mirror of
https://github.com/opencv/opencv.git
synced 2024-12-15 09:49:13 +08:00
f4d55d512f
* imgproc: fix bit-exact GaussianBlur() / sepFilter2D() - avoid kernels with bad approximation - GaussiabBlur - apply error-diffusion approximation for kernel (8-bit fraction) * java(test): update features2d ref data * test: update test_facedetect
93 lines
2.7 KiB
Python
93 lines
2.7 KiB
Python
#!/usr/bin/env python
|
|
|
|
'''
|
|
face detection using haar cascades
|
|
'''
|
|
|
|
# Python 2/3 compatibility
|
|
from __future__ import print_function
|
|
|
|
import numpy as np
|
|
import cv2 as cv
|
|
|
|
def detect(img, cascade):
|
|
rects = cascade.detectMultiScale(img, scaleFactor=1.275, minNeighbors=4, minSize=(30, 30),
|
|
flags=cv.CASCADE_SCALE_IMAGE)
|
|
if len(rects) == 0:
|
|
return []
|
|
rects[:,2:] += rects[:,:2]
|
|
return rects
|
|
|
|
from tests_common import NewOpenCVTests, intersectionRate
|
|
|
|
class facedetect_test(NewOpenCVTests):
|
|
|
|
def test_facedetect(self):
|
|
cascade_fn = self.repoPath + '/data/haarcascades/haarcascade_frontalface_alt.xml'
|
|
nested_fn = self.repoPath + '/data/haarcascades/haarcascade_eye.xml'
|
|
|
|
cascade = cv.CascadeClassifier(cascade_fn)
|
|
nested = cv.CascadeClassifier(nested_fn)
|
|
|
|
samples = ['samples/data/lena.jpg', 'cv/cascadeandhog/images/mona-lisa.png']
|
|
|
|
faces = []
|
|
eyes = []
|
|
|
|
testFaces = [
|
|
#lena
|
|
[[218, 200, 389, 371],
|
|
[ 244, 240, 294, 290],
|
|
[ 309, 246, 352, 289]],
|
|
|
|
#lisa
|
|
[[167, 119, 307, 259],
|
|
[188, 153, 229, 194],
|
|
[236, 153, 277, 194]]
|
|
]
|
|
|
|
for sample in samples:
|
|
|
|
img = self.get_sample( sample)
|
|
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
|
|
gray = cv.GaussianBlur(gray, (5, 5), 0)
|
|
|
|
rects = detect(gray, cascade)
|
|
faces.append(rects)
|
|
|
|
if not nested.empty():
|
|
for x1, y1, x2, y2 in rects:
|
|
roi = gray[y1:y2, x1:x2]
|
|
subrects = detect(roi.copy(), nested)
|
|
|
|
for rect in subrects:
|
|
rect[0] += x1
|
|
rect[2] += x1
|
|
rect[1] += y1
|
|
rect[3] += y1
|
|
|
|
eyes.append(subrects)
|
|
|
|
faces_matches = 0
|
|
eyes_matches = 0
|
|
|
|
eps = 0.8
|
|
|
|
for i in range(len(faces)):
|
|
for j in range(len(testFaces)):
|
|
if intersectionRate(faces[i][0], testFaces[j][0]) > eps:
|
|
faces_matches += 1
|
|
#check eyes
|
|
if len(eyes[i]) == 2:
|
|
if intersectionRate(eyes[i][0], testFaces[j][1]) > eps and intersectionRate(eyes[i][1] , testFaces[j][2]) > eps:
|
|
eyes_matches += 1
|
|
elif intersectionRate(eyes[i][1], testFaces[j][1]) > eps and intersectionRate(eyes[i][0], testFaces[j][2]) > eps:
|
|
eyes_matches += 1
|
|
|
|
self.assertEqual(faces_matches, 2)
|
|
self.assertEqual(eyes_matches, 2)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
NewOpenCVTests.bootstrap()
|