Marmelator/cv-analysis-service
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases 37 Wiki Activity

Refactoring

Browse Source 
Remove code that is not adhering to separation of concerns from Rectangle class
This commit is contained in:
Matthias Bisping 2023-01-04 16:28:53 +01:00
parent 4be91de036
commit 00e53fb54d
2 changed files with 24 additions and 36 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

8
cv_analysis/utils/structures.py
View File

@ -77,14 +77,6 @@ class Rectangle:
y2 = y1 + h
return cls(x1, y1, x2, y2, discrete=discrete)
@classmethod
def from_dict_xywh(cls, xywh: Dict[str, Coord], discrete=True):
"""Creates a rectangle from a point and a width and height."""
x1, y1, w, h = itemgetter("x", "y", "width", "height")(xywh)
x2 = x1 + w
y2 = y1 + h
return cls(x1, y1, x2, y2, discrete=discrete)
def xyxy(self):
return self.x1, self.y1, self.x2, self.y2

52
cv_analysis/utils/test_metrics.py
View File

@ -1,5 +1,10 @@
from operator import itemgetter
from typing import Iterable
import numpy as np
from funcy import lmap, lpluck
from cv_analysis.utils import lift
from cv_analysis.utils.structures import Rectangle
@ -25,37 +30,28 @@ def compute_page_iou(results_boxes: Iterable[Rectangle], ground_truth_boxes: Ite
return score
def compute_document_score(results_dict, annotation_dict):
def compute_document_score(result_dict, ground_truth_dicts):
page_weights = np.array([len(page["cells"]) for page in annotation_dict["pages"]])
page_weights = page_weights / sum(page_weights)
extract_cells = lambda dicts: lpluck("cells", dicts["pages"])
scores = []
for i in range(len(annotation_dict["pages"])):
scores.append(
compute_page_iou(
map(Rectangle.from_dict_xywh, results_dict["pages"][i]["cells"]),
map(Rectangle.from_dict_xywh, annotation_dict["pages"][i]["cells"]),
)
)
cells_per_ground_truth_page, cells_per_result_page = map(extract_cells, (ground_truth_dicts, result_dict))
cells_on_page_to_rectangles = lift(rectangle_from_dict)
cells_on_pages_to_rectangles = lift(cells_on_page_to_rectangles)
doc_score = np.average(np.array(scores), weights=page_weights)
rectangles_per_ground_truth_page, rectangles_per_result_page = map(
cells_on_pages_to_rectangles, (cells_per_ground_truth_page, cells_per_result_page)
)
return doc_score
scores = lmap(compute_page_iou, rectangles_per_result_page, rectangles_per_ground_truth_page)
n_cells_per_page = np.array(lmap(len, cells_per_ground_truth_page))
document_score = np.average(scores, weights=n_cells_per_page / n_cells_per_page.sum())
return document_score
"""
from cv_analysis.utils.test_metrics import *
r1 = Rectangle.from_dict_xywh({'x': 30, 'y': 40, 'width': 50, 'height': 60})
r2 = Rectangle.from_dict_xywh({'x': 40, 'y': 30, 'width': 55, 'height': 65})
r3 = Rectangle.from_dict_xywh({'x': 45, 'y': 35, 'width': 45, 'height': 55})
r4 = Rectangle.from_dict_xywh({'x': 25, 'y': 45, 'width': 45, 'height': 55})
d1 = {"pages": [{"cells": [r1.json_xywh(), r2.json_xywh()]}]}
d2 = {"pages": [{"cells": [r3.json_xywh(), r4.json_xywh()]}]}
compute_iou_from_boxes(r1, r2)
find_max_overlap(r1, [r2, r3, r4])
compute_page_iou([r1, r2], [r3, r4])
compute_document_score(d1, d2)
"""
def rectangle_from_dict(d):
x1, y1, w, h = itemgetter("x", "y", "width", "height")(d)
x2 = x1 + w
y2 = y1 + h
return Rectangle(x1, y1, x2, y2)