Hello, I want to use my own network for face detection, so I tried to pass face_detector=None to FaceAlignment class, but it gives me an error.

Is there any functionality to pass cropped faces or its bounding boxes to the get_landmarks method?

hi, when I run 'sudo python setup.py install', it can't work. the error is: error in face_alignment setup command: 'install_requires' must be a string or list of strings containing valid project/version requirement specifiers

Could you help me? Thanks a lot.

When I'm trying the example, I got something below, I suspect it cannot download the model, could you add a link of model?

[email protected]:~/workspace/02_work/52-face-aligment/examples$ python detect_landmarks_in_image.py
Traceback (most recent call last):
  File "detect_landmarks_in_image.py", line 8, in <module>
    fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._3D, enable_cuda=False, flip_input=False)
  File "build/bdist.linux-x86_64/egg/face_alignment/api.py", line 106, in __init__
  File "/home/hw/anaconda2/lib/python2.7/site-packages/torch/serialization.py", line 267, in load
    return _load(f, map_location, pickle_module)
  File "/home/hw/anaconda2/lib/python2.7/site-packages/torch/serialization.py", line 410, in _load
    magic_number = pickle_module.load(f)
cPickle.UnpicklingError: invalid load key, '<'.

The current code outputs grid coordinates as detection results without detection confidence. Therefore, the model often generates confusing detections for some edge-case images. It is easy to get the face detection confidence, while it is hard to get the alignment confidence. I go through the code but it is not an easy job for new comers. Is there any approach?

Hello,

great work. I want to use the face-alignment for a study on facial expressions and metacognition at the BCCN Berlin. Unfortunately I encounter the following error AttributeError: 's3fd' object has no attribute 'to' , runing the sfd_detector script (line 45) I have a neuroscience/psychology background, so any help appreciated. Thanks a lot. Carina

I am getting an error in landmarks detection with a vertical video frame. This is the image

This is the error:

/usr/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject return f(*args, **kwds) /home/aditya/Python_code_learning/dev/kython_env/lib/python3.7/site-packages/face_alignment/utils.py:79: RuntimeWarning: divide by zero encountered in double_scalars t[0, 0] = resolution / h /home/aditya/Python_code_learning/dev/kython_env/lib/python3.7/site-packages/face_alignment/utils.py:80: RuntimeWarning: divide by zero encountered in double_scalars t[1, 1] = resolution / h E

ERROR: test_predict_points (main.Tester)

Traceback (most recent call last): File "facealignment_test.py", line 33, in test_predict_points landmarks = fa.get_landmarks_from_image(image) File "/home/aditya/Python_code_learning/dev/kython_env/lib/python3.7/site-packages/torch/autograd/grad_mode.py", line 26, in decorate_context return func(*args, **kwargs) File "/home/aditya/Python_code_learning/dev/kython_env/lib/python3.7/site-packages/face_alignment/api.py", line 153, in get_landmarks_from_image inp = crop(image, center, scale) File "/home/aditya/Python_code_learning/dev/kython_env/lib/python3.7/site-packages/face_alignment/utils.py", line 128, in crop interpolation=cv2.INTER_LINEAR) cv2.error: OpenCV(4.4.0) /tmp/pip-build-qct9o6da/opencv-python/opencv/modules/imgproc/src/resize.cpp:3929: error: (-215:Assertion failed) !ssize.empty() in function 'resize'

create_target_heatmap() is useful for people who want to fine-tune or train the model from scratch. Figuring it out was not trivial, so I thought it will save people time. It addresses #128

Downloading the face detection CNN. Please wait... Traceback (most recent call last): File "/opt/conda/lib/python3.5/urllib/request.py", line 1254, in do_open h.request(req.get_method(), req.selector, req.data, headers) File "/opt/conda/lib/python3.5/http/client.py", line 1106, in request self._send_request(method, url, body, headers) File "/opt/conda/lib/python3.5/http/client.py", line 1151, in _send_request self.endheaders(body) File "/opt/conda/lib/python3.5/http/client.py", line 1102, in endheaders self._send_output(message_body) File "/opt/conda/lib/python3.5/http/client.py", line 934, in _send_output self.send(msg) File "/opt/conda/lib/python3.5/http/client.py", line 877, in send self.connect() File "/opt/conda/lib/python3.5/http/client.py", line 1260, in connect server_hostname=server_hostname) File "/opt/conda/lib/python3.5/ssl.py", line 377, in wrap_socket _context=self) File "/opt/conda/lib/python3.5/ssl.py", line 752, in init self.do_handshake() File "/opt/conda/lib/python3.5/ssl.py", line 988, in do_handshake self._sslobj.do_handshake() File "/opt/conda/lib/python3.5/ssl.py", line 633, in do_handshake self._sslobj.do_handshake() ssl.SSLEOFError: EOF occurred in violation of protocol (_ssl.c:645)

