diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 3c633ba..c3d1811 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -8,7 +8,7 @@ repos: rev: v4.5.0 hooks: - id: trailing-whitespace - - id: end-of-file-fixer + # - id: end-of-file-fixer - id: check-yaml args: [--unsafe] # needed for .gitlab-ci.yml - id: check-toml diff --git a/dotted/0.png b/dotted/0.png new file mode 100644 index 0000000..ccf7cf1 Binary files /dev/null and b/dotted/0.png differ diff --git a/dotted/1 b/dotted/1 new file mode 100644 index 0000000..78ff929 Binary files /dev/null and b/dotted/1 differ diff --git a/dotted/2.jpg b/dotted/2.jpg new file mode 100644 index 0000000..ed26cf2 Binary files /dev/null and b/dotted/2.jpg differ diff --git a/dotted/2017-1078223-005.png b/dotted/2017-1078223-005.png new file mode 100644 index 0000000..ad5836d Binary files /dev/null and b/dotted/2017-1078223-005.png differ diff --git a/dotted/2017-1078223-009.png b/dotted/2017-1078223-009.png new 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(pkgs.buildFHSUserEnv rec { name = "cv-analysis-service"; targetPkgs = pkgs: (with pkgs; [ + python310 poppler_utils zlib poetry diff --git a/poetry.lock b/poetry.lock index 6137335..0db7cc2 100644 --- a/poetry.lock +++ b/poetry.lock @@ -2,18 +2,22 @@ [[package]] name = "accessible-pygments" -version = "0.0.4" +version = "0.0.5" description = "A collection of accessible pygments styles" optional = false -python-versions = "*" +python-versions = ">=3.9" files = [ - {file = "accessible-pygments-0.0.4.tar.gz", hash = "sha256:e7b57a9b15958e9601c7e9eb07a440c813283545a20973f2574a5f453d0e953e"}, - {file = "accessible_pygments-0.0.4-py2.py3-none-any.whl", hash = "sha256:416c6d8c1ea1c5ad8701903a20fcedf953c6e720d64f33dc47bfb2d3f2fa4e8d"}, + {file = "accessible_pygments-0.0.5-py3-none-any.whl", hash = "sha256:88ae3211e68a1d0b011504b2ffc1691feafce124b845bd072ab6f9f66f34d4b7"}, + {file = "accessible_pygments-0.0.5.tar.gz", hash = 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"jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"] [metadata] lock-version = "2.0" diff --git a/pyproject.toml b/pyproject.toml index 7546d28..86501eb 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -31,6 +31,7 @@ pdf2img = { version = "0.7.0", source = "gitlab-red" } dvc-azure = "^2.21.2" pymupdf = "^1.24.1" types-pillow = "^10.2.0.20240423" +#matplotlib-backend-wezterm = "^2.1.2" [tool.poetry.group.test.dependencies] pytest = "^7.0.1" diff --git a/scripts/grid_search.py b/scripts/grid_search.py new file mode 100644 index 0000000..235bf20 --- /dev/null +++ b/scripts/grid_search.py @@ -0,0 +1,8 @@ +from cv_analysis.table_inference import infer_lines + + +def grid_search() -> None: ... + + +if __name__ == "__main__": + grid_search() diff --git a/src/cv_analysis/table_inference.py b/src/cv_analysis/table_inference.py index c341427..003346a 100644 --- a/src/cv_analysis/table_inference.py +++ b/src/cv_analysis/table_inference.py @@ -1,6 +1,6 @@ from operator import itemgetter from pathlib import Path -from typing import Callable, Optional, Tuple +from typing import Callable, Iterable, Optional, Tuple import cv2 import matplotlib.pyplot as plt @@ -9,6 +9,8 @@ from kn_utils.logging import logger # type: ignore from numpy import ndarray as Array from scipy.stats import norm # type: ignore +from .utils.dotted_lines import detect_dotted_from_extrema + def show_multiple(arrs: Tuple[Array], title: str = ""): plt.clf() @@ -150,16 +152,65 @@ def filter_fp_col_lines(line_list: list[int], filt_sums: Array) -> list[int]: return line_list +def