mirror of
https://github.com/PaddlePaddle/PaddleOCR.git
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347 lines
14 KiB
Python
347 lines
14 KiB
Python
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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import math
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from collections import namedtuple
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import numpy as np
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from shapely.geometry import Polygon
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class DetectionICDAR2013Evaluator(object):
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def __init__(self,
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area_recall_constraint=0.8,
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area_precision_constraint=0.4,
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ev_param_ind_center_diff_thr=1,
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mtype_oo_o=1.0,
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mtype_om_o=0.8,
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mtype_om_m=1.0):
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self.area_recall_constraint = area_recall_constraint
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self.area_precision_constraint = area_precision_constraint
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self.ev_param_ind_center_diff_thr = ev_param_ind_center_diff_thr
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self.mtype_oo_o = mtype_oo_o
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self.mtype_om_o = mtype_om_o
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self.mtype_om_m = mtype_om_m
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def evaluate_image(self, gt, pred):
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def get_union(pD, pG):
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return Polygon(pD).union(Polygon(pG)).area
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def get_intersection_over_union(pD, pG):
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return get_intersection(pD, pG) / get_union(pD, pG)
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def get_intersection(pD, pG):
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return Polygon(pD).intersection(Polygon(pG)).area
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def one_to_one_match(row, col):
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cont = 0
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for j in range(len(recallMat[0])):
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if recallMat[row,
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j] >= self.area_recall_constraint and precisionMat[
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row, j] >= self.area_precision_constraint:
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cont = cont + 1
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if (cont != 1):
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return False
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cont = 0
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for i in range(len(recallMat)):
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if recallMat[
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i, col] >= self.area_recall_constraint and precisionMat[
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i, col] >= self.area_precision_constraint:
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cont = cont + 1
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if (cont != 1):
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return False
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if recallMat[row,
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col] >= self.area_recall_constraint and precisionMat[
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row, col] >= self.area_precision_constraint:
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return True
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return False
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def one_to_many_match(gtNum):
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many_sum = 0
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detRects = []
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for detNum in range(len(recallMat[0])):
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if gtRectMat[gtNum] == 0 and detRectMat[
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detNum] == 0 and detNum not in detDontCareRectsNum:
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if precisionMat[gtNum,
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detNum] >= self.area_precision_constraint:
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many_sum += recallMat[gtNum, detNum]
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detRects.append(detNum)
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if round(many_sum, 4) >= self.area_recall_constraint:
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return True, detRects
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else:
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return False, []
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def many_to_one_match(detNum):
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many_sum = 0
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gtRects = []
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for gtNum in range(len(recallMat)):
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if gtRectMat[gtNum] == 0 and detRectMat[
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detNum] == 0 and gtNum not in gtDontCareRectsNum:
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if recallMat[gtNum, detNum] >= self.area_recall_constraint:
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many_sum += precisionMat[gtNum, detNum]
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gtRects.append(gtNum)
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if round(many_sum, 4) >= self.area_precision_constraint:
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return True, gtRects
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else:
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return False, []
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def center_distance(r1, r2):
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return ((np.mean(r1, axis=0) - np.mean(r2, axis=0))**2).sum()**0.5
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def diag(r):
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r = np.array(r)
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return ((r[:, 0].max() - r[:, 0].min())**2 +
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(r[:, 1].max() - r[:, 1].min())**2)**0.5
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perSampleMetrics = {}
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recall = 0
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precision = 0
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hmean = 0
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recallAccum = 0.
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precisionAccum = 0.
