Py-FEAT: Python Facial Expression Analysis Toolbox

Computational Social Affective Neuroscience Laboratory

Last update: Jan 6, 2023

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Overview

Py-FEAT: Python Facial Expression Analysis Toolbox

Py-FEAT is a suite for facial expressions (FEX) research written in Python. This package includes tools to detect faces, extract emotional facial expressions (e.g., happiness, sadness, anger), facial muscle movements (e.g., action units), and facial landmarks, from videos and images of faces, as well as methods to preprocess, analyze, and visualize FEX data.

For detailed examples, tutorials, and API please refer to the Py-FEAT website.

Installation

Option 1: Easy installation for quick use Clone the repository
pip install py-feat

Option 2: Installation in development mode

git clone https://github.com/cosanlab/feat.git
cd feat && python setup.py install -e .

Usage examples

1. Detect FEX data from images or videos

FEAT is intended for use in Jupyter Notebook or Jupyter Lab environment. In a notebook cell, you can run the following to detect faces, facial landmarks, action units, and emotional expressions from images or videos. On the first execution, it will automatically download the default model files. You can also change the detection models from the list of supported models.

from feat.detector import Detector
detector = Detector() 
# Detect FEX from video
out = detector.detect_video("input.mp4")
# Detect FEX from image
out = detector.detect_image("input.png")

2. Visualize FEX data

Visualize FEX detection results.

from feat.detector import Detector
detector = Detector() 
out = detector.detect_image("input.png")
out.plot_detections()

3. Preprocessing & analyzing FEX data

We provide a number of preprocessing and analysis functionalities including baselining, feature extraction such as timeseries descriptors and wavelet decompositions, predictions, regressions, and intersubject correlations. See examples in our tutorial.

Supported Models

Please respect the usage licenses for each model.

Face detection models

Facial landmark detection models

Action Unit detection models

FEAT-Random Forest
FEAT-SVM
FEAT-Logistic
DRML: Deep Region and Multi-Label Learning
JAANet: Joint AU Detection and Face Alignment via Adaptive Attention

Emotion detection models

FEAT-Random Forest
FEAT-Logistic
FerNet
ResMaskNet: Residual Masking Network

Contributing

Fork the repository on GitHub.
Run the tests with pytest tests/ to make confirm that all tests pass on your system. If some tests fail, try to find out why they are failing. Common issues may be not having downloaded model files or missing dependencies.
Create your feature AND add tests to make sure they are working.
Run the tests again with pytest tests/ to make sure everything still passes, including your new feature. If you broke something, edit your feature so that it doesn't break existing code.
Create a pull request to the main repository's master branch.

Licenses

Py-FEAT is provided under the MIT license. You also need to respect the licenses of each model you are using. Please see the LICENSE file for links to each model's license information.

Comments

A few models cannot be found
Hi,

I was able to install py-feat with no issues. When I tried to run "Detector", it seemed that a few models were downloaded and others were not, which seem to be missing.

These files show up in the folder "/py-feat/feat/resources/"

hog_pca_all_emotio.joblib

hog_scalar_aus.joblib

mobilefacenet_model_best.pth.tar

mobilenet0.25_Final.pth

mobilenet_224_model_best_gdconv_external.pth.tar

model_list.json

Could you please let me know if I am doing something wrong?

Thanks!

