def testDetectFaces(self):
'''
Go through two images, the first with 1 face, the second with 4 faces
Assert that the detected faces are correct, and draw them.
Also creates output images of the padded faces
'''
fnames = ['./resources/cascade/Fayssal_Mekdad_0002.jpg', './resources/cascade/family-home.png']
expected_faces = [[],[]]
expected_faces[0].append(CascadeResult(box_with_score = ([61,62,132,132], 344), cascade_type = 'haar', angle = 0.0))
expected_faces[1].append(CascadeResult(box_with_score = ([327,101,119,119], 244), cascade_type = 'haar', angle = 0.0))
expected_faces[1].append(CascadeResult(box_with_score = ([238,107,111,111], 135), cascade_type = 'lbp', angle = 0.0))
expected_faces[1].append(CascadeResult(box_with_score = ([163,48,93,93], 51), cascade_type = 'lbp', angle = 0.0))
expected_faces[1].append(CascadeResult(box_with_score = ([433,86,95,95], 92), cascade_type = 'lbp', angle = 0.0))
for n_images, fname in enumerate(fnames):
_, base_fname = os.path.split(fname)
img = cv2.imread(fname)
gray_img = cv2.imread(fname, 0)
face_finder = CascadeFaceFinder(haar_file = '../resources/haarcascade_frontalface_default.xml',
lbp_file = '../resources/lbpcascade_frontalface.xml')
faces = face_finder.get_faces_list_in_photo(gray_img)
img_to_draw_on = img.copy()
for n_face, face in enumerate(faces):
self.assertAlmostEqual(face.overlap(expected_faces[n_images][n_face]) / face.area, 1.00, 0.01)
draw_rect(img_to_draw_on, face)
padded_face, bounding_box_in_padded_face, _, _ = extract_box(img, face, padding_factor = 0.25)
new_face_file = os.path.join('./outputs/cascade/1/', base_fname.split('.')[0] + '.face.%d.png' %n_face)
cv2.imwrite(new_face_file, padded_face)
padded_face_loaded = cv2.imread(new_face_file)
draw_rect(padded_face_loaded, bounding_box_in_padded_face)
cv2.imshow('face %d' %n_face, padded_face_loaded)
cv2.waitKey()
cv2.imshow('faces detected', img_to_draw_on)
cv2.waitKey()
def testDetectFaces(self):
'''
Go through two images, the first with 1 face, the second with 4 faces
Assert that the detected faces are correct, and draw them.
Also creates output images of the padded faces
'''
fnames = ['./resources/cascade/janice2.jpg',
'./resources/cascade/albert4.jpg',
'./resources/cascade/albert5.jpg',
'./resources/cascade/david1.jpg']
for n_images, fname in enumerate(fnames):
_, base_fname = os.path.split(fname)
img = cv2.imread(fname)
gray_img = cv2.imread(fname, 0)
face_finder = CascadeFaceFinder(haar_file = '../resources/haarcascade_frontalface_default.xml',
lbp_file = '../resources/lbpcascade_frontalface.xml')
faces = face_finder.get_faces_list_in_photo(gray_img)
img_to_draw_on = img.copy()
for n_face, face in enumerate(faces):
draw_rect(img_to_draw_on, face)
padded_face, bounding_box_in_padded_face, _, _ = extract_box(img, face, padding_factor = 0.25)
new_face_file = os.path.join('./outputs/cascade/1/', base_fname.split('.')[0] + '.face.%d.png' %n_face)
cv2.imwrite(new_face_file, padded_face)
padded_face_loaded = cv2.imread(new_face_file)
draw_rect(padded_face_loaded, bounding_box_in_padded_face)
cv2.imshow('face %d' %n_face, padded_face_loaded)
cv2.waitKey()
cv2.imshow('faces detected', img_to_draw_on)
cv2.waitKey()
def testDetectFaces(self):
'''
Go through two images, the first with 1 face, the second with 4 faces
Assert that the detected faces are correct, and draw them.
Also creates output images of the padded faces
'''
fnames = [
'./resources/cascade/Fayssal_Mekdad_0002.jpg',
'./resources/cascade/family-home.png'
]
expected_faces = [[], []]
expected_faces[0].append(
CascadeResult(box_with_score=([61, 62, 132, 132], 344),
cascade_type='haar',
angle=0.0))
expected_faces[1].append(
CascadeResult(box_with_score=([327, 101, 119, 119], 244),
cascade_type='haar',
angle=0.0))
expected_faces[1].append(
CascadeResult(box_with_score=([238, 107, 111, 111], 135),
cascade_type='lbp',
angle=0.0))
expected_faces[1].append(
CascadeResult(box_with_score=([163, 48, 93, 93], 51),
cascade_type='lbp',
angle=0.0))
expected_faces[1].append(
CascadeResult(box_with_score=([433, 86, 95, 95], 92),
cascade_type='lbp',
angle=0.0))
for n_images, fname in enumerate(fnames):
_, base_fname = os.path.split(fname)
img = cv2.imread(fname)
gray_img = cv2.imread(fname, 0)
face_finder = CascadeFaceFinder(
haar_file='../resources/haarcascade_frontalface_default.xml',
lbp_file='../resources/lbpcascade_frontalface.xml')
faces = face_finder.get_faces_list_in_photo(gray_img)
img_to_draw_on = img.copy()
for n_face, face in enumerate(faces):
self.assertAlmostEqual(
face.overlap(expected_faces[n_images][n_face]) / face.area,
1.00, 0.01)
draw_rect(img_to_draw_on, face)
padded_face, bounding_box_in_padded_face, _, _ = extract_box(
img, face, padding_factor=0.25)
new_face_file = os.path.join(
'./outputs/cascade/1/',
base_fname.split('.')[0] + '.face.%d.png' % n_face)
cv2.imwrite(new_face_file, padded_face)
padded_face_loaded = cv2.imread(new_face_file)
draw_rect(padded_face_loaded, bounding_box_in_padded_face)
cv2.imshow('face %d' % n_face, padded_face_loaded)
cv2.waitKey()
cv2.imshow('faces detected', img_to_draw_on)
cv2.waitKey()
