def annotate(pose,
face_box_model='mtcnn',
au_model='rf',
face_id_model='deepface',
every=1,
output_path=None,
save_results=True,
shot_detection=True,
extract_aus=True,
extract_face_id=True,
num_workers=None):
########## Run pose estimation ##########
pose_data = estimate_pose(pose, num_workers=num_workers)
# Split tracks based on shot detection
########## Run shot detection ##########
if shot_detection:
tqdm.write("Detecting shots...")
shots = utils.get_shots(pose.vid_path)
# Here, shots is a list of tuples (each tuple contains the in and out frames of each shot)
pose_data, pose.splitcount = utils.split_tracks(pose_data, shots)
pose.shots = shots
# Add pose data to the pose object
pose.pose_data = pose_data
pose.n_tracks = len(pose_data)
########## Run face detection + face feature extraction ##########
if extract_aus:
detector = Detector(face_model=face_box_model, au_model=au_model)
tqdm.write("Extracting facial expressions...")
pose.face_data = detector.detect_video(pose.vid_path,
skip_frames=every)
########## Extract face identify encodings ##########
if extract_aus and extract_face_id:
add_face_id(pose)
########## Saving results ##########
if output_path == None:
output_path = os.getcwd()
if save_results:
os.makedirs(output_path + '/' + pose.vid_name, exist_ok=True)
if extract_aus:
pose.face_data.to_csv(output_path + '/' + pose.vid_name +
'/psypose_faces.csv')
joblib.dump(
pose.pose_data,
os.path.join(output_path + '/' + pose.vid_name +
'/psypose_bodies.pkl'))
print('Finished annotation for file: ', pose.vid_name)
def test_simultaneous():
# Test processing everything:
detector04 = Detector(
face_model="RetinaFace",
emotion_model="fer",
landmark_model="PFLD",
au_model="jaanet",
)
files = detector04.process_frame(img01, 0)
def test_pfld():
detector03 = Detector(face_model="RetinaFace",
emotion_model=None,
landmark_model="PFLD")
bboxes = detector03.detect_faces(img01)
landmarks = detector03.detect_landmarks(img01, bboxes)
assert landmarks[0].shape == (68, 2)
assert (np.any(landmarks[0][:, 0] > 0) and np.any(landmarks[0][:, 0] < w)
and np.any(landmarks[0][:, 1] > 0)
and np.any(landmarks[0][:, 1] < h))
def test_mobilenet():
detector02 = Detector(face_model="RetinaFace",
emotion_model=None,
landmark_model="MobileNet")
bboxes = detector02.detect_faces(img01)
landmarks = detector02.detect_landmarks(img01, bboxes)
assert landmarks[0].shape == (68, 2)
assert (np.any(landmarks[0][:, 0] > 0) and np.any(landmarks[0][:, 0] < w)
and np.any(landmarks[0][:, 1] > 0)
and np.any(landmarks[0][:, 1] < h))
def test_multiface():
inputFname2 = os.path.join(get_test_data_path(),
"tim-mossholder-hOF1bWoet_Q-unsplash.jpg")
img02 = cv2.imread(inputFname2)
detector = Detector(
face_model="RetinaFace",
emotion_model="fer",
landmark_model="PFLD",
au_model="jaanet",
)
files = detector.process_frame(img02, 0)
assert files.shape[0] == 5
def test_jaanet():
# AU Detection Case:
detector1 = Detector(
face_model="RetinaFace",
emotion_model=None,
landmark_model="MobileFaceNet",
au_model="jaanet",
)
bboxes = detector1.detect_faces(img01)
lands = detector1.detect_landmarks(img01, bboxes)
aus = detector1.detect_aus(img01, lands)
assert np.sum(np.isnan(aus)) == 0
assert aus.shape[-1] == 12
def test_pnp():
# Test that facepose can be estimated properly using landmarks + pnp algorithm
detector = Detector(face_model="RetinaFace",
landmark_model="MobileFaceNet",
facepose_model="PnP")
bboxes = detector.detect_faces(frame=img01)
lms = detector.detect_landmarks(frame=img01, detected_faces=bboxes)
poses = detector.detect_facepose(frame=img01, landmarks=lms)
pose_to_test = poses[0][0] # first image and first face
pitch, roll, yaw = pose_to_test.reshape(-1)
assert -10 < pitch < 10
assert -5 < roll < 5
assert -10 < yaw < 10
def test_drml():
# AU Detection Case2:
inputFname = os.path.join(get_test_data_path(), "sampler0000.jpg")
img01 = cv2.imread(inputFname)
detector1 = Detector(
face_model="RetinaFace",
emotion_model=None,
landmark_model="MobileFaceNet",
au_model="drml",
)
bboxes = detector1.detect_faces(img01)