detector = Detector(verbose=True) Downloading https://objects.githubusercontent.com/github-production-release-asset-2e65be/118517740/675aae00-6f8d-11eb-991e-c7886284a630?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20221123%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20221123T070446Z&X-Amz-Expires=300&X-Amz-Signature=74b29bce2b4913901b3db5574d330b63ec44e1348fd84a85910bb93b40450cc0&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=118517740&response-content-disposition=attachment%3B%20filename%3Dmobilenet0.25_Final.pth&response-content-type=application%2Foctet-stream to /home/temp/miniconda3/envs/env100/lib/python3.9/site-packages/feat/resources/mobilenet0.25_Final.pth 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1786259/1786259 [00:00<00:00, 3251717.53it/s] Downloading https://objects.githubusercontent.com/github-production-release-asset-2e65be/118517740/046a1680-6f8f-11eb-997d-d1266747f4bf?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20221123%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20221123T070447Z&X-Amz-Expires=300&X-Amz-Signature=7e7a241f1533a768a774e192e216ea18fecfe6bab10c32b6b8fa436b747d2b82&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=118517740&response-content-disposition=attachment%3B%20filename%3Dmobilenet_224_model_best_gdconv_external.pth.tar&response-content-type=application%2Foctet-stream to /home/temp/miniconda3/envs/env100/lib/python3.9/site-packages/feat/resources/mobilenet_224_model_best_gdconv_external.pth.tar 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 45256601/45256601 [00:06<00:00, 7100594.44it/s] Downloading https://objects.githubusercontent.com/github-production-release-asset-2e65be/118517740/ee9cd200-8b88-11eb-9992-cc9383e9a7eb?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20221123%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20221123T070455Z&X-Amz-Expires=300&X-Amz-Signature=8cad35bc21ac17cc6f9939cee2824e1a37c3ac37c2e8a0e445c714ced9f396b2&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=118517740&response-content-disposition=attachment%3B%20filename%3Dhog_pca_all_emotio.joblib&response-content-type=application%2Foctet-stream to /home/temp/miniconda3/envs/env100/lib/python3.9/site-packages/feat/resources/hog_pca_all_emotio.joblib 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 51773375/51773375 [00:01<00:00, 29761468.32it/s] Using downloaded and verified file: /home/temp/miniconda3/envs/env100/lib/python3.9/site-packages/feat/resources/hog_pca_all_emotio.joblib Downloading https://objects.githubusercontent.com/github-production-release-asset-2e65be/118517740/ecfc2580-8da5-11eb-9d71-275376e20c4c?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20221123%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20221123T070458Z&X-Amz-Expires=300&X-Amz-Signature=8c3ed03733a6bff706e6b0e1f89fa815f23f42674a37f338fd61e38a627474ac&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=118517740&response-content-disposition=attachment%3B%20filename%3Dhog_scalar_aus.joblib&response-content-type=application%2Foctet-stream to /home/temp/miniconda3/envs/env100/lib/python3.9/site-packages/feat/resources/hog_scalar_aus.joblib 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 130390/130390 [00:00<00:00, 109379059.71it/s] Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/temp/miniconda3/envs/env100/lib/python3.9/site-packages/feat/detector.py", line 108, in __init__ face, landmark, au, emotion, facepose = get_pretrained_models( File "/home/temp/miniconda3/envs/env100/lib/python3.9/site-packages/feat/pretrained.py", line 239, in get_pretrained_models download_url(url, get_resource_path(), verbose=verbose) File "/home/temp/miniconda3/envs/env100/lib/python3.9/site-packages/feat/utils.py", line 1063, in download_url return tv_download_url(*args, **kwargs) File "/home/temp/miniconda3/envs/env100/lib/python3.9/site-packages/torchvision/datasets/utils.py", line 147, in download_url url = _get_redirect_url(url, max_hops=max_redirect_hops) File "/home/temp/miniconda3/envs/env100/lib/python3.9/site-packages/torchvision/datasets/utils.py", line 95, in _get_redirect_url with urllib.request.urlopen(urllib.request.Request(url, headers=headers)) as response: File "/home/temp/miniconda3/envs/env100/lib/python3.9/urllib/request.py", line 214, in urlopen return opener.open(url, data, timeout) File "/home/temp/miniconda3/envs/env100/lib/python3.9/urllib/request.py", line 523, in open response = meth(req, response) File "/home/temp/miniconda3/envs/env100/lib/python3.9/urllib/request.py", line 632, in http_response response = self.parent.error( File "/home/temp/miniconda3/envs/env100/lib/python3.9/urllib/request.py", line 555, in error result = self._call_chain(*args) File "/home/temp/miniconda3/envs/env100/lib/python3.9/urllib/request.py", line 494, in _call_chain result = func(*args) File "/home/temp/miniconda3/envs/env100/lib/python3.9/urllib/request.py", line 747, in http_error_302 return self.parent.open(new, timeout=req.timeout) File "/home/temp/miniconda3/envs/env100/lib/python3.9/urllib/request.py", line 523, in open response = meth(req, response) File "/home/temp/miniconda3/envs/env100/lib/python3.9/urllib/request.py", line 632, in http_response response = self.parent.error( File "/home/temp/miniconda3/envs/env100/lib/python3.9/urllib/request.py", line 561, in error return self._call_chain(*args) File "/home/temp/miniconda3/envs/env100/lib/python3.9/urllib/request.py", line 494, in _call_chain result = func(*args) File "/home/temp/miniconda3/envs/env100/lib/python3.9/urllib/request.py", line 641, in http_error_default raise HTTPError(req.full_url, code, msg, hdrs, fp) urllib.error.HTTPError: HTTP Error 404: Not Found
opened by ethanfa 11
GPU not really utilized well

So I've run a few tests, as I've noticed that PyFeat is quite slow in comparison to something like OpenFace2. Turns out, while the CPU is always utilized, the GPU is not. This seems to be, because the data is loaded 1-by-1 through OpenCV instead of using a proper GPU library for lists of images. I think it makes sense, if a list of images is given to a Detector for detect_image, to load and predict the images batch wise through torch.DataLoaders.
enhancement

opened by ichitaka 8
Sessions Attribute Functionality Discussion.

Working on sessions attribute for Fex objects. Right now users can pass in a sessions array that can be iterated over using Fex.itersessions(), exactly the same way that pd.DataFrame.iterrows() works.

Just wanted to run a few things by @jcheong0428 and @Nathaniel-Haines. If Fex.sesssions is not None, should we iterate over every unique session for all preprocessing/descriptive/feature extraction methods? For example, Fex.downsample() should probably downsample separately for each unique session (e.g., trial, subject) right? And Fex.clean() should do the same as well as Fex.extract_boft()

opened by ljchang 8

added Fextractor class for extracting features from a Fex instance

The Fextractor class works like this:

# cleaned data
df = read_facet('iMotions_Test.txt')
sessions = np.array([[x]*10 for x in range(1+int(len(df)/10))]).flatten()[:-1]
dat = Fex(df, sampling_freq=30, sessions=sessions)
dat = dat.interpolate(method='linear')

# Create instance of class
extractor = Fextractor()

# Extraction methods build a list of different features within extractor.extracted_features
extractor.mean(fex_object=dat)
extractor.max(fex_object=dat)
extractor.min(fex_object=dat)
#extractor.boft(fex_object=dat, min_freq=.01, max_freq=.20, bank=1) # boft not working yet
extractor.multi_wavelet(fex_object=dat)    
extractor.wavelet(fex_object=dat, freq=f, num_cyc=num_cyc)

# Merge and return all extracted features as a single wide or long DataFrame
newdat = extractor.merge(out_format='long')

Currently, it returns a pandas DataFrame. Also the boft extraction does not work (but I am not sure if that was already the case?).

