def test_identifier():
print('test identifier:')
detector = Detector()
aligner = Aligner()
identifier = Identifier()
# load image
image_color_A = Image.open('data/single.jpg').convert('RGB')
image_gray_A = image_color_A.convert('L')
image_color_B = Image.open('data/double.jpg').convert('RGB')
image_gray_B = image_color_B.convert('L')
# detect face in image
faces_A = detector.detect(image_gray_A)
faces_B = detector.detect(image_gray_B)
draw_A = ImageDraw.Draw(image_color_A)
draw_B = ImageDraw.Draw(image_color_B)
if len(faces_A) and len(faces_B):
landmarks_A = aligner.align(image_gray_A, faces_A[0])
featA = identifier.extract_feature_with_crop(image_color_A,
landmarks_A)
print(len(featA))
draw_A.rectangle([(faces_A[0].left, faces_A[0].top),
(faces_A[0].right, faces_A[0].bottom)],
outline='green')
sim_list = []
for face in faces_B:
landmarks_B = aligner.align(image_gray_B, face)
featB = identifier.extract_feature_with_crop(
image_color_B, landmarks_B)
sim = identifier.calc_similarity(featA, featB)
sim_list.append(sim)
print('sim: {}'.format(sim_list))
index = np.argmax(sim_list)
for i, face in enumerate(faces_B):
color = 'green' if i == index else 'red'
draw_B.rectangle([(face.left, face.top),
(face.right, face.bottom)],
outline=color)
image_color_A.show()
image_color_B.show()
identifier.release()
aligner.release()
detector.release()
def test_identifier():
print('test identifier:')
# load model
detector = Detector()
aligner = Aligner()
identifier = Identifier()
# load image
image_color_A = cv2.imread('data/single.jpg', cv2.IMREAD_COLOR)
image_gray_A = cv2.cvtColor(image_color_A, cv2.COLOR_BGR2GRAY)
image_color_B = cv2.imread('data/double.jpg', cv2.IMREAD_COLOR)
image_gray_B = cv2.cvtColor(image_color_B, cv2.COLOR_BGR2GRAY)
# detect face in image
faces_A = detector.detect(image_gray_A)
faces_B = detector.detect(image_gray_B)
if len(faces_A) and len(faces_B):
landmarks_A = aligner.align(image_gray_A, faces_A[0])
featA = identifier.extract_feature_with_crop(image_color_A,
landmarks_A)
cv2.rectangle(image_color_A, (faces_A[0].left, faces_A[0].top),
(faces_A[0].right, faces_A[0].bottom), (0, 255, 0),
thickness=2)
sim_list = []
for face in faces_B:
landmarks_B = aligner.align(image_gray_B, face)
featB = identifier.extract_feature_with_crop(
image_color_B, landmarks_B)
sim = identifier.calc_similarity(featA, featB)
sim_list.append(sim)
print('sim: {}'.format(sim_list))
index = np.argmax(sim_list)
for i, face in enumerate(faces_B):
color = (0, 255, 0) if i == index else (0, 0, 255)
cv2.rectangle(image_color_B, (face.left, face.top),
(face.right, face.bottom),
color,
thickness=2)
cv2.imshow('single', image_color_A)
cv2.imshow('double', image_color_B)
cv2.waitKey(0)
identifier.release()
aligner.release()
detector.release()
def test_recognition(img_path,db_path):
if not os.path.isfile(img_path) or not os.path.isdir(db_path):
print("indicated path doesn't exist!");return
# load model
detector = Detector(DET_MODEL_PATH)
aligner = Aligner(ALI_MODEL_PATH)
identifier = Identifier(REC_MODEL_PATH)
# load test image
image_color_A = cv2.imread(img_path, cv2.IMREAD_COLOR)
image_gray_A = cv2.cvtColor(image_color_A, cv2.COLOR_BGR2GRAY)
faces_A = detector.detect(image_gray_A)
# load database
for fn in os.listdir(db_path):
fp = os.path.join(db_path,fn)
if not os.path.isfile(fp): continue
image_color_B = cv2.imread(fp, cv2.IMREAD_COLOR)
image_gray_B = cv2.cvtColor(image_color_B, cv2.COLOR_BGR2GRAY)
# detect face in image
faces_B = detector.detect(image_gray_B)
if len(faces_A) and len(faces_B):
landmarks_A = aligner.align(image_gray_A, faces_A[0])
featA = identifier.extract_feature_with_crop(image_color_A, landmarks_A)
# cv2.rectangle(image_color_A, (faces_A[0].left, faces_A[0].top), (faces_A[0].right, faces_A[0].bottom), (0,255,0), thickness=2)
sim_list = []
for face in faces_B:
landmarks_B = aligner.align(image_gray_B, face)
featB = identifier.extract_feature_with_crop(image_color_B, landmarks_B)
sim = identifier.calc_similarity(featA, featB)
sim_list.append(sim)
print('sim: {}'.format(sim_list))
# index = np.argmax(sim_list)
for i, face in enumerate(faces_B):
color = (0,255,0) if sim_list[i] > 0.5 else (0,0,255)
cv2.rectangle(image_color_B, (face.left, face.top), (face.right, face.bottom), color, thickness=2)
# cv2.imshow('test', resize(image_color_A))
cv2.imshow('double', resize(image_color_B))
cv2.waitKey(0)
