| | """utils.py: Helper Functions to keep this Repo Standalone""" |
| |
|
| | |
| | from math import sin, cos, radians, sqrt |
| |
|
| | __author__ = "Johannes Bayer" |
| | __copyright__ = "Copyright 2023, DFKI" |
| | __license__ = "CC" |
| | __version__ = "0.0.1" |
| | __email__ = "johannes.bayer@dfki.de" |
| | __status__ = "Prototype" |
| |
|
| |
|
| | def shift(p, q): |
| | """Shifts a Point by another point""" |
| |
|
| | return [p[0]+q[0], p[1]+q[1]] |
| |
|
| |
|
| | def rotate(p, angle): |
| | """Rotates a Point by an Angle""" |
| |
|
| | return [p[0] * cos(angle) - p[1] * sin(angle), |
| | p[0] * sin(angle) + p[1] * cos(angle)] |
| |
|
| |
|
| | def scale(p, scale_x, scale_y): |
| | """Scales a Point in two Dimensions""" |
| |
|
| | return [p[0]*scale_x, p[1]*scale_y] |
| |
|
| |
|
| | def transform(port, bb): |
| | """Transforms a Point from Unit Space (classes ports) to Global Bounding Box (image)""" |
| |
|
| | p = shift(port['position'], (-.5, -0.5)) |
| | p = scale(p, 1.0, -1.0) |
| | p = rotate(p, -radians(bb['rotation'])) |
| | p = scale(p, bb["xmax"] - bb["xmin"], bb["ymax"] - bb["ymin"]) |
| | p = shift(p, [(bb["xmin"]+bb["xmax"])/2, (bb["ymin"]+bb["ymax"])/2]) |
| |
|
| | return {"name": port['name'], "position": p} |
| |
|
| |
|
| | def bbdist(bb1, bb2): |
| | """Calculates the Distance between two Bounding Box Annotations""" |
| |
|
| | return sqrt(((bb1["xmin"]+bb1["xmax"])/2 - (bb2["xmin"]+bb2["xmax"])/2)**2 + |
| | ((bb1["ymin"]+bb1["ymax"])/2 - (bb2["ymin"]+bb2["ymax"])/2)**2) |
| |
|
| |
|
| | def overlap(bbox1, bbox2): |
| |
|
| | if bbox1["xmin"] > bbox2["xmax"] or bbox1["xmax"] < bbox2["xmin"]: |
| | return False |
| |
|
| | if bbox1["ymin"] > bbox2["ymax"] or bbox1["ymax"] < bbox2["ymin"]: |
| | return False |
| |
|
| | return True |
| |
|
| |
|
| | def associated_keypoints(instances, shape): |
| | """Returns the points with same group id as the provided polygon""" |
| |
|
| | return [point for point in instances["points"] |
| | if point["group"] == shape["group"] and point["class"] == "connector"] |
| |
|
| |
|
| | def IoU(bb1, bb2): |
| | """Intersection over Union""" |
| |
|
| | intersection = 1 |
| | union = 1 |
| |
|
| | return intersection/union |
| |
|
| |
|
| | if __name__ == "__main__": |
| |
|
| | import sys |
| | from loader import read_pascal_voc, write_pascal_voc |
| | import numpy as np |
| | import random |
| |
|
| | if len(sys.argv) == 3: |
| | source = sys.argv[1] |
| | target = sys.argv[2] |
| |
|
| | ann1, ann2 = [[bbox for bbox in read_pascal_voc(path)['bboxes'] if bbox['class'] == "text"] |
| | for path in [source, target]] |
| |
|
| | if not len(ann1) == len(ann2): |
| |
|
| | print(f"Warning: Unequal Text Count ({len(ann1)} vs. {len(ann2)}), cropping..") |
| | consensus = min(len(ann1), len(ann2)) |
| | ann1 = ann1[:consensus] |
| | ann2 = ann2[:consensus] |
| |
|
| | x1 = np.array([(bbox['xmin']+bbox['xmax'])/2 for bbox in ann1]) |
| | y1 = np.array([(bbox['ymin']+bbox['ymax'])/2 for bbox in ann1]) |
| | x2 = np.array([(bbox['xmin']+bbox['xmax'])/2 for bbox in ann2]) |
| | y2 = np.array([(bbox['ymin']+bbox['ymax'])/2 for bbox in ann2]) |
| | x1 = ((x1-np.min(x1))/(np.max(x1)-np.min(x1))) * (np.max(x2)-np.min(x2)) + np.min(x2) |
| | y1 = ((y1-np.min(y1))/(np.max(y1)-np.min(y1))) * (np.max(y2)-np.min(y2)) + np.min(y2) |
| | dist = np.sqrt((x1-x2[np.newaxis].T)**2 + (y1-y2[np.newaxis].T)**2) |
| | indices_1 = np.arange(len(ann1)) |
| | indices_2 = np.arange(len(ann2)) |
| | print(np.sum(np.diagonal(dist))) |
| |
|
| | for i in range(10000): |
| | if random.random() > 0.5: |
| | max_dist_pos = np.argmax(np.diagonal(dist)) |
| |
|
| | else: |
| | max_dist_pos = random.randint(0, len(ann1)-1) |
| |
|
| | if np.min(dist[max_dist_pos, :]) < np.min(dist[:, max_dist_pos]): |
| | min_dist_pos = np.argmin(dist[max_dist_pos, :]) |
| | dist[:, [max_dist_pos, min_dist_pos]] = dist[:, [min_dist_pos, max_dist_pos]] |
| | indices_1[[max_dist_pos, min_dist_pos]] = indices_1[[min_dist_pos, max_dist_pos]] |
| |
|
| | else: |
| | min_dist_pos = np.argmin(dist[:, max_dist_pos]) |
| | dist[[max_dist_pos, min_dist_pos], :] = dist[[min_dist_pos, max_dist_pos], :] |
| | indices_2[[max_dist_pos, min_dist_pos]] = indices_2[[min_dist_pos, max_dist_pos]] |
| |
|
| | print(np.sum(np.diagonal(dist))) |
| |
|
| | wb = read_pascal_voc(target) |
| |
|
| | for i in range(len(ann1)): |
| | ann2[indices_2[i]]['text'] = ann1[indices_1[i]]['text'] |
| | bbox_match = [bbox for bbox in wb['bboxes'] |
| | if bbox['xmin'] == ann2[indices_2[i]]['xmin'] and |
| | bbox['xmax'] == ann2[indices_2[i]]['xmax'] and |
| | bbox['ymin'] == ann2[indices_2[i]]['ymin'] and |
| | bbox['ymax'] == ann2[indices_2[i]]['ymax']] |
| |
|
| | if len(bbox_match) == 1: |
| | bbox_match[0]['text'] = ann1[indices_1[i]]['text'] |
| | bbox_match[0]['rotation'] = ann1[indices_1[i]]['rotation'] |
| |
|
| | write_pascal_voc(wb) |
| |
|
| | else: |
| | print("Args: source target") |
| |
|
