90 lines
2.7 KiB
Python
90 lines
2.7 KiB
Python
#!/usr/bin/env python
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'''
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face detection using haar cascades
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'''
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# Python 2/3 compatibility
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from __future__ import print_function
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import numpy as np
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import cv2
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def detect(img, cascade):
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rects = cascade.detectMultiScale(img, scaleFactor=1.3, minNeighbors=4, minSize=(30, 30),
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flags=cv2.CASCADE_SCALE_IMAGE)
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if len(rects) == 0:
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return []
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rects[:,2:] += rects[:,:2]
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return rects
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from tests_common import NewOpenCVTests, intersectionRate
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class facedetect_test(NewOpenCVTests):
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def test_facedetect(self):
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import sys, getopt
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cascade_fn = self.repoPath + '/data/haarcascades/haarcascade_frontalface_alt.xml'
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nested_fn = self.repoPath + '/data/haarcascades/haarcascade_eye.xml'
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cascade = cv2.CascadeClassifier(cascade_fn)
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nested = cv2.CascadeClassifier(nested_fn)
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samples = ['samples/data/lena.jpg', 'cv/cascadeandhog/images/mona-lisa.png']
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faces = []
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eyes = []
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testFaces = [
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#lena
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[[218, 200, 389, 371],
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[ 244, 240, 294, 290],
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[ 309, 246, 352, 289]],
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#lisa
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[[167, 119, 307, 259],
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[188, 153, 229, 194],
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[236, 153, 277, 194]]
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]
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for sample in samples:
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img = self.get_sample( sample)
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gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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gray = cv2.GaussianBlur(gray, (5, 5), 5.1)
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rects = detect(gray, cascade)
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faces.append(rects)
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if not nested.empty():
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for x1, y1, x2, y2 in rects:
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roi = gray[y1:y2, x1:x2]
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subrects = detect(roi.copy(), nested)
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for rect in subrects:
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rect[0] += x1
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rect[2] += x1
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rect[1] += y1
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rect[3] += y1
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eyes.append(subrects)
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faces_matches = 0
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eyes_matches = 0
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eps = 0.8
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for i in range(len(faces)):
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for j in range(len(testFaces)):
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if intersectionRate(faces[i][0], testFaces[j][0]) > eps:
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faces_matches += 1
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#check eyes
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if len(eyes[i]) == 2:
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if intersectionRate(eyes[i][0], testFaces[j][1]) > eps and intersectionRate(eyes[i][1] , testFaces[j][2]) > eps:
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eyes_matches += 1
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elif intersectionRate(eyes[i][1], testFaces[j][1]) > eps and intersectionRate(eyes[i][0], testFaces[j][2]) > eps:
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eyes_matches += 1
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self.assertEqual(faces_matches, 2)
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self.assertEqual(eyes_matches, 2) |