Anyway, this is a first attempt at a feature extraction class. Let me know what you think!

opened by Nathaniel-Haines 7

calculation of sampling freq

our FACET, OpenFace can calculate sampling frequency by doing a .diff() on the timestamps. It would be nice to have that set as the default when sampling_freq is not passed when the class is not initialized.
enhancement low priority

opened by jcheong0428 7
How to use au_model="rf" and emotion_model = "rf" in version 0.4

Hi,

I wish to use au_model="rf" since RF model gives the intensity as random continuous variable unlike SVM which just gives the possiblity of detecting aus for my analysis.

It was possible in version 0.3.7 but is not in latest version. What's the alternative for this? Also, my requirement is to use HOG based models only for au and emotions. So I can only use either RF or SVM. Is there any alternative for it?

opened by ritika24-s 6
added mean, min, and max feature extraction methods

Let me know what you all think of this method of feature extraction. The output format is the same as the boft extractor (1 row, and a column for each feature), and specifying the 'by' argument allows users to group observations by other features in the data before summarizing (e.g. by subjects, trials, or whatever). By default, the functions will summarize data across all rows.

This is all default pandas functionality too, so it is quick and easy.

opened by Nathaniel-Haines 6
Modify Interpolate method
At some point we should write a new interpolate method. We can base it off of the upsample function.

It should:

be able to accomodate nonlinear methods such as cubic and spline. Current one only seems to work with linear.

we should add a 'limit' flag which means it won't try to interpolate if chunk of time is too large.

enhancement low priority
opened by ljchang 5

RuntimeError: The size of tensor a (2) must match the size of tensor b (3) at non-singleton dimension 0

Hello, I encountered the following error when attempting to execute the example from notebook 3. It's the same code on a newly-initialized environment, so I couldn't figure out what may cause this problem.

Traceback (most recent call last):
  File "C:\research\feat\feat\detector.py", line 787, in process_frame
    detected_faces = self.detect_faces(frame=frames)
  File "C:\research\feat\feat\detector.py", line 325, in detect_faces
    faces, poses = self.face_detector(frame)
  File "C:\research\feat\feat\facepose_detectors\img2pose\img2pose_test.py", line 99, in __call__
    preds = self.scale_and_predict(img)
  File "C:\research\feat\feat\facepose_detectors\img2pose\img2pose_test.py", line 132, in scale_and_predict
    preds = self.predict(img, border_size, scale)
  File "C:\research\feat\feat\facepose_detectors\img2pose\img2pose_test.py", line 169, in predict
    pred = self.model.predict([self.transform(img)])[0]
  File "C:\research\feat\feat\facepose_detectors\img2pose\img2pose_model.py", line 107, in predict
    predictions = self.run_model(imgs)
  File "C:\research\feat\feat\facepose_detectors\img2pose\img2pose_model.py", line 95, in run_model
    outputs = self.fpn_model(imgs, targets)
  File "C:\Users\owner\anaconda3\envs\py38\lib\site-packages\torch\nn\modules\module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "C:\Users\owner\anaconda3\envs\py38\lib\site-packages\torch\nn\parallel\data_parallel.py", line 168, in forward
    outputs = self.parallel_apply(replicas, inputs, kwargs)
  File "C:\Users\owner\anaconda3\envs\py38\lib\site-packages\torch\nn\parallel\data_parallel.py", line 178, in parallel_apply
    return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)])
  File "C:\Users\owner\anaconda3\envs\py38\lib\site-packages\torch\nn\parallel\parallel_apply.py", line 86, in parallel_apply
    output.reraise()
  File "C:\Users\owner\anaconda3\envs\py38\lib\site-packages\torch\_utils.py", line 457, in reraise
    raise exception
RuntimeError: Caught RuntimeError in replica 0 on device 0.
Original Traceback (most recent call last):
  File "C:\Users\owner\anaconda3\envs\py38\lib\site-packages\torch\nn\parallel\parallel_apply.py", line 61, in _worker
    output = module(*input, **kwargs)
  File "C:\Users\owner\anaconda3\envs\py38\lib\site-packages\torch\nn\modules\module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "C:\research\feat\feat\facepose_detectors\img2pose\deps\generalized_rcnn.py", line 59, in forward
    images, targets = self.transform(images, targets)
  File "C:\Users\owner\anaconda3\envs\py38\lib\site-packages\torch\nn\modules\module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "C:\Users\owner\anaconda3\envs\py38\lib\site-packages\torchvision\models\detection\transform.py", line 127, in forward
    image = self.normalize(image)
  File "C:\Users\owner\anaconda3\envs\py38\lib\site-packages\torchvision\models\detection\transform.py", line 152, in normalize
    return (image - mean[:, None, None]) / std[:, None, None]
RuntimeError: The size of tensor a (2) must match the size of tensor b (3) at non-singleton dimension 0

