From df8096d011c01c4ca1d7c15d9977ee73aae0bafd Mon Sep 17 00:00:00 2001 From: Arindam Kulshi Date: Thu, 23 May 2024 16:50:40 -0700 Subject: [PATCH] added tests and examples for checkbox dectector --- ...=> checkbox_detector__subtraction_main.py} | 2 +- OCR/ocr/checkbox_detector_vision_main.py | 45 + .../checkbox_detector_image_extractor.py | 122 +++ ...or.py => checkbox_detector_subtraction.py} | 2 +- OCR/ocr/services/checkbox_image_align.py | 88 ++ OCR/ocr/services/checkbox_template.png | Bin 2460 -> 0 bytes OCR/tests/assets/TRF.png | Bin 0 -> 275578 bytes OCR/tests/assets/TRF_ann.json | 916 ++++++++++++++++++ .../assets/checkbox_filled_100percent.png | Bin 0 -> 162 bytes OCR/tests/assets/checkbox_filled_1percent.png | Bin 2456 -> 244 bytes .../assets/checkbox_filled_20percent.png | Bin 2462 -> 0 bytes .../assets/checkbox_marked_10percent.png | Bin 0 -> 404 bytes OCR/tests/assets/checkbox_template.png | Bin 2460 -> 1844 bytes OCR/tests/assets/image_snap.jpeg | Bin 0 -> 1486542 bytes .../assets/per_example_marked_labels.json | 160 +++ OCR/tests/checkbox_detector_test.py | 29 + 16 files changed, 1362 insertions(+), 2 deletions(-) rename OCR/ocr/{checkbox_detector_main.py => checkbox_detector__subtraction_main.py} (90%) create mode 100644 OCR/ocr/checkbox_detector_vision_main.py create mode 100644 OCR/ocr/services/checkbox_detector_image_extractor.py rename OCR/ocr/services/{checkbox_detector.py => checkbox_detector_subtraction.py} (93%) create mode 100644 OCR/ocr/services/checkbox_image_align.py delete mode 100644 OCR/ocr/services/checkbox_template.png create mode 100644 OCR/tests/assets/TRF.png create mode 100644 OCR/tests/assets/TRF_ann.json create mode 100644 OCR/tests/assets/checkbox_filled_100percent.png delete mode 100644 OCR/tests/assets/checkbox_filled_20percent.png create mode 100644 OCR/tests/assets/checkbox_marked_10percent.png create mode 100644 OCR/tests/assets/image_snap.jpeg create mode 100644 OCR/tests/assets/per_example_marked_labels.json create mode 100644 OCR/tests/checkbox_detector_test.py diff --git a/OCR/ocr/checkbox_detector_main.py b/OCR/ocr/checkbox_detector__subtraction_main.py similarity index 90% rename from OCR/ocr/checkbox_detector_main.py rename to OCR/ocr/checkbox_detector__subtraction_main.py index 6655fdff..ffd0841c 100644 --- a/OCR/ocr/checkbox_detector_main.py +++ b/OCR/ocr/checkbox_detector__subtraction_main.py @@ -1,4 +1,4 @@ -from ocr.services.checkbox_detector import CheckboxDetector +from ocr.services.checkbox_detector_subtraction import CheckboxDetector import os diff --git a/OCR/ocr/checkbox_detector_vision_main.py b/OCR/ocr/checkbox_detector_vision_main.py new file mode 100644 index 00000000..989d51b4 --- /dev/null +++ b/OCR/ocr/checkbox_detector_vision_main.py @@ -0,0 +1,45 @@ +import cv2 +import argparse +from ocr.services.checkbox_image_align import SIFTAligner +from ocr.services.checkbox_detector_image_extractor import MorphDataExtractor + + +def extract_data(im, aligner, extractor): + query = cv2.imread(im) + aligned_sift = aligner.align(query) + + entities = extractor.extract(aligned_sift) + + return entities + + +def main(args): + query_image_location = args.query + template_location = args.template + roi_json_file = args.roi + + # roi_data = "./Users/arindamkulshi/IDWA/IDWA/OCR/tests/assets/checkbox1.json" + + morph_extractor = MorphDataExtractor(roi_json_file) + + # lifecell_template_path = "./Users/arindamkulshi/IDWA/IDWA/OCR/tests/assets/checkbox_template.png" + + sift_aligner = SIFTAligner(template_location) + + entities = extract_data(im=query_image_location, aligner=sift_aligner, extractor=morph_extractor) + + print(entities) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser("Parsing arguments for information extraction") + + parser.add_argument("--query", type=str, help="Image from where the data needs to be extracted") + + parser.add_argument("--template", type=str, help="The template document which is used for alignment") + + parser.add_argument("--roi", type=str, help="json file that contains the roi data") + + args = parser.parse_args() + + main(args) diff --git a/OCR/ocr/services/checkbox_detector_image_extractor.py b/OCR/ocr/services/checkbox_detector_image_extractor.py new file mode 100644 index 00000000..d8e557ba --- /dev/null +++ b/OCR/ocr/services/checkbox_detector_image_extractor.py @@ -0,0 +1,122 @@ +import cv2 +from abc import ABC, abstractmethod +import json +import numpy as np + +AREA_THRESHOLD = 40 + + +class TRFDataExtractor(ABC): + def __init__(self, roi_data) -> None: + super().