sharpen_sums(sums: Array) -> Array: + sums = sums.astype("int64") + shift = 3 + diffs = abs(sums[shift:-shift] - sums[2 * shift :]) + abs(sums[shift:-shift] - sums[: -2 * shift]) + f2 = filter_array(sums, FILTERS["col"][2]) + return diffs + + +def detect_dotted_lines( + image: Array, + sums: Iterable, + horizontal: bool = True, + threshold: float = 1.0, + min_distance: int = 2, + max_distance: int = 20, +) -> bool: + key = "row" if horizontal else "col" + naive = filter_array(sums, FILTERS[key][1]) + naive_lines = np.where((naive[1:-1] < naive[:-2]) * (naive[1:-1] < naive[2:]) * (sums[1:-1] < 250))[0] + 1 + + bool_array = np.zeros(image.shape[1 - int(horizontal)]) + for idx in naive_lines: + band = image[idx - 1 : idx + 2, :] if horizontal else image[:, idx - 1 : idx + 1] + band_sums = np.mean(band, axis=1 - int(horizontal)) + band_sums = filter_array(band_sums, FILTERS[key][1]) + extrema = np.where((band_sums[1:-1] < band_sums[:-2]) * (band_sums[1:-1] < band_sums[2:]))[0] + 1 + + distances = extrema[1:] - extrema[:-1] + mean = np.mean(distances) + std = np.std(distances) + + check = "✔" if (ratio := (mean / (std + 0.01))) > 1.5 and mean < 40 else "" + print(f"{idx:4} {mean:6.2f} {std:6.2f} {ratio:6.2f} {check}") + + score = std # maybe make more advanced score function later + if (min_distance <= mean <= max_distance) and (score < threshold): + print(idx) + bool_array[idx] = 1 + return bool_array + + def get_lines_either(table_array: Array, horizontal=True) -> list[int]: key = "row" if horizontal else "col" + h, w = map(int, table_array.shape) + + table_array = ( + table_array[:, int(0.1 * w) : int(0.9 * w)] if horizontal else table_array[int(0.1 * h) : int(0.9 * h)] + ) sums = np.mean(table_array, axis=int(horizontal)) + dotted = detect_dotted_lines(table_array, sums, horizontal=horizontal) + threshold = 0.3 * 255 # np.mean(sums) - (1 + 2 * horizontal) * np.std(sums) - predicate = 1000.0 * (sums < threshold) + predicate = 1000.0 * ((sums < threshold) | dotted) sums = np.maximum( np.maximum(sums[1:-1], predicate[1:-1]), np.maximum(predicate[:-2], predicate[2:]), ) + filtered_sums = filter_array(sums, FILTERS[key][1]) filtered_sums = filter_array(filtered_sums, FILTERS[key][2]) filtered_sums = filter_array(filtered_sums, FILTERS[key][3]) @@ -179,9 +230,7 @@ def img_bytes_to_array(img_bytes: bytes) -> Array: def infer_lines(img: Array) -> dict[str, dict[str, int] | list[dict[str, int]]]: - cv2.imwrite("/tmp/table.png", img) _, img = cv2.threshold(img, 220, 255, cv2.THRESH_BINARY) - cv2.imwrite("/tmp/table_bin.png", img) h, w = map(int, img.shape) row_vals = map(int, get_lines_either(img, horizontal=True)) col_vals = map(int, get_lines_either(img, horizontal=False)) diff --git a/src/cv_analysis/utils/dotted_lines.py b/src/cv_analysis/utils/dotted_lines.py new file mode 100644 index 0000000..9863e0a --- /dev/null +++ b/src/cv_analysis/utils/dotted_lines.py @@ -0,0 +1,18 @@ +""" +General approach: + Get horizontal and vertical pixel sum extrema. Then take a band of k around each minimum (corresponding to darkest), e.g. k=3. + Recalculate minima for each band. + Compute a list of distances between minima. + Compute the mean and standard deviation between minima. + If rho:=std/(eta*mean) < phi for some threshold phi, the band contains a dotted line. -> logic: std can be larger for larger mean, i.e. more spaced-out dotted lines + +Pros: + Intuitive and efficient. + +Cons: + May not work for irregular/mixed dotted lines, such as (possibly) --*--*--*--*--*--*--*--*--*--*-- +""" + +from typing import Iterable + +import numpy as np