exception occurred in the batch
Since singleframe4error=FALSE, giving up this entire batch result
Traceback (most recent call last):
  File "C:\research\feat\feat\detector.py", line 787, in process_frame
    detected_faces = self.detect_faces(frame=frames)
  File "C:\research\feat\feat\detector.py", line 325, in detect_faces
    faces, poses = self.face_detector(frame)
  File "C:\research\feat\feat\facepose_detectors\img2pose\img2pose_test.py", line 99, in __call__
    preds = self.scale_and_predict(img)
  File "C:\research\feat\feat\facepose_detectors\img2pose\img2pose_test.py", line 132, in scale_and_predict
    preds = self.predict(img, border_size, scale)
  File "C:\research\feat\feat\facepose_detectors\img2pose\img2pose_test.py", line 169, in predict
    pred = self.model.predict([self.transform(img)])[0]
  File "C:\research\feat\feat\facepose_detectors\img2pose\img2pose_model.py", line 107, in predict
    predictions = self.run_model(imgs)
  File "C:\research\feat\feat\facepose_detectors\img2pose\img2pose_model.py", line 95, in run_model
    outputs = self.fpn_model(imgs, targets)
  File "C:\Users\owner\anaconda3\envs\py38\lib\site-packages\torch\nn\modules\module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "C:\Users\owner\anaconda3\envs\py38\lib\site-packages\torch\nn\parallel\data_parallel.py", line 168, in forward
    outputs = self.parallel_apply(replicas, inputs, kwargs)
  File "C:\Users\owner\anaconda3\envs\py38\lib\site-packages\torch\nn\parallel\data_parallel.py", line 178, in parallel_apply
    return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)])
  File "C:\Users\owner\anaconda3\envs\py38\lib\site-packages\torch\nn\parallel\parallel_apply.py", line 86, in parallel_apply
    output.reraise()
  File "C:\Users\owner\anaconda3\envs\py38\lib\site-packages\torch\_utils.py", line 457, in reraise
    raise exception
RuntimeError: Caught RuntimeError in replica 0 on device 0.
Original Traceback (most recent call last):
  File "C:\Users\owner\anaconda3\envs\py38\lib\site-packages\torch\nn\parallel\parallel_apply.py", line 61, in _worker
    output = module(*input, **kwargs)
  File "C:\Users\owner\anaconda3\envs\py38\lib\site-packages\torch\nn\modules\module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "C:\research\feat\feat\facepose_detectors\img2pose\deps\generalized_rcnn.py", line 59, in forward
    images, targets = self.transform(images, targets)
  File "C:\Users\owner\anaconda3\envs\py38\lib\site-packages\torch\nn\modules\module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "C:\Users\owner\anaconda3\envs\py38\lib\site-packages\torchvision\models\detection\transform.py", line 127, in forward
    image = self.normalize(image)
  File "C:\Users\owner\anaconda3\envs\py38\lib\site-packages\torchvision\models\detection\transform.py", line 152, in normalize
    return (image - mean[:, None, None]) / std[:, None, None]
RuntimeError: The size of tensor a (2) must match the size of tensor b (3) at non-singleton dimension 0

exception occurred in the batch
Since singleframe4error=FALSE, giving up this entire batch result
Traceback (most recent call last):
  File "C:\research\feat\feat\detector.py", line 787, in process_frame
    detected_faces = self.detect_faces(frame=frames)
  File "C:\research\feat\feat\detector.py", line 325, in detect_faces
    faces, poses = self.face_detector(frame)
  File "C:\research\feat\feat\facepose_detectors\img2pose\img2pose_test.py", line 99, in __call__
    preds = self.scale_and_predict(img)
  File "C:\research\feat\feat\facepose_detectors\img2pose\img2pose_test.py", line 132, in scale_and_predict
    preds = self.predict(img, border_size, scale)
  File "C:\research\feat\feat\facepose_detectors\img2pose\img2pose_test.py", line 169, in predict
    pred = self.model.predict([self.transform(img)])[0]
  File "C:\research\feat\feat\facepose_detectors\img2pose\img2pose_model.py", line 107, in predict
    predictions = self.run_model(imgs)
  File "C:\research\feat\feat\facepose_detectors\img2pose\img2pose_model.py", line 95, in run_model
    outputs = self.fpn_model(imgs, targets)
  File "C:\Users\owner\anaconda3\envs\py38\lib\site-packages\torch\nn\modules\module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "C:\Users\owner\anaconda3\envs\py38\lib\site-packages\torch\nn\parallel\data_parallel.py", line 168, in forward
    outputs = self.parallel_apply(replicas, inputs, kwargs)
  File "C:\Users\owner\anaconda3\envs\py38\lib\site-packages\torch\nn\parallel\data_parallel.py", line 178, in parallel_apply
    return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)])
  File "C:\Users\owner\anaconda3\envs\py38\lib\site-packages\torch\nn\parallel\parallel_apply.py", line 86, in parallel_apply
    output.reraise()
  File "C:\Users\owner\anaconda3\envs\py38\lib\site-packages\torch\_utils.py", line 457, in reraise
    raise exception
RuntimeError: Caught RuntimeError in replica 0 on device 0.
Original Traceback (most recent call last):
  File "C:\Users\owner\anaconda3\envs\py38\lib\site-packages\torch\nn\parallel\parallel_apply.py", line 61, in _worker
    output = module(*input, **kwargs)
  File "C:\Users\owner\anaconda3\envs\py38\lib\site-packages\torch\nn\modules\module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "C:\research\feat\feat\facepose_detectors\img2pose\deps\generalized_rcnn.py", line 59, in forward
    images, targets = self.transform(images, targets)
  File "C:\Users\owner\anaconda3\envs\py38\lib\site-packages\torch\nn\modules\module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "C:\Users\owner\anaconda3\envs\py38\lib\site-packages\torchvision\models\detection\transform.py", line 127, in forward
    image = self.normalize(image)
  File "C:\Users\owner\anaconda3\envs\py38\lib\site-packages\torchvision\models\detection\transform.py", line 152, in normalize
    return (image - mean[:, None, None]) / std[:, None, None]
RuntimeError: The size of tensor a (2) must match the size of tensor b (3) at non-singleton dimension 0