__init__() + with open(roi_data, "rb") as roi_data_file: + roi = json.load(roi_data_file) + shapes = [] + regions = [] + for item in roi["data"]["regions"]: + shapes.append(item["shape_attributes"]) + regions.append(item["region_attributes"]) + + self._roi = list(zip(shapes, regions)) + + def _get_edges(self, im: cv2.Mat) -> cv2.Mat: + gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY) + blur = cv2.GaussianBlur(gray, (5, 5), 0) + _, thresh = cv2.threshold(blur, 127, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) + + edges = cv2.Canny(blur, 220, 250) + + return edges + + @abstractmethod + def extract(self, aligned_image: cv2.Mat) -> list: + pass + + +class BoxDataExtractor(TRFDataExtractor): + def __init__(self, roi_data, non_black_count_threshold=90) -> None: + super().__init__(roi_data) + self._non_black_count_threshold = non_black_count_threshold + + def extract(self, aligned_image: cv2.Mat) -> list: + entity_list = [] + + edges = self._get_edges(aligned_image) + + for shape, region in self._roi: + x = shape["x"] + y = shape["y"] + w = shape["width"] + h = shape["height"] + + im = edges[y : y + h, x : x + w] + non_black_count = cv2.countNonZero(im) + # print( + # f'Region: {region["type"]}, Non Black Count: {non_black_count}') + if non_black_count > self._non_black_count_threshold: + entity_list.append(region["type"]) + + return entity_list + + +class MorphDataExtractor(TRFDataExtractor): + def __init__(self, roi_data, non_black_count_threshold=0) -> None: + super().__init__(roi_data) + self._non_black_count_threshold = non_black_count_threshold + + def _clean_image(self, im: cv2.Mat) -> cv2.Mat: + cnts, _ = cv2.findContours(im, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)[-2:] + + for c in cnts: + area = cv2.contourArea(c) + if area < AREA_THRESHOLD: + cv2.drawContours(im, [c], -1, 0, -1) + + return im + + def extract(self, aligned_image: cv2.Mat) -> list: + entity_list = [] + gray = cv2.cvtColor(aligned_image, cv2.COLOR_BGR2GRAY) + + for shape, region in self._roi: + x = shape["x"] + y = shape["y"] + w = shape["width"] + h = shape["height"] + + im = gray[y : y + h, x : x + w] + + thresh = cv2.adaptiveThreshold(im, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 21, 10) + thresh = self._clean_image(thresh) + + repair_kernel_horizontal = cv2.getStructuringElement(cv2.MORPH_RECT, (4, 1)) + repair_horizontal = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, repair_kernel_horizontal, iterations=2) + + horizontal_lines = cv2.HoughLinesP(repair_horizontal, 1, np.pi / 180, 20, 10, 10) + if horizontal_lines is not None: + for line in horizontal_lines: + for x1, y1, x2, y2 in line: + theta = np.arctan((y2 - y1) / (x2 - x1 + 1e-8)) * 180 / np.pi + if theta > -6 and theta < 6: + cv2.line(thresh, (x1, y1), (x2, y2), (0, 0, 0), 2) + + repair_kernel_vertical = cv2.getStructuringElement(cv2.MORPH_RECT, (1, 4)) + repair_vertical = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, repair_kernel_vertical, iterations=1) + + vertical_lines = cv2.HoughLinesP(repair_vertical, 1, np.pi / 180, 12, 40, 4) + if vertical_lines is not None: + for line in vertical_lines: + for x1, y1, x2, y2 in line: + theta = np.abs(np.arctan((y2 - y1) / (x2 - x1 + 1e-8)) * 180 / np.pi) + if theta > 84 and theta < 96: + cv2.line(thresh, (x1, y1), (x2, y2), (0, 0, 0), 2) + + thresh = self._clean_image(thresh) + + non_black_count = cv2.countNonZero(thresh) + print(f"ROI: {region['type']}, Non-Black Count: {non_black_count}") + + if