lands = detector1.detect_landmarks(img01, bboxes)
aus = detector1.detect_aus(img01, lands)
assert np.sum(np.isnan(aus)) == 0
assert aus.shape[-1] == 12
def test_detect_image():
# Test detect image
detector = Detector(n_jobs=1)
inputFname = os.path.join(get_test_data_path(), "input.jpg")
out = detector.detect_image(inputFname=inputFname)
assert type(out) == Fex
assert len(out) == 1
assert out.happiness.values[0] > 0
outputFname = os.path.join(get_test_data_path(), "output.csv")
out = detector.detect_image(inputFname=inputFname, outputFname=outputFname)
assert out
assert os.path.exists(outputFname)
out = pd.read_csv(outputFname)
assert out.happiness.values[0] > 0
def test_mtcnn():
detector03 = Detector(
face_model="MTCNN",
landmark_model=None,
au_model=None,
emotion_model=None,
n_jobs=1,
)
out = detector03.detect_faces(img01)
bbox_x = out[0][0]
assert bbox_x != None
bbox_width = out[0][1]
bbox_y = out[0][2]
bbox_height = out[0][3]
assert len(out[0]) == 5
assert bbox_x > 180 and bbox_x < 200
def test_retinaface():
detector02 = Detector(
face_model="RetinaFace",
landmark_model=None,
au_model=None,
emotion_model=None,
n_jobs=1,
)
out = detector02.detect_faces(img01)
bbox_x = out[0][0]
assert bbox_x != None
bbox_width = out[0][1]
bbox_y = out[0][2]
bbox_height = out[0][3]
assert len(out[0]) == 5
assert bbox_x > 180 and bbox_x < 200
def test_faceboxes():
# Face Detector Test Case:
detector01 = Detector(
face_model="FaceBoxes",
landmark_model=None,
au_model=None,
emotion_model=None,
n_jobs=1,
)
out = detector01.detect_faces(img01)
bbox_x = out[0][0]
assert bbox_x != None
bbox_width = out[0][1]
bbox_y = out[0][2]
bbox_height = out[0][3]
assert len(out[0]) == 5
assert bbox_x > 180 and bbox_x < 200
def test_detect_video_parallel():
# Test detect video
detector = Detector(n_jobs=2)
inputFname = os.path.join(get_test_data_path(), "input.mp4")
out = detector.detect_video(inputFname=inputFname,
skip_frames=20,
verbose=True)
assert len(out) == 4
outputFname = os.path.join(get_test_data_path(), "output.csv")
out = detector.detect_video(inputFname=inputFname,
outputFname=outputFname,
skip_frames=10)
assert out
assert os.path.exists(outputFname)
out = pd.read_csv(outputFname)
assert out.happiness.values.max() > 0
def test_img2pose():
# Test that both face detection and facepose estimation work
detector = Detector(face_model="img2pose", facepose_model="img2pose")
# Face detection
faces = detector.detect_faces(img01)[0]
bbox_x = faces[0][0]
assert bbox_x is not None
assert len(faces[0]) == 5
assert 180 < bbox_x < 200
# Pose estimation
poses = detector.detect_facepose(img01)[0]
pose_to_test = poses[0][0] # first image and first face
pitch, roll, yaw = pose_to_test.reshape(-1)
assert -10 < pitch < 10
assert -5 < roll < 5
assert -10 < yaw < 10
def test_multiface():
# Test multiple faces
inputFname2 = os.path.join(get_test_data_path(),
"tim-mossholder-hOF1bWoet_Q-unsplash.jpg")
img01 = read_pictures([inputFname])
_, h, w, _ = img01.shape
img02 = cv2.imread(inputFname2)
# @tiankang: seems to be a problem with fer
detector = Detector(
face_model="RetinaFace",
emotion_model="fer",
landmark_model="PFLD",
au_model="jaanet",
)
files, _ = detector.process_frame(img02, 0)
assert files.shape[0] == 5
def test_mtcnn():
detector03 = Detector(
face_model="MTCNN",
landmark_model=None,
au_model=None,
emotion_model=None,
n_jobs=1,
)
out = detector03.detect_faces(img01)
bbox_left = out[0][0]
assert bbox_left != None
bbox_right = out[0][1]
bbox_top = out[0][2]
bbox_bottom = out[0][3]
assert len(out[0]) == 5
assert (bbox_left > 0 and bbox_right > 0 and bbox_top > 0
and bbox_bottom > 0 and bbox_left < bbox_right
and bbox_top < bbox_bottom and bbox_left < w and bbox_right < w
and bbox_top < h and bbox_bottom < h)
def test_faceboxes():
# Face Detector Test Case:
detector01 = Detector(
face_model="FaceBoxes",
landmark_model=None,
au_model=None,
emotion_model=None,
n_jobs=1,
)
out = detector01.detect_faces(img01)
bbox_left = out[0][0]
assert bbox_left != None
bbox_right = out[0][1]
bbox_top = out[0][2]