exception occurred in the batch
Since singleframe4error=FALSE, giving up this entire batch result
Traceback (most recent call last):
  File "C:\research\feat\feat\detector.py", line 787, in process_frame
    detected_faces = self.detect_faces(frame=frames)
  File "C:\research\feat\feat\detector.py", line 325, in detect_faces
    faces, poses = self.face_detector(frame)
  File "C:\research\feat\feat\facepose_detectors\img2pose\img2pose_test.py", line 99, in __call__
    preds = self.scale_and_predict(img)
  File "C:\research\feat\feat\facepose_detectors\img2pose\img2pose_test.py", line 132, in scale_and_predict
    preds = self.predict(img, border_size, scale)
  File "C:\research\feat\feat\facepose_detectors\img2pose\img2pose_test.py", line 169, in predict
    pred = self.model.predict([self.transform(img)])[0]
  File "C:\research\feat\feat\facepose_detectors\img2pose\img2pose_model.py", line 107, in predict
    predictions = self.run_model(imgs)
  File "C:\research\feat\feat\facepose_detectors\img2pose\img2pose_model.py", line 95, in run_model
    outputs = self.fpn_model(imgs, targets)
  File "C:\Users\owner\anaconda3\envs\py38\lib\site-packages\torch\nn\modules\module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "C:\Users\owner\anaconda3\envs\py38\lib\site-packages\torch\nn\parallel\data_parallel.py", line 168, in forward
    outputs = self.parallel_apply(replicas, inputs, kwargs)
  File "C:\Users\owner\anaconda3\envs\py38\lib\site-packages\torch\nn\parallel\data_parallel.py", line 178, in parallel_apply
    return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)])
  File "C:\Users\owner\anaconda3\envs\py38\lib\site-packages\torch\nn\parallel\parallel_apply.py", line 86, in parallel_apply
    output.reraise()
  File "C:\Users\owner\anaconda3\envs\py38\lib\site-packages\torch\_utils.py", line 457, in reraise
    raise exception
RuntimeError: Caught RuntimeError in replica 0 on device 0.
Original Traceback (most recent call last):
  File "C:\Users\owner\anaconda3\envs\py38\lib\site-packages\torch\nn\parallel\parallel_apply.py", line 61, in _worker
    output = module(*input, **kwargs)
  File "C:\Users\owner\anaconda3\envs\py38\lib\site-packages\torch\nn\modules\module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "C:\research\feat\feat\facepose_detectors\img2pose\deps\generalized_rcnn.py", line 59, in forward
    images, targets = self.transform(images, targets)
  File "C:\Users\owner\anaconda3\envs\py38\lib\site-packages\torch\nn\modules\module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "C:\Users\owner\anaconda3\envs\py38\lib\site-packages\torchvision\models\detection\transform.py", line 127, in forward
    image = self.normalize(image)
  File "C:\Users\owner\anaconda3\envs\py38\lib\site-packages\torchvision\models\detection\transform.py", line 152, in normalize
    return (image - mean[:, None, None]) / std[:, None, None]
RuntimeError: The size of tensor a (2) must match the size of tensor b (3) at non-singleton dimension 0

