def test_prediction(self):
base_path = '..' + os.sep + 'test_files' + os.sep + 'face_models'
base_path = os.path.join('..', 'test_files', 'face_models')
face_rec_data = os.path.abspath(os.path.join(base_path, 'face_rec_data'))
params = {c.GLOBAL_FACE_MODELS_MIN_DIFF_KEY: -1,
c.GLOBAL_FACE_REC_DATA_DIR_PATH_KEY: face_rec_data}
fm = FaceModels(params)
self.test_add_face()
fm.load_models()
im_path = os.path.join(base_path, '0000000_aligned.png')
face = cv2.imread(im_path, cv2.IMREAD_GRAYSCALE)
(label, conf) = fm.recognize_face(face)
print 'Predicted label = %s (confidence=%.2f)' % (label, conf)
self.assertEquals(label, 3812)
im_path = os.path.join(base_path, '0000002_aligned.png')
face = cv2.imread(im_path, cv2.IMREAD_GRAYSCALE)
(tag, conf) = fm.recognize_face(face)
print 'Predicted tag = %s (confidence=%.2f)' % (tag, conf)
self.assertEquals(tag, 3815)
# Detect face in second image
result = detect_faces_in_image(
image_path_2, align_path, params, show_results=False)
# Recognize aligned face detected in second image
if result and c.FACES_KEY in result:
faces = result[c.FACES_KEY]
if len(faces) == 1:
# Get aligned face and calculate distance between faces
file_name = faces[0][c.ALIGNED_FACE_FILE_NAME_KEY]
file_name_complete = file_name + '_gray.png'
file_path = os.path.join(align_path, file_name_complete)
aligned_face = cv2.imread(file_path, cv2.IMREAD_GRAYSCALE)
(label, conf) = fm.recognize_face(aligned_face)
# Draw bounding box around face
img = cv2.imread(image_path_2)
(x, y, w, h) = faces[0][c.BBOX_KEY]
cv2.rectangle(img, (x, y), (x + w, y + h), (0, 0, 255), 3, 8, 0)
# Write distance from reference image
cv2.putText(img, str(conf), (x, y + h + 30),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
cv2.imshow('Image', img)
cv2.waitKey(0)
image_path_2 = os.path.splitext(image_path_2)[0]
image_path_2 = image_path_2 + '_comparison.png'
def main(argv):
# Set path of images to be analyzed
image_path_1 = argv[1]
print "image_path_1 ", image_path_1
image_path_2 = argv[2]
print "image_path_2 ", image_path_2
# Set path of directory where aligned faces will be saved
align_path = '/tmp'
# Set parameters
params = {
c.CHECK_EYE_POSITIONS_KEY: True,
c.CLASSIFIERS_DIR_PATH_KEY: '/home/active/gitactive/ACTIVE/face_extractor/examples/haarcascades',
c.CROPPED_FACE_HEIGHT_KEY: 250,
c.CROPPED_FACE_WIDTH_KEY: 200,
c.EYE_DETECTION_CLASSIFIER_KEY: 'haarcascade_mcs_lefteye.xml',
c.FACE_DETECTION_ALGORITHM_KEY: 'HaarCascadeFrontalFaceAlt2',
c.FLAGS_KEY: 'DoCannyPruning',
c.MIN_NEIGHBORS_KEY: 5,
c.MIN_SIZE_HEIGHT_KEY: 20,
c.MIN_SIZE_WIDTH_KEY: 20,
c.OFFSET_PCT_X_KEY: 0.30,
c.OFFSET_PCT_Y_KEY: 0.42,
c.SCALE_FACTOR_KEY: 1.1,
c.MAX_EYE_ANGLE_KEY: 0.125,
c.MIN_EYE_DISTANCE_KEY: 0.25,
c.USE_EYES_POSITION_KEY: True,
c.USE_NOSE_POS_IN_DETECTION_KEY: False,
c.ALIGNED_FACES_PATH_KEY: align_path,
c.GLOBAL_FACE_REC_DATA_DIR_PATH_KEY: '/home/active/gitactive/ACTIVE/face_extractor/examples/face_rec_data2',
c.LBP_GRID_X_KEY: 4,
c.LBP_GRID_Y_KEY: 5,
c.LBP_NEIGHBORS_KEY: 8,
c.LBP_RADIUS_KEY: 1
}
fm = FaceModels(params)
# Delete possible already existing models
fm.delete_models()
# Add face from first image to face models
label = 0
tag = ''
fm.add_face(label, tag, image_path_1)
# Detect face in second image
result = detect_faces_in_image(
image_path_2, align_path, params, show_results=False)
# Recognize aligned face detected in second image
if result and c.FACES_KEY in result:
faces = result[c.FACES_KEY]
if len(faces) == 1:
# Get aligned face and calculate distance between faces
file_name = faces[0][c.ALIGNED_FACE_FILE_NAME_KEY]
file_name_complete = file_name + '_gray.png'
file_path = os.path.join(align_path, file_name_complete)
aligned_face = cv2.imread(file_path, cv2.IMREAD_GRAYSCALE)
(label, conf) = fm.recognize_face(aligned_face)
# Draw bounding box around face
img = cv2.imread(image_path_2)
(x, y, w, h) = faces[0][c.BBOX_KEY]
cv2.rectangle(img, (x, y), (x + w, y + h), (0, 0, 255), 3, 8, 0)
# Write distance from reference image
cv2.putText(img, str(conf), (x, y + h + 30),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
print "distance ", conf
cv2.imshow('Image', img)
cv2.waitKey(0)
image_path_2 = os.path.splitext(image_path_2)[0]
image_path_2 = image_path_2 + '_comparison.png'
cv2.imwrite(image_path_2, img)