release(aligner,detector,identifier)
def test_identifier(model_path, img1_name, img2_name):
print('test identifier:')
if model_path is None:
print('test_identifier: Please specify the path to the model folder')
return
if img1_name is None:
print('test_identifier: Please specify the path to the image file1')
return
if img2_name is None:
print('test_identifier: Please specify the path to the image file2')
return
assert os.path.isdir(
model_path
) is True, 'test_identifier: The model file path should be a folder'
assert os.path.isfile(
img1_name) is True, 'test_identifier: no such image file1'
assert os.path.isfile(
img2_name) is True, 'test_identifier: no such image file2'
# load model
model_path_fd = model_path + '/seeta_fd_frontal_v1.0.bin'
model_path_fa = model_path + '/seeta_fa_v1.1.bin'
model_path_fr = model_path + '/seeta_fr_v1.0.bin'
detector = Detector(model_path_fd)
aligner = Aligner(model_path_fa)
identifier = Identifier(model_path_fr)
# load image
image_color_A = cv2.imread(img1_name, cv2.IMREAD_COLOR)
image_gray_A = cv2.cvtColor(image_color_A, cv2.COLOR_BGR2GRAY)
image_color_B = cv2.imread(img2_name, cv2.IMREAD_COLOR)
image_gray_B = cv2.cvtColor(image_color_B, cv2.COLOR_BGR2GRAY)
# detect face in image
faces_A = detector.detect(image_gray_A)
faces_B = detector.detect(image_gray_B)
if len(faces_A) and len(faces_B):
landmarks_A = aligner.align(image_gray_A, faces_A[0])
featA = identifier.extract_feature_with_crop(image_color_A,
landmarks_A)
cv2.rectangle(image_color_A, (faces_A[0].left, faces_A[0].top),
(faces_A[0].right, faces_A[0].bottom), (0, 255, 0),
thickness=2)
sim_list = []
for face in faces_B:
landmarks_B = aligner.align(image_gray_B, face)
featB = identifier.extract_feature_with_crop(
image_color_B, landmarks_B)
sim = identifier.calc_similarity(featA, featB)
sim_list.append(sim)
print('sim: {}'.format(sim_list))
index = np.argmax(sim_list)
for i, face in enumerate(faces_B):
color = (0, 255, 0) if i == index else (0, 0, 255)
cv2.rectangle(image_color_B, (face.left, face.top),
(face.right, face.bottom),
color,
thickness=2)
cv2.imshow('single', image_color_A)
cv2.imshow('double', image_color_B)
cv2.waitKey(0)
identifier.release()
aligner.release()
detector.release()
def test_identifier(model_path, img1_name, img2_name):
print('test identifier:')
if model_path is None:
print('test_identifier: Please specify the path to the model folder')
return
if img1_name is None:
print('test_identifier: Please specify the path to the image file1')
return
if img2_name is None:
print('test_identifier: Please specify the path to the image file2')
return
assert os.path.isdir(
model_path
) is True, 'test_identifier: The model file path should be a folder'
assert os.path.isfile(
img1_name) is True, 'test_identifier: no such image file1'
assert os.path.isfile(
img2_name) is True, 'test_identifier: no such image file2'
model_path_fd = model_path + '/seeta_fd_frontal_v1.0.bin'
model_path_fa = model_path + '/seeta_fa_v1.1.bin'
model_path_fr = model_path + '/seeta_fr_v1.0.bin'
detector = Detector(model_path_fd)
detector.set_min_face_size(30)
aligner = Aligner(model_path_fa)
identifier = Identifier(model_path_fr)
# load image
image_color_A = Image.open(img1_name).convert('RGB')
image_gray_A = image_color_A.convert('L')
image_color_B = Image.open(img2_name).convert('RGB')
image_gray_B = image_color_B.convert('L')
# detect face in image
faces_A = detector.detect(image_gray_A)
faces_B = detector.detect(image_gray_B)
draw_A = ImageDraw.Draw(image_color_A)
draw_B = ImageDraw.Draw(image_color_B)
if len(faces_A) and len(faces_B):
landmarks_A = aligner.align(image_gray_A, faces_A[0])
featA = identifier.extract_feature_with_crop(image_color_A,
landmarks_A)
draw_A.rectangle([(faces_A[0].left, faces_A[0].top),
(faces_A[0].right, faces_A[0].bottom)],
outline='green')
sim_list = []
for face in faces_B:
landmarks_B = aligner.align(image_gray_B, face)
featB = identifier.extract_feature_with_crop(
image_color_B, landmarks_B)
sim = identifier.calc_similarity(featA, featB)
sim_list.append(sim)
print('sim: {}'.format(sim_list))
index = np.argmax(sim_list)
for i, face in enumerate(faces_B):
color = 'green' if i == index else 'red'
draw_B.rectangle([(face.left, face.top),
(face.right, face.bottom)],
outline=color)
image_color_A.show()
image_color_B.show()
identifier.release()
aligner.release()
detector.release()