exception occurred in the batch
Since singleframe4error=FALSE, giving up this entire batch result
Traceback (most recent call last):
  File "C:\research\feat\feat\detector.py", line 787, in process_frame
    detected_faces = self.detect_faces(frame=frames)
  File "C:\research\feat\feat\detector.py", line 325, in detect_faces
    faces, poses = self.face_detector(frame)
  File "C:\research\feat\feat\facepose_detectors\img2pose\img2pose_test.py", line 99, in __call__
    preds = self.scale_and_predict(img)
  File "C:\research\feat\feat\facepose_detectors\img2pose\img2pose_test.py", line 132, in scale_and_predict
    preds = self.predict(img, border_size, scale)
  File "C:\research\feat\feat\facepose_detectors\img2pose\img2pose_test.py", line 169, in predict
    pred = self.model.predict([self.transform(img)])[0]
  File "C:\research\feat\feat\facepose_detectors\img2pose\img2pose_model.py", line 107, in predict
    predictions = self.run_model(imgs)
  File "C:\research\feat\feat\facepose_detectors\img2pose\img2pose_model.py", line 95, in run_model
    outputs = self.fpn_model(imgs, targets)
  File "C:\Users\owner\anaconda3\envs\py38\lib\site-packages\torch\nn\modules\module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "C:\Users\owner\anaconda3\envs\py38\lib\site-packages\torch\nn\parallel\data_parallel.py", line 168, in forward
    outputs = self.parallel_apply(replicas, inputs, kwargs)
  File "C:\Users\owner\anaconda3\envs\py38\lib\site-packages\torch\nn\parallel\data_parallel.py", line 178, in parallel_apply
    return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)])
  File "C:\Users\owner\anaconda3\envs\py38\lib\site-packages\torch\nn\parallel\parallel_apply.py", line 86, in parallel_apply
    output.reraise()
  File "C:\Users\owner\anaconda3\envs\py38\lib\site-packages\torch\_utils.py", line 457, in reraise
    raise exception
RuntimeError: Caught RuntimeError in replica 0 on device 0.
Original Traceback (most recent call last):
  File "C:\Users\owner\anaconda3\envs\py38\lib\site-packages\torch\nn\parallel\parallel_apply.py", line 61, in _worker
    output = module(*input, **kwargs)
  File "C:\Users\owner\anaconda3\envs\py38\lib\site-packages\torch\nn\modules\module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "C:\research\feat\feat\facepose_detectors\img2pose\deps\generalized_rcnn.py", line 59, in forward
    images, targets = self.transform(images, targets)
  File "C:\Users\owner\anaconda3\envs\py38\lib\site-packages\torch\nn\modules\module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "C:\Users\owner\anaconda3\envs\py38\lib\site-packages\torchvision\models\detection\transform.py", line 127, in forward
    image = self.normalize(image)
  File "C:\Users\owner\anaconda3\envs\py38\lib\site-packages\torchvision\models\detection\transform.py", line 152, in normalize
    return (image - mean[:, None, None]) / std[:, None, None]
RuntimeError: The size of tensor a (2) must match the size of tensor b (3) at non-singleton dimension 0

exception occurred in the batch
Since singleframe4error=FALSE, giving up this entire batch result

opened by TalBarami 4

Exception Occurred when using Detector

An exception occurs when using "mobilenet" as the landmark model on my machine with CUDA installed. The other landmark models don't have this issue. The exception seems to be caused by not all of the torch module's parameters and buffers are on GPU.

Traceback (most recent call last):
  File "pyfeat_test.py", line 26, in <module>
    image_prediction = detector.detect_image(test_image)
  File "venv/lib/python3.8/site-packages/feat/detector.py", line 689, in detect_image
    df = self.process_frame(frame)
  File "venv/lib/python3.8/site-packages/feat/detector.py", line 540, in process_frame
    landmarks = self.detect_landmarks(
  File "venv/lib/python3.8/site-packages/feat/detector.py", line 366, in detect_landmarks
    landmark = self.landmark_detector(input).cpu().data.numpy()
  File "venv/lib/python3.8/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
    result = self.forward(*input, **kwargs)
  File "venv/lib/python3.8/site-packages/torch/nn/parallel/data_parallel.py", line 153, in forward
    raise RuntimeError("module must have its parameters and buffers "
RuntimeError: module must have its parameters and buffers on device cuda:0 (device_ids[0]) but found one of them on device: cpu

Does anyone have an idea on how to solve this? Thanks!

investigate

opened by sulibo 4

WIP: Added sessions and updated wavelet
Added new sessions attribute and itersessions() method. for grouping by sessions for preproc and feature extraction. Still needs to be tested.

Updated wavelet and extract_wavelet() methods. Can now output complex wavelet and calculate filtered signal, phase angle, or power. Still needs to be tested.

changed sampling_rate -> sampling freq to be consistent

made duration automatically detected.

still might want to zero pad data before convolving.

@jcheong0428: Feel free to modify if you want. Once all of the two new functions seem to be tested and working, I would recommend updating your extract_botf() method to use the new functions.
opened by ljchang 4
Getting Error: List index out of rage. While using detect_video(). Please help

I am using py-feat 0.5.0 pypi version and using detect_video function to extract features from video. The program started successfully and in the middle of execution, its failing with the below error.

IndexError Traceback (most recent call last) Input In [44], in <cell line: 7>() 11 print(f"Processing: {video}") 13 # This is the line that does detection! ---> 14 fex = detector.detect_video(video) 16 fex.to_csv(out_name, index=False)

File ~.conda\envs\LIE\lib\site-packages\feat\detector.py:802, in Detector.detect_video(self, video_path, skip_frames, output_size, batch_size, num_workers, pin_memory, **detector_kwargs) 800 frames = list(batch_data["Frame"].numpy()) 801 landmarks = _inverse_landmark_transform(landmarks, batch_data) --> 802 output = self._create_fex( 803 faces, landmarks, poses, aus, emotions, batch_data["FileName"], frames 804 ) 805 batch_output.append(output) 807 batch_output = pd.concat(batch_output)

File ~.conda\envs\LIE\lib\site-packages\feat\detector.py:872, in Detector._create_fex(self, faces, landmarks, poses, aus, emotions, file_names, frame_counter) 856 for j, face_in_frame in enumerate(frame): 857 facebox_df = pd.DataFrame( 858 [ 859 [ (...) 868 index=[j], 869 ) 871 facepose_df = pd.DataFrame( --> 872 [poses[i][j].flatten(order="F")], 873 columns=self.info["facepose_model_columns"], 874 index=[j], 875 ) 877 landmarks_df = pd.DataFrame( 878 [landmarks[i][j].flatten(order="F")], 879 columns=self.info["face_landmark_columns"], 880 index=[j], 881 ) 883 aus_df = pd.DataFrame( 884 aus[i][j, :].reshape(1, len(self["au_presence_columns"])), 885 columns=self.info["au_presence_columns"], 886 index=[j], 887 )

IndexError: list index out of range

opened by Abid-S 5
Make `.detect_video` more memory efficient
@ljchang after chatting with @TiankangXie it looks like we can fairly easily roll our own read_video function because torch also provides a lower level API with their VideoReader class.