During handling of the above exception, another exception occurred:

Traceback (most recent call last): File "", line 1, in File "/workspace/face-alignment/face_alignment/api.py", line 81, in init os.path.join(path_to_detector)) File "/opt/conda/lib/python3.5/urllib/request.py", line 188, in urlretrieve with contextlib.closing(urlopen(url, data)) as fp: File "/opt/conda/lib/python3.5/urllib/request.py", line 163, in urlopen return opener.open(url, data, timeout) File "/opt/conda/lib/python3.5/urllib/request.py", line 466, in open response = self._open(req, data) File "/opt/conda/lib/python3.5/urllib/request.py", line 484, in _open '_open', req) File "/opt/conda/lib/python3.5/urllib/request.py", line 444, in _call_chain result = func(*args) File "/opt/conda/lib/python3.5/urllib/request.py", line 1297, in https_open context=self._context, check_hostname=self._check_hostname) File "/opt/conda/lib/python3.5/urllib/request.py", line 1256, in do_open raise URLError(err) urllib.error.URLError: <urlopen error EOF occurred in violation of protocol (_ssl.c:645)>

Hi It seems that the results of the update (version 1.3.1) are noticeably worse than the last one (version 1.2). I made some benchmark and although it does not seem much in terms of NME, it is there and even more noticeable when you look at the images

(look at the eyes and temple landmarks) version 1.2 version 1.3 I looked at a bunch of results on the FFHQ dataset, and noticed consistently worse precision.

I could not track down what causes this difference, my suspicion is currently on the new batch inference code but could not pinpoint it yet

Hi @1adrianb .

I was bench marking your latest Pytorch source code for both 2D and 3D landmark detection with SFD face detector, I'm observing about 10x faster speed in CPU w.r.t to GPU, which is strange. Any help here would be appreciated.

CPU - Intel i9, 9th Generation Machine. GPU - GTX GeForce 1070 8GiB.

Thanks and Regards, Vinayak

Dear Adrian,

First I have to admit that this is a great work! I can use your provided face alignment tool to extract the face shape coordinates in difficult condition. I wonder how can I output the bounding box (rectangle) of the face of an input image? For now, by reading your user guide, I can only extract the shape coordinates. In my understanding, it should be a two-step process, first find the bounding box of a face, and then find the face shape coordinates inside this bounding box. So my question is how can I get the bounding box?

My code is as follows:

  imgs = imgs.permute(0, 3, 1, 2)# B x C x H x W 
  landmark = self.face_algm.get_landmarks_from_batch(imgs)

The picture I used is the frame intercepted by the MEAD dataset, but it returned an empty list to me, what did I do wrong?

In "Testing on a batch":

fig = plt.figure(figsize=(10, 5))
for i, pred in enumerate(preds):
    plt.subplot(1, 2, i + 1)
    plt.imshow(frames[1])
    plt.title(f'frame[{i}]')
    for detection in pred:
        plt.scatter(detection[:,0], detection[:,1], 2)

the 2nd loop is redundant, need to plot pred[:, 0], pred[:, 1] only.

change

fig = plt.figure(figsize=(10, 5))
for i, pred in enumerate(preds):
    plt.subplot(1, 2, i + 1)
    plt.imshow(frames[1])
    plt.title(f'frame[{i}]')
    for detection in pred:
        plt.scatter(detection[:,0], detection[:,1], 2)

to

fig = plt.figure(figsize=(10, 5))
for i, detection in enumerate(preds):
    plt.subplot(1, 2, i + 1)
    plt.imshow(frames[1])
    plt.title(f'frame[{i}]')
    plt.scatter(detection[:,0], detection[:,1], 2)

hey, thanks for your share. when using the method 'get_landmarks_from_image', I input a tensor whose size is (1, 3, 128, 128) to image_or_path. then it will call method 'get_image' from face_alignment.utils. first, it transforms tensor into numpy, then steps into 'elif image.ndim == 4: \n\t image = image[..., :3]. this step changes the size of numpy from (1, 3, 128, 128) into (1, 3, 128, 3). actually, final size will not be used correctly in the method 'detect_from_image' in face_alignment.detection.sfd.sfd_detector.SFDDecetector. if it's a bug here, or it because of my wrong usage. hope for your reply!

Hello, thanks for the share, I got lots of help from the program, but I need to change the result of point position, so I need to train a new model to fit it, could I got the information fo backbone, thanks so much.

Hey!

Are there anyways to find the confidence scores of the landmark predictions? I see there's a parameter "return_landmark_score" in the "get_landmarks" method but I do not know what the units for that value are . The scores are an array.