2. Detecting facial expressions from images#

Written by Jin Hyun Cheong and Eshin Jolly

In this tutorial we’ll explore the Detector class in more depth, demonstrating how to detect faces, facial landmarks, action units, and emotions from images. You can try it out interactively in Google Collab:

# Uncomment the line below and run this only if you're using Google Collab
# !pip install -q py-feat

2.1 Detecting a single face from a single image#

Setting up the Detector#

When using the Detector you can either specify specific models to use or just load the default models which are defined explicitly below:

from feat import Detector

detector = Detector(
    face_model="retinaface",
    landmark_model="mobilefacenet",
    au_model='svm',
    emotion_model="resmasknet",
    facepose_model="img2pose",
)

detector

feat.detector.Detector(face_model=retinaface, landmark_model=mobilefacenet, au_model=svm, emotion_model=resmasknet, facepose_model=img2pose)

Let’s process a single image with a single face. Py-feat includes a demo image for this purpose called single_face.jpg so lets use that. You can also use the convenient imshow function which will automatically load an image into a numpy array if provided a path unlike matplotlib:

from feat.utils import get_test_data_path
from feat.plotting import imshow
import os

# Helper to point to the test data folder
test_data_dir = get_test_data_path()

# Get the full path
single_face_img_path = os.path.join(test_data_dir, "single_face.jpg")

# Plot it
imshow(single_face_img_path)

Now we use our initialized detector instance to make predictions with the detect_image() method. This is the main workhorse method that will perform face, landmark, au, and emotion detection using the loaded models. It always returns a Fex data instance:

single_face_prediction = detector.detect_image(single_face_img_path)

# Show results
single_face_prediction

/Users/Esh/anaconda3/envs/py-feat/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at  /Users/distiller/project/pytorch/aten/src/ATen/native/TensorShape.cpp:2228.)
  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]

	frame	FaceRectX	FaceRectY	FaceRectWidth	FaceRectHeight	FaceScore	x_0	x_1	x_2	x_3	...	Roll	Yaw	anger	disgust	fear	happiness	sadness	surprise	neutral	input
0	0	196.976837	140.997742	173.810486	257.639343	0.999681	191.624944	191.644596	193.813321	199.024401	...	-3.809027	6.605721	0.000369	0.000026	0.000485	0.986996	0.000046	0.01201	0.000068	/Users/Esh/Documents/pypackages/py-feat/feat/t...

1 rows × 173 columns

Working with Fex data class results#

Because the output is a Fex data class instance, we can utilize its various helper methods and attributes to inspect our predictions.

Easily accessing FEX columns of interest.#

Fex data classes make it simple to access various columns of interest (AUs, emotion, faceboxes, etc):

single_face_prediction.facebox

	FaceRectX	FaceRectY	FaceRectWidth	FaceRectHeight	FaceScore
0	196.976837	140.997742	173.810486	257.639343	0.999681

single_face_prediction.aus

	AU01	AU02	AU04	AU05	AU06	AU07	AU09	AU10	AU11	AU12	AU14	AU15	AU17	AU20	AU23	AU24	AU25	AU26	AU28	AU43
0	0	0	0	0	0	1	0	1	1	1	1	0	0	0	0	0	1	0	0	0

single_face_prediction.emotions

	anger	disgust	fear	happiness	sadness	surprise	neutral
0	0.000369	0.000026	0.000485	0.986996	0.000046	0.01201	0.000068

single_face_prediction.facepose # (in degrees)

	Pitch	Roll	Yaw
0	0.832747	-3.809027	6.605721

Saving detection to a file#

We can also save our detection directly to a file by specifying an outputFname when using .detect_image. The detector will return True when it’s finished.

detector.detect_image(single_face_img_path, outputFname = "output.csv")

	frame	FaceRectX	FaceRectY	FaceRectWidth	FaceRectHeight	FaceScore	x_0	x_1	x_2	x_3	...	Roll	Yaw	anger	disgust	fear	happiness	sadness	surprise	neutral	input
0	0	196.976837	140.997742	173.810486	257.639343	0.999681	191.624944	191.644596	193.813321	199.024401	...	-3.809027	6.605721	0.000369	0.000026	0.000485	0.986996	0.000046	0.01201	0.000068	/Users/Esh/Documents/pypackages/py-feat/feat/t...