Just like in their examples, we can just write a function that wraps the next(reader) calls and return a generator so at most we load only batch_size frames at most into memory on each loop iteration. That way even long videos shouldn't be a problem on low RAM/VRAM machines, and more memory will simply allow for bigger batch sizes.

The downside trying to get it to work right now is that torch needs to be compiled with support for it and requires a working ffmeg install:

*** RuntimeError: Not compiled with video_reader support, to enable video_reader support, please install ffmpeg (version 4.2 is currently supported) and build torchvision from source. Traceback (most recent call last): File "/Users/Esh/anaconda3/envs/py-feat/lib/python3.8/site-packages/torchvision/io/__init__.py", line 130, in __init__ raise RuntimeError(

So it seems like the real cost of rolling our own solution with VideoReader until torch allows for more memory efficient read_video(), is an added dependency on ffmepg and potentially more installation hassle. Or we can try a different library or solution for loading video frames. From a brief search on github it looks like there are lots of custom solutions as third party libraries, because this isn't quite "solved." But most libraries "cheat" a bit IMO. e.g. Expecting that you've pre-saved each frame as a separate image file on disk
opened by ejolly 0
Image size degrades emotion classification accuracy when holding face pixel size constant

Using v0.4.0 or the current m1_testing branch, using the default detectors specified in the documentation, I'm encountering an issue where using large images seems to degrade performance, when holding the pixel size of the actual faces constant. Simply cropping out face-free parts of the image improves performance considerably. I suspect that this might be happening because the image is downsampled for face detection, and then when the faces are extracted using the resulting bounding boxes, the downsampled rather than original image is used. This would result in the faces being unnecessarily downsampled in large images that are mostly free of faces, leading to degraded performance. If this is the problem, I would suggest upsampling the bounding boxes back to the original image resolution, and then extracting the faces from the original. They could always be downsampled from this point if necessary for the emotion model, but at least it wouldn't be based on something arbitrary like the overall image size.
WIP

opened by markallenthornton 2
Different AU values when passing to detector.detect_image a single image or a batch of images

Hi there,

I am trying to extract AU values from some images. Calling the function "detector.detect_image" on each image individually, I obtain some values in the AU columns different from those I obtain by passing to the function a list containing all the images (the same used in the single image case).

The values on the other columns are the same (same detection coordinates and same emotion values), only the values for the AU columns are different.

Is this behavior expected?
WIP

opened by VaianiLorenzo 2

Releases(v0.5.0)

v0.5.0(Dec 14, 2022)
Notes

This is a large overhaul and refactor of some of the core testing and API functionality to make future development, maintenance, and testing easier. Notable highlights include:

tighter integration with torch data loaders

dropping opencv as a dependency

experimental support for macOS m1 GPUs

passing keyword arguments to underlying torch models for more control

Detector Changes

New

you can now pass keyword arguments directly to the underlying pytorch/sklearn models on Detector initialization using dictionaries. For example you can do: detector = Detector(facepose_model_kwargs={'keep_top_k': 500}) to initialize img2pose to only use 500 instead of 750 features

all .detect_* methods can also pass keyword arguments to the underlying pytorch/sklearn models, albeit these will be passed to their underlying __call__ methods

SVM AU model has been retrained with new HOG feature PCA pipeline

new XGBoost AU model with new HOG feature PCA pipeline

.detect_image and .detect_video now display a tqdm progressbar

new skip_failed_detections keyword argument to still generate a Fex object when processing multiple images and one or more detections fail

Breaking

the new default model for landmark detection was changed from mobilenet to mobilefacenet.

the new default model for AU detection was changed to our new xgb model which gives continuous valued predictions between 0-1

remove support for fer emotion model

remove support for jaanet AU model

remove support for pnp facepose detector

drop support for reading and manipulating Affectiva and FACET data

.detect_image will no longer resize images on load as the new default for output_size=None. If you want to process images with batch_size > 1 and images differ in size, then you will be required to manually set output_size otherwise py-feat will raise a helpful error message

Fex Changes

New

new .update_sessions() method that returns a copy of a Fex frame with the .sessions attribute updated, making it easy to chain operations

.predict() and .regress() now support passing attributes to X and or Y using string names that match the attribute names:

'emotions' use all emotion columns (i.e. fex.emotions)

'aus' use all AU columns (i.e. fex.aus)

'poses' use all pose columns (i.e. fex.poses)

'landmarks' use all landmark columns (i.e. fex.landmarks)

'faceboxes' use all facebox columns (i.e. fex.faceboxes)

