_regions[:,int(x_min_marginals_right[0]):][text_regions[:,int(x_min_marginals_right[0]):]==1]=0
|
||||
|
||||
text_regions[:,:int(min_point_of_left_marginal)][text_regions[:,:int(min_point_of_left_marginal)]==1]=0
|
||||
text_regions[:,int(max_point_of_right_marginal):][text_regions[:,int(max_point_of_right_marginal):]==1]=0
|
||||
|
||||
###text_regions[:,0:point_left][text_regions[:,0:point_left]==1]=4
|
||||
|
||||
###text_regions[:,point_right:][ text_regions[:,point_right:]==1]=4
|
||||
#plt.plot(region_sum_0)
|
||||
#plt.plot(peaks,region_sum_0[peaks],'*')
|
||||
#plt.show()
|
||||
|
||||
|
||||
#plt.imshow(text_regions)
|
||||
#plt.show()
|
||||
|
||||
#sys.exit()
|
||||
else:
|
||||
pass
|
||||
return text_regions
|
||||
@ -0,0 +1,4 @@
|
||||
import cv2
|
||||
|
||||
def resize_image(img_in, input_height, input_width):
|
||||
return cv2.resize(img_in, (input_width, input_height), interpolation=cv2.INTER_NEAREST)
|
||||
@ -0,0 +1,85 @@
|
||||
import math
|
||||
|
||||
import imutils
|
||||
import cv2
|
||||
|
||||
def rotatedRectWithMaxArea(w, h, angle):
|
||||
if w <= 0 or h <= 0:
|
||||
return 0, 0
|
||||
|
||||
width_is_longer = w >= h
|
||||
side_long, side_short = (w, h) if width_is_longer else (h, w)
|
||||
|
||||
# since the solutions for angle, -angle and 180-angle are all the same,
|
||||
# if suffices to look at the first quadrant and the absolute values of sin,cos:
|
||||
sin_a, cos_a = abs(math.sin(angle)), abs(math.cos(angle))
|
||||
if side_short <= 2.0 * sin_a * cos_a * side_long or abs(sin_a - cos_a) < 1e-10:
|
||||
# half constrained case: two crop corners touch the longer side,
|
||||
# the other two corners are on the mid-line parallel to the longer line
|
||||
x = 0.5 * side_short
|
||||
wr, hr = (x / sin_a, x / cos_a) if width_is_longer else (x / cos_a, x / sin_a)
|
||||
else:
|
||||
# fully constrained case: crop touches all 4 sides
|
||||
cos_2a = cos_a * cos_a - sin_a * sin_a
|
||||
wr, hr = (w * cos_a - h * sin_a) / cos_2a, (h * cos_a - w * sin_a) / cos_2a
|
||||
|
||||
return wr, hr
|
||||
|
||||
def rotate_max_area_new(image, rotated, angle):
|
||||
wr, hr = rotatedRectWithMaxArea(image.shape[1], image.shape[0], math.radians(angle))
|
||||
h, w, _ = rotated.shape
|
||||
y1 = h // 2 - int(hr / 2)
|
||||
y2 = y1 + int(hr)
|
||||
x1 = w // 2 - int(wr / 2)
|
||||
x2 = x1 + int(wr)
|
||||
return rotated[y1:y2, x1:x2]
|
||||
|
||||
def rotation_image_new(img, thetha):
|
||||
rotated = imutils.rotate(img, thetha)
|
||||
return rotate_max_area_new(img, rotated, thetha)
|
||||
|
||||
def rotate_image(img_patch, slope):
|
||||
(h, w) = img_patch.shape[:2]
|
||||
center = (w // 2, h // 2)
|
||||
M = cv2.getRotationMatrix2D(center, slope, 1.0)
|
||||
return cv2.warpAffine(img_patch, M, (w, h), flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE)
|
||||
|
||||
def rotyate_image_different( img, slope):
|
||||
# img = cv2.imread('images/input.jpg')
|
||||
num_rows, num_cols = img.shape[:2]
|
||||
|
||||
rotation_matrix = cv2.getRotationMatrix2D((num_cols / 2, num_rows / 2), slope, 1)
|
||||
img_rotation = cv2.warpAffine(img, rotation_matrix, (num_cols, num_rows))
|
||||
return img_rotation
|
||||
|
||||
def rotate_max_area(image, rotated, rotated_textline, rotated_layout, angle):
|
||||
wr, hr = rotatedRectWithMaxArea(image.shape[1], image.shape[0], math.radians(angle))
|
||||
h, w, _ = rotated.shape
|
||||
y1 = h // 2 - int(hr / 2)
|
||||
y2 = y1 + int(hr)
|
||||
x1 = w // 2 - int(wr / 2)
|
||||
x2 = x1 + int(wr)
|
||||
return rotated[y1:y2, x1:x2], rotated_textline[y1:y2, x1:x2], rotated_layout[y1:y2, x1:x2]
|
||||
|
||||
def rotation_not_90_func(img, textline, text_regions_p_1, thetha):
|
||||
rotated = imutils.rotate(img, thetha)
|
||||
rotated_textline = imutils.rotate(textline, thetha)
|
||||
rotated_layout = imutils.rotate(text_regions_p_1, thetha)
|
||||
return rotate_max_area(img, rotated, rotated_textline, rotated_layout, thetha)
|
||||
|
||||
def rotation_not_90_func_full_layout(img, textline, text_regions_p_1, text_regions_p_fully, thetha):
|
||||
rotated = imutils.rotate(img, thetha)
|
||||
rotated_textline = imutils.rotate(textline, thetha)
|
||||
rotated_layout = imutils.rotate(text_regions_p_1, thetha)
|
||||
rotated_layout_full = imutils.rotate(text_regions_p_fully, thetha)
|
||||
return rotate_max_area_full_layout(img, rotated, rotated_textline, rotated_layout, rotated_layout_full, thetha)
|
||||
|
||||
def rotate_max_area_full_layout(image, rotated, rotated_textline, rotated_layout, rotated_layout_full, angle):
|
||||
wr, hr = rotatedRectWithMaxArea(image.shape[1], image.shape[0], math.radians(angle))
|
||||
h, w, _ = rotated.shape
|
||||
y1 = h // 2 - int(hr / 2)
|
||||
y2 = y1 + int(hr)
|
||||
x1 = w // 2 - int(wr / 2)
|
||||
x2 = x1 + int(wr)
|
||||
return rotated[y1:y2, x1:x2], rotated_textline[y1:y2, x1:x2], rotated_layout[y1:y2, x1:x2], rotated_layout_full[y1:y2, x1:x2]
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
Binary file not shown.
@ -0,0 +1,7 @@
|
||||
def test_utils_import():
|
||||
import sbb_newspapers_org_image.utils
|
||||
import sbb_newspapers_org_image.utils.contour
|
||||
import sbb_newspapers_org_image.utils.drop_capitals
|
||||
import sbb_newspapers_org_image.utils.drop_capitals
|
||||
import sbb_newspapers_org_image.utils.is_nan
|
||||
import sbb_newspapers_org_image.utils.rotate
|
||||
Loading…
Reference in New Issue