1 rows × 173 columns

Loading detection results from a saved file#

We can load this output using the read_feat() function, which behaves just like pd.read_csv from Pandas, but returns a Fex data class instead of a DataFrame. This gives you the full suite of Fex funcionality right away.

# prefer to pandas read_csv
from feat.utils import read_feat

input_prediction = read_feat("output.csv")

# Show results
input_prediction

	frame	FaceRectX	FaceRectY	FaceRectWidth	FaceRectHeight	FaceScore	x_0	x_1	x_2	x_3	...	Roll	Yaw	anger	disgust	fear	happiness	sadness	surprise	neutral	input
0	0	196.97684	140.99774	173.81049	257.63934	0.999681	191.624944	191.644596	193.813321	199.024401	...	-3.809027	6.605721	0.000369	0.000026	0.000485	0.986996	0.000046	0.01201	0.000068	output.csv

1 rows × 173 columns

Visualizing detection results.#

We can use the .plot_detections() method to generate a summary figure of detected faces, action units and emotions. It always returns a list of matplotlib figures:

figs = single_face_prediction.plot_detections(poses=True)

By default .plot_detections() will overlay facial lines on top of the input image. However, it’s also possible to visualize a face using Py-Feat’s standardized AU landmark model, which takes the detected AUs and projects them onto a template face. You an control this by change by setting faces='aus' instead of the default faces='landmarks'. For more details about this kind of visualization see the visualizing facial expressions and the creating an AU visualization model tutorials:

figs = single_face_prediction.plot_detections(faces='aus', muscles=True)

2.2 Detecting multiple faces from a single image#

A Detector can automatically find multiple faces in a single image. We’ll see that in the next example, the number of rows of the Fex data class returned from .detect_image() has one row for each detected face. We’ll also try using a different model this time, Img2Pose, which acts as both a face detector and a face pose estimator.

Notice how image_prediction is now a Fex instance with 5 rows, one for each detected face. We can confirm this by plotting our detection results and poses like before:

multi_face_image_path = os.path.join(test_data_dir, "multi_face.jpg")
multi_face_prediction = detector.detect_image(multi_face_image_path)

# Show results
multi_face_prediction

	FaceRectX	FaceRectY	FaceRectWidth	FaceRectHeight	FaceScore	x_0	x_1	x_2	x_3	...	Roll	Yaw	anger	disgust	fear	happiness	sadness	surprise	neutral	input
0	683.844116	288.876495	103.788025	134.104675	0.999768	685.243732	683.924138	683.493398	684.032188	...	-2.973639	-4.679388	0.050378	0.002782	0.160675	0.011653	0.587488	0.075299	0.111725	/Users/Esh/Documents/pypackages/py-feat/feat/t...
1	533.678894	309.400024	96.237732	124.128448	0.999421	535.572383	533.746383	533.008999	533.146794	...	4.255018	8.171312	0.03112	0.001458	0.186241	0.104354	0.269578	0.014566	0.392683	/Users/Esh/Documents/pypackages/py-feat/feat/t...
2	316.984406	233.779205	92.016876	126.462952	0.999196	313.083172	314.54322	317.242715	321.314606	...	9.734623	6.029476	0.033716	0.114599	0.055362	0.181432	0.060542	0.110806	0.443542	/Users/Esh/Documents/pypackages/py-feat/feat/t...
3	221.29747	64.152306	85.109207	109.057442	0.996842	219.466511	217.443319	216.14972	216.210632	...	14.068047	-4.917797	0.000943	0.001107	0.036498	0.08564	0.020177	0.772348	0.083288	/Users/Esh/Documents/pypackages/py-feat/feat/t...
4	437.129089	213.861359	79.529785	97.050537	0.996773	437.59345	438.101893	439.270088	441.157225	...	1.971143	-4.030666	0.20795	0.002251	0.002176	0.416435	0.002668	0.219107	0.149411	/Users/Esh/Documents/pypackages/py-feat/feat/t...