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gtRects = []
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detRects = []
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gtPolPoints = []
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detPolPoints = []
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gtDontCareRectsNum = [
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] #Array of Ground Truth Rectangles' keys marked as don't Care
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detDontCareRectsNum = [
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] #Array of Detected Rectangles' matched with a don't Care GT
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pairs = []
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evaluationLog = ""
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recallMat = np.empty([1, 1])
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precisionMat = np.empty([1, 1])
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for n in range(len(gt)):
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points = gt[n]['points']
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# transcription = gt[n]['text']
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dontCare = gt[n]['ignore']
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if not Polygon(points).is_valid or not Polygon(points).is_simple:
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continue
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gtRects.append(points)
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gtPolPoints.append(points)
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if dontCare:
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gtDontCareRectsNum.append(len(gtRects) - 1)
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evaluationLog += "GT rectangles: " + str(len(gtRects)) + (
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" (" + str(len(gtDontCareRectsNum)) + " don't care)\n"
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if len(gtDontCareRectsNum) > 0 else "\n")
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for n in range(len(pred)):
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points = pred[n]['points']
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if not Polygon(points).is_valid or not Polygon(points).is_simple:
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continue
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detRect = points
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detRects.append(detRect)
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detPolPoints.append(points)
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if len(gtDontCareRectsNum) > 0:
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for dontCareRectNum in gtDontCareRectsNum:
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dontCareRect = gtRects[dontCareRectNum]
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intersected_area = get_intersection(dontCareRect, detRect)
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rdDimensions = Polygon(detRect).area
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if (rdDimensions == 0):
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precision = 0
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else:
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precision = intersected_area / rdDimensions
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if (precision > self.area_precision_constraint):
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detDontCareRectsNum.append(len(detRects) - 1)
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break
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evaluationLog += "DET rectangles: " + str(len(detRects)) + (
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" (" + str(len(detDontCareRectsNum)) + " don't care)\n"
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if len(detDontCareRectsNum) > 0 else "\n")
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if len(gtRects) == 0:
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recall = 1
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precision = 0 if len(detRects) > 0 else 1
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if len(detRects) > 0:
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#Calculate recall and precision matrixs
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outputShape = [len(gtRects), len(detRects)]
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recallMat = np.empty(outputShape)
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precisionMat = np.empty(outputShape)
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gtRectMat = np.zeros(len(gtRects), np.int8)
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detRectMat = np.zeros(len(detRects), np.int8)
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for gtNum in range(len(gtRects)):
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for detNum in range(len(detRects)):
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rG = gtRects[gtNum]
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rD = detRects[detNum]
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intersected_area = get_intersection(rG, rD)
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rgDimensions = Polygon(rG).area
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rdDimensions = Polygon(rD).area
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recallMat[
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gtNum,
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detNum] = 0 if rgDimensions == 0 else intersected_area / rgDimensions
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precisionMat[
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gtNum,
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detNum] = 0 if rdDimensions == 0 else intersected_area / rdDimensions
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# Find one-to-one matches
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evaluationLog += "Find one-to-one matches\n"
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for gtNum in range(len(gtRects)):
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for detNum in range(len(detRects)):
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if gtRectMat[gtNum] == 0 and detRectMat[
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detNum] == 0 and gtNum not in gtDontCareRectsNum and detNum not in detDontCareRectsNum:
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match = one_to_one_match(gtNum, detNum)
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if match is True:
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#in deteval we have to make other validation before mark as one-to-one
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rG = gtRects[gtNum]
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rD = detRects[detNum]
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normDist = center_distance(rG, rD)
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normDist /= diag(rG) + diag(rD)
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normDist *= 2.0
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if normDist < self.ev_param_ind_center_diff_thr:
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gtRectMat[gtNum] = 1
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detRectMat[detNum] = 1
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recallAccum += self.mtype_oo_o
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precisionAccum += self.mtype_oo_o
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pairs.append({
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'gt': gtNum,
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'det': detNum,
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'type': 'OO'
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})
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evaluationLog += "Match GT #" + str(
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gtNum) + " with Det #" + str(detNum) + "\n"
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else:
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evaluationLog += "Match Discarded GT #" + str(
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gtNum) + " with Det #" + str(
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detNum) + " normDist: " + str(
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normDist) + " \n"
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# Find one-to-many matches
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evaluationLog += "Find one-to-many matches\n"
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for gtNum in range(len(gtRects)):
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if gtNum not in gtDontCareRectsNum:
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match, matchesDet = one_to_many_match(gtNum)
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if match is True:
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evaluationLog += "num_overlaps_gt=" + str(