non_black_count > self._non_black_count_threshold: + entity_list.append(region["type"]) + + return entity_list diff --git a/OCR/ocr/services/checkbox_detector.py b/OCR/ocr/services/checkbox_detector_subtraction.py similarity index 93% rename from OCR/ocr/services/checkbox_detector.py rename to OCR/ocr/services/checkbox_detector_subtraction.py index f931f82f..355714b0 100644 --- a/OCR/ocr/services/checkbox_detector.py +++ b/OCR/ocr/services/checkbox_detector_subtraction.py @@ -15,7 +15,7 @@ def is_checked(self, image_path, threshold=18000): diff = cv2.absdiff(self.template, checkbox_image) - # Calculate the sum of the absolute differences + # Calculate difference diff_sum = np.sum(diff) print(diff_sum) print(threshold) diff --git a/OCR/ocr/services/checkbox_image_align.py b/OCR/ocr/services/checkbox_image_align.py new file mode 100644 index 00000000..f30790a7 --- /dev/null +++ b/OCR/ocr/services/checkbox_image_align.py @@ -0,0 +1,88 @@ +from abc import ABC, abstractmethod +import cv2 +import numpy as np + + +class ImageAligner(ABC): + def __init__(self, template: str) -> None: + super().__init__() + template_im: cv2.Mat = cv2.imread(template) + self._template: cv2.Mat = cv2.cvtColor(template_im, cv2.COLOR_BGR2GRAY) + + @abstractmethod + def align(self, query_image: cv2.Mat) -> cv2.Mat: + pass + + +class SIFTAligner(ImageAligner): + def __init__(self, template: str) -> None: + super().__init__(template) + self._h, self._w = self._template.shape + self._sift = cv2.SIFT_create() + self._kps_template, self._desc_template = self._sift.detectAndCompute(self._template, None) + self._matcher = cv2.BFMatcher() + + # def align(self, query_image: str) -> cv2.Mat: + # query = cv2.imread(query_image) + # self.align(query) + + def align(self, query_image: cv2.Mat) -> cv2.Mat: + # query_image = cv2.imread(query_image_path) + query = cv2.cvtColor(query_image, cv2.COLOR_BGR2GRAY) + kps_query, desc_query = self._sift.detectAndCompute(query, None) + + matches = self._matcher.knnMatch(desc_query, self._desc_template, k=2) + # Apply ratio test + good = [] + for m, n in matches: + if m.distance < 0.75 * n.distance: + good.append([m]) + + pts_template = np.zeros((len(good), 2), dtype="float") + pts_query = np.zeros((len(good), 2), dtype="float") + + for i, m in enumerate(good): + try: + pts_query[i, :] = kps_query[m[0].queryIdx].pt + pts_template[i, :] = self._kps_template[m[0].trainIdx].pt + except IndexError: + print(f"Index Error at {i}") + + H, mask = cv2.findHomography(pts_query, pts_template, method=cv2.RANSAC) + aligned = cv2.warpPerspective(query_image, H, (self._w, self._h)) + + return aligned + + +class ORBAligner(ImageAligner): + def __init__(self, template: str, max_features=3000, keep_percent=0.2) -> None: + super().__init__(template) + self._h, self._w = self._template.shape + self._orb = cv2.ORB_create(max_features) + + self._kps_template, self._desc_template = self._orb.detectAndCompute(self._template, None) + self._matcher = cv2.BFMatcher(cv2.NORM_HAMMING) + self._keep_percent = keep_percent + + def align(self, query_image: cv2.Mat) -> cv2.Mat: + query = cv2.cvtColor(query_image, cv2.COLOR_BGR2GRAY) + kps_query, desc_query = self._orb.detectAndCompute(query, None) + + matches = self._matcher.match(desc_query, self._desc_template) + + matches = sorted(matches, key=lambda x: x.distance, reverse=False) + + keep = int(len(matches) * self._keep_percent) + matches = matches[:keep] + + pts_template = np.zeros((len(matches), 2), dtype="float") + pts_query = np.zeros((len(matches), 2), dtype="float") + + for i, m in enumerate(matches): + pts_query[i, :] = kps_query[m.queryIdx].pt + pts_template[i, :] = self._kps_template[m.trainIdx].pt + + H, mask = cv2.findHomography(pts_query, pts_template, method=cv2.RANSAC) + aligned_orb = cv2.warpPerspective(query_image, H, (self._w, self._h)) + + return aligned_orb diff --git a/OCR/ocr/services/checkbox_template.png b/OCR/ocr/services/checkbox_template.png 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