5 rows × 173 columns

figs = multi_face_prediction.plot_detections(add_titles=False)

2.3 Detecting faces from multiple images#

Detector is also flexible enough to process multiple image files simultaneously if .detect_image() is passed a list of images. You can process multiple images in a batch to speed up processing, but all images in a batch must have the same dimensions.

In the example below we process both our single and multi-face example images from above, but force Py-Feat not to batch process them by setting batch_size = 1.

Notice how the returned Fex data class instance has 6 rows: 1 for the first face in the first image, and 5 for the faces in the second image:

img_list = [single_face_img_path, multi_face_image_path]

mixed_prediction = detector.detect_image(img_list, batch_size=1)
mixed_prediction

	frame	FaceRectX	FaceRectY	FaceRectWidth	FaceRectHeight	FaceScore	x_0	x_1	x_2	x_3	...	Roll	Yaw	anger	disgust	fear	happiness	sadness	surprise	neutral	input
0	0	196.976837	140.997742	173.810486	257.639343	0.999681	191.624944	191.644596	193.813321	199.024401	...	-3.809027	6.605721	0.000369	0.000026	0.000485	0.986996	0.000046	0.01201	0.000068	/Users/Esh/Documents/pypackages/py-feat/feat/t...
1	1	683.844116	288.876495	103.788025	134.104675	0.999768	685.243732	683.924138	683.493398	684.032188	...	-2.973639	-4.679388	0.050378	0.002782	0.160675	0.011653	0.587488	0.075299	0.111725	/Users/Esh/Documents/pypackages/py-feat/feat/t...
2	1	533.678894	309.400024	96.237732	124.128448	0.999421	535.572383	533.746383	533.008999	533.146794	...	4.255018	8.171312	0.03112	0.001458	0.186241	0.104354	0.269578	0.014566	0.392683	/Users/Esh/Documents/pypackages/py-feat/feat/t...
3	1	316.984406	233.779205	92.016876	126.462952	0.999196	313.083172	314.54322	317.242715	321.314606	...	9.734623	6.029476	0.033716	0.114599	0.055362	0.181432	0.060542	0.110806	0.443542	/Users/Esh/Documents/pypackages/py-feat/feat/t...
4	1	221.29747	64.152306	85.109207	109.057442	0.996842	219.466511	217.443319	216.14972	216.210632	...	14.068047	-4.917797	0.000943	0.001107	0.036498	0.08564	0.020177	0.772348	0.083288	/Users/Esh/Documents/pypackages/py-feat/feat/t...
5	1	437.129089	213.861359	79.529785	97.050537	0.996773	437.59345	438.101893	439.270088	441.157225	...	1.971143	-4.030666	0.20795	0.002251	0.002176	0.416435	0.002668	0.219107	0.149411	/Users/Esh/Documents/pypackages/py-feat/feat/t...

6 rows × 173 columns

Calling .plot_detections() will now plot detections for all images the detector was passed:

figs = mixed_prediction.plot_detections(add_titles=False)

However, it’s easy to use pandas slicing sytax to just grab predictions for the image you want. For example you can use .loc and chain it to .plot_detections():

# Just plot the detection corresponding to the first row in the Fex data
figs = mixed_prediction.loc[0].plot_detections(add_titles=False)

Likewise you can use .query() and chain it to .plot_detections(). Fex data classes store each file path in the 'input' column. So we can use regular pandas methods like .unique() to get all the unique images (2 in our case) and pick the second one.

# Choose plot based on image file name
img_name = mixed_prediction['input'].unique()[1]
axes = mixed_prediction.query("input == @img_name").plot_detections(add_titles=False)

Py-Feat

2. Detecting facial expressions from images

Contents

2. Detecting facial expressions from images#

2.1 Detecting a single face from a single image#

Setting up the Detector#

Working with Fex data class results#

Easily accessing FEX columns of interest.#

Saving detection to a file#

Loading detection results from a saved file#

Visualizing detection results.#

2.2 Detecting multiple faces from a single image#

2.3 Detecting faces from multiple images#