bbox_bottom = out[0][3]
assert len(out[0]) == 5
assert (bbox_left > 0 and bbox_right > 0 and bbox_top > 0
and bbox_bottom > 0 and bbox_left < bbox_right
and bbox_top < bbox_bottom and bbox_left < w and bbox_right < w
and bbox_top < h and bbox_bottom < h)
def test_detector():
detector = Detector(n_jobs=1)
assert detector['n_jobs'] == 1
assert type(detector) == Detector
# Test detect image
inputFname = os.path.join(get_test_data_path(), "input.jpg")
out = detector.detect_image(inputFname=inputFname)
assert type(out) == Fex
assert len(out) == 1
assert out.happiness.values[0] > 0
outputFname = os.path.join(get_test_data_path(), "output.csv")
out = detector.detect_image(inputFname=inputFname, outputFname=outputFname)
assert out
assert os.path.exists(outputFname)
out = pd.read_csv(outputFname)
assert out.happiness.values[0] > 0
# Test detect video
inputFname = os.path.join(get_test_data_path(), "input.mp4")
out = detector.detect_video(inputFname=inputFname)
assert len(out) == 72
outputFname = os.path.join(get_test_data_path(), "output.csv")
out = detector.detect_video(inputFname=inputFname, outputFname=outputFname)
assert out
assert os.path.exists(outputFname)
out = pd.read_csv(outputFname)
assert out.happiness.values.max() > 0
def test_rf():
# AU Detection Case:
detector1 = Detector(
face_model="RetinaFace",
emotion_model=None,
landmark_model="MobileFaceNet",
au_model="RF",
)
bboxes = detector1.detect_faces(img01)
lands = detector1.detect_landmarks(img01, bboxes)
convex_hull, new_lands = detector1.extract_face(
frame=img01,
detected_faces=[bboxes[0:4]],
landmarks=lands,
size_output=112)
hogs = detector1.extract_hog(frame=convex_hull, visualize=False)
aus = detector1.detect_aus(frame=hogs, landmarks=new_lands)
assert np.sum(np.isnan(aus)) == 0
assert aus.shape[-1] == 20
def test_svm():
# AU Detection Case:
detector1 = Detector(
face_model="RetinaFace",
emotion_model=None,
landmark_model="MobileFaceNet",
au_model="svm",
)
detected_faces = detector1.detect_faces(img01)
landmarks = detector1.detect_landmarks(img01, detected_faces)
hogs, new_lands = detector1._batch_hog(frames=img01,
detected_faces=detected_faces,
landmarks=landmarks)
aus = detector1.detect_aus(frame=hogs, landmarks=new_lands)
assert np.sum(np.isnan(aus)) == 0
assert aus.shape[-1] == 20
def test_img2pose_mismatch():
# Check that `detector` properly handles case where user selects img2pose as face pose estimator
# but selects a different face detector. Detector should tell user they must use img2pose as both face detector and
# pose estimator, and force face model to be `img2pose`.
detector = Detector(face_model="RetinaFace", facepose_model="img2pose-c")
assert detector.info["face_model"] == "img2pose-c"
def test_emotionsvm():
inputFname = os.path.join(get_test_data_path(), "input.jpg")
detector1 = Detector(emotion_model="svm")
out = detector1.detect_image(inputFname)
assert out.emotions()["happiness"].values > 0.5
def test_detector():
detector = Detector(n_jobs=1)
assert detector["n_jobs"] == 1
assert type(detector) == Detector
def test_detect_video():
# Test detect video
detector = Detector(n_jobs=1)
inputFname = os.path.join(get_test_data_path(), "input.mp4")
out = detector.detect_video(inputFname=inputFname, skip_frames=60)
assert len(out) == 2
def test_emotionrf():
# Emotion RF models is not good
inputFname = os.path.join(get_test_data_path(), "input.jpg")
detector1 = Detector(emotion_model="rf")
out = detector1.detect_image(inputFname)
assert out.emotions()["happiness"].values > 0.0
def test_wrongmodelname():
with pytest.raises(KeyError):
detector1 = Detector(emotion_model="badmodelname")
def test_resmasknet():
inputFname = os.path.join(get_test_data_path(), "sampler0000.jpg")
detector1 = Detector(emotion_model="resmasknet")
out = detector1.detect_image(inputFname)
assert out.emotions()["neutral"].values > 0.5
def test_nofile():
with pytest.raises(FileNotFoundError):
inputFname = os.path.join(get_test_data_path(), "nosuchfile.jpg")
detector1 = Detector(emotion_model="svm")
out = detector1.detect_image(inputFname)