You can also combine feature groups using a comma-separated string e.g. fex.regress(X='emotions,poses', y='landmarks')

.extract_* methods now include std and sem. These are also included in .extract_summary()

Breaking

All Fex attributes have been pluralized as indicated below. For the time-being old attribute access will continue to work but will show a warning. We plan to formally drop support in a few versions

.landmark -> .landmarks

.facepose -> .poses

.input -> .inputs

.landmark_x -> .landmarks_x

.landmark_y -> .landmarks_y

.facebox -> .faceboxes

Development changes

test_pretrained_models.py is now more organized using pytest classes

added tests for img2pose models

added more robust testing for the interaction between batch_size and output_size

General Fixes

data loading with multiple images of potentially different sizes should be faster and more reliable

fix bug in resmasknet that would give poor predictions when multiple faces were present and particularly small

#150

#149

#148

#147

#145

#137

#134

#132

#131

#130

#129

#127

#121

#104

Source code(tar.gz)
Source code(zip)
v0.4.0(Jun 10, 2022)

This is version-breaking release! See the full changelog here: https://py-feat.org/pages/changelog.html
Source code(tar.gz)
Source code(zip)
0.3.7(Apr 4, 2021)

Fix import error due to missing init. Deployed 0.3.7 to Pypi
Source code(tar.gz)
Source code(zip)
0.3.6(Mar 30, 2021)

Trigger Zenodo release
Source code(tar.gz)
Source code(zip)
0.2(Feb 9, 2021)

Source code(tar.gz)
Source code(zip)
DRMLNetParams.pth(217.20 MB)
toy_disfa.csv(1.43 KB)
toy_test.csv(1.71 KB)
v0.1(Dec 15, 2020)

Source code(tar.gz)
Source code(zip)
fer_aug_model.h5(27.53 MB)
lbfmodel.yaml(53.76 MB)
JAANetparams.zip(89.18 MB)
FaceBoxesProd.pth(3.88 MB)
onet.npy(2.23 MB)
pnet.npy(40.30 KB)
rnet.npy(590.47 KB)
mobilenet0.25_Final.pth(1.70 MB)
mobilefacenet_model_best.pth.tar(11.71 MB)
pfld_model_best.pth.tar(8.47 MB)
ResMaskNet_Z_resmasking_dropout1_rot30.pth(526.07 MB)
best_ferModel.pth(83.43 MB)
hog_pca_all_emotio.joblib(49.37 MB)
RF_568.joblib(359.81 MB)
emo_hog_scalar.joblib(127.33 KB)
emo_hog_pca.joblib(55.40 MB)
emoRF36.joblib(56.90 MB)
emoSVM38.joblib(82.99 KB)
hog_scalar_aus.joblib(127.33 KB)
WIDER_train_pose_mean_v1.npy(176 bytes)
WIDER_train_pose_stddev_v1.npy(176 bytes)
reference_3d_68_points_trans.npy(944 bytes)
img2pose_v1.pth(161.97 MB)
img2pose_v1_ft_300w_lp.pth(161.97 MB)
rnet.pt(393.69 KB)
pnet.pt(27.90 KB)
onet.pt(1.48 MB)
jaanet_aus_to_landmarks.h5(10.30 MB)
jaanet_aus_to_landmarks.joblib(11.72 MB)
pyfeat_aus_to_landmarks.h5(19.54 MB)
pyfeat_aus_to_landmarks.joblib(23.50 MB)
ResMaskNet_fer2013_config.json(448 bytes)
cross_dset_pca.pkl(50.32 MB)
svm_60_Nov22022.pkl(192.58 KB)
full_face_pcaSet.pkl(52.47 MB)
lower_face_pcaSet.pkl(45.04 MB)
upper_face_pcaSet.pkl(35.04 MB)
xgb_60_oct312022.pkl(11.62 MB)
Oct30FinalXGB_AU1.ubj(1.60 MB)
Oct30FinalXGB_AU2.ubj(580.95 KB)
Oct30FinalXGB_AU4.ubj(559.76 KB)
Oct30FinalXGB_AU5.ubj(561.96 KB)
Oct30FinalXGB_AU6.ubj(202.32 KB)
Oct30FinalXGB_AU7.ubj(1.09 MB)
Oct30FinalXGB_AU9.ubj(716.22 KB)
Oct30FinalXGB_AU10.ubj(322.78 KB)
Oct30FinalXGB_AU11.ubj(110.44 KB)
Oct30FinalXGB_AU12.ubj(578.89 KB)
Oct30FinalXGB_AU14.ubj(201.52 KB)
Oct30FinalXGB_AU15.ubj(704.14 KB)
Oct30FinalXGB_AU17.ubj(583.01 KB)
Oct30FinalXGB_AU20.ubj(202.26 KB)
Oct30FinalXGB_AU23.ubj(252.43 KB)
Oct30FinalXGB_AU24.ubj(252.50 KB)
Oct30FinalXGB_AU25.ubj(2.14 MB)
Oct30FinalXGB_AU26.ubj(317.07 KB)
Oct30FinalXGB_AU28.ubj(641.68 KB)
Oct30FinalXGB_AU43.ubj(84.08 KB)
emoXGB33.ubj(2.27 MB)
mobilenet_224_model_best_gdconv_external.pth.tar(43.23 MB)