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num_overlaps_gt(gtNum))
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gtRectMat[gtNum] = 1
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recallAccum += (self.mtype_oo_o if len(matchesDet) == 1
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else self.mtype_om_o)
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precisionAccum += (self.mtype_oo_o
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if len(matchesDet) == 1 else
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self.mtype_om_o * len(matchesDet))
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pairs.append({
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'gt': gtNum,
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'det': matchesDet,
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'type': 'OO' if len(matchesDet) == 1 else 'OM'
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})
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for detNum in matchesDet:
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detRectMat[detNum] = 1
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evaluationLog += "Match GT #" + str(
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gtNum) + " with Det #" + str(matchesDet) + "\n"
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# Find many-to-one matches
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evaluationLog += "Find many-to-one matches\n"
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for detNum in range(len(detRects)):
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if detNum not in detDontCareRectsNum:
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match, matchesGt = many_to_one_match(detNum)
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if match is True:
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detRectMat[detNum] = 1
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recallAccum += (self.mtype_oo_o if len(matchesGt) == 1
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else self.mtype_om_m * len(matchesGt))
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precisionAccum += (self.mtype_oo_o
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if len(matchesGt) == 1 else
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self.mtype_om_m)
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pairs.append({
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'gt': matchesGt,
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'det': detNum,
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'type': 'OO' if len(matchesGt) == 1 else 'MO'
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})
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for gtNum in matchesGt:
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gtRectMat[gtNum] = 1
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evaluationLog += "Match GT #" + str(
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matchesGt) + " with Det #" + str(detNum) + "\n"
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numGtCare = (len(gtRects) - len(gtDontCareRectsNum))
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if numGtCare == 0:
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recall = float(1)
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precision = float(0) if len(detRects) > 0 else float(1)
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else:
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recall = float(recallAccum) / numGtCare
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precision = float(0) if (
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len(detRects) - len(detDontCareRectsNum)
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) == 0 else float(precisionAccum) / (
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len(detRects) - len(detDontCareRectsNum))
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hmean = 0 if (precision + recall
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) == 0 else 2.0 * precision * recall / (
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precision + recall)
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numGtCare = len(gtRects) - len(gtDontCareRectsNum)
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numDetCare = len(detRects) - len(detDontCareRectsNum)
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perSampleMetrics = {
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'precision': precision,
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'recall': recall,
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'hmean': hmean,
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'pairs': pairs,
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'recallMat': [] if len(detRects) > 100 else recallMat.tolist(),
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'precisionMat': []
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if len(detRects) > 100 else precisionMat.tolist(),
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'gtPolPoints': gtPolPoints,
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'detPolPoints': detPolPoints,
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'gtCare': numGtCare,
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'detCare': numDetCare,
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'gtDontCare': gtDontCareRectsNum,
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'detDontCare': detDontCareRectsNum,
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'recallAccum': recallAccum,
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'precisionAccum': precisionAccum,
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'evaluationLog': evaluationLog
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}
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return perSampleMetrics
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def combine_results(self, results):
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numGt = 0
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numDet = 0
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methodRecallSum = 0
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methodPrecisionSum = 0
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for result in results:
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numGt += result['gtCare']
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numDet += result['detCare']
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methodRecallSum += result['recallAccum']
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methodPrecisionSum += result['precisionAccum']
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methodRecall = 0 if numGt == 0 else methodRecallSum / numGt
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methodPrecision = 0 if numDet == 0 else methodPrecisionSum / numDet
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methodHmean = 0 if methodRecall + methodPrecision == 0 else 2 * methodRecall * methodPrecision / (
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methodRecall + methodPrecision)
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methodMetrics = {
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'precision': methodPrecision,
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'recall': methodRecall,
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'hmean': methodHmean
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}
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return methodMetrics
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if __name__ == '__main__':
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evaluator = DetectionICDAR2013Evaluator()
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gts = [[{
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'points': [(0, 0), (1, 0), (1, 1), (0, 1)],
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'text': 1234,
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'ignore': False,
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}, {
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'points': [(2, 2), (3, 2), (3, 3), (2, 3)],
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'text': 5678,
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'ignore': True,
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}]]
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preds = [[{
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'points': [(0.1, 0.1), (1, 0), (1, 1), (0, 1)],
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'text': 123,
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'ignore': False,
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}]]
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results = []
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for gt, pred in zip(gts, preds):
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results.append(evaluator.evaluate_image(gt, pred))
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metrics = evaluator.combine_results(results)
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print(metrics)
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