def test_plot_detections():
test_data_dir = get_test_data_path()
test_image = join(test_data_dir, "input.jpg")
detector = Detector()
image_prediction = detector.detect_image(test_image)
axes = image_prediction.plot_detections()
assert axes[1].get_xlim() == (0.0, 1.1)
plt.close()
axes = image_prediction.plot_detections(muscle=True)
assert axes[1].get_xlim() == (0.0, 1.1)
plt.close()
image_prediction2 = image_prediction.copy()
image_prediction2["input"] = "NO_SUCH_FILE_EXISTS"
axes = image_prediction2.plot_detections()
assert axes[1].get_xlim() == (0.0, 1.1)
plt.close()
import pandas as pd
import seaborn as sns
sns.set_context("talk")
clip_attrs = pd.read_csv("clip_attrs.csv")
videos = np.sort(glob.glob("*.mp4"))
print(videos)
# Process each video using our detector.
# In[ ]:
from feat import Detector
detector = Detector(au_model = "rf", emotion_model = "resmasknet")
for video in videos:
detector.detect_video(video, outputFname = video.replace(".mp4", ".csv"))
# In[9]:
from feat.utils import read_feat
import pandas as pd
for ix ,video in enumerate(videos):
outputF = video.replace(".mp4", ".csv")
if ix == 0:
fex = read_feat(outputF)
else:
# !pip install -q py-feat
# ## Detecting facial expressions from images.
#
# First, load the detector class. You can specify which models you want to use.
# In[2]:
from feat import Detector
face_model = "retinaface"
landmark_model = "mobilenet"
au_model = "rf"
emotion_model = "resmasknet"
detector = Detector(face_model=face_model,
landmark_model=landmark_model,
au_model=au_model,
emotion_model=emotion_model)
# Find the file you want to process. In our case, we'll use our test image `input.jpg`.
# In[3]:
# Find the file you want to process.
from feat.tests.utils import get_test_data_path
import os
test_data_dir = get_test_data_path()
test_image = os.path.join(test_data_dir, "input.jpg")
# Here is what our test image looks like.
# In[4]:
fd, hog_image = hog(resized_face_np,
orientations=8,
pixels_per_cell=(8, 8),
cells_per_block=(2, 2),
visualize=True,
multichannel=True)
return fd, hog_image, points
# Replace the paths so that it points to your local dataset directory.
# In[ ]:
detector = Detector(face_model="retinaface", landmark_model="mobilenet")
# Correct path to your downloaded dataset.
EmotioNet_images = np.sort(glob.glob("/Storage/Data/EmotioNet/imgs/*.jpg"))
labels = pd.read_csv(
"/Storage/Data/EmotioNet/labels/EmotioNet_FACS_aws_2020_24600.csv")
labels = labels.dropna(axis=0)
for col in labels.columns:
if "AU" in col:
kwargs = {
col.replace("'", '').replace('"', '').replace(" ", ""):
labels[[col]]
}
labels = labels.assign(**kwargs)
labels = labels.drop(columns=col)
labels = labels.assign(
URL=labels.URL.apply(lambda x: x.split("/")[-1].replace("'", "")))
## How to use the Feat Detector class. *Written by Jin Hyun Cheong* Here is an example of how to use the `Detector` class to detect faces, facial landmarks, Action Units, and emotions, from face images or videos. ## Detecting facial expressions from images. First, load the detector class. You can specify which models you want to use. from feat import Detector face_model = "retinaface" landmark_model = "mobilenet" au_model = "jaanet" emotion_model = "fer" detector = Detector() Find the file you want to process. In our case, we'll use our test image `input.jpg`. # Find the file you want to process. from feat.tests.utils import get_test_data_path import os test_data_dir = get_test_data_path() test_image = os.path.join(test_data_dir, "input.jpg") Here is what our test image looks like. from PIL import Image import matplotlib.pyplot as plt f, ax = plt.subplots() im = Image.open(test_image)
# Installation example *Written by Jin Hyun Cheong* Open the current notebook in [Google Colab](http://colab.research.google.com/) and run the cell below to install Py-Feat. # Install Py-Feat from Pypi. !pip install py-feat # Check Fex class installation. from feat import Fex fex = Fex() # Check Detector class installation. from feat import Detector detector = Detector()
#!/usr/bin/env python3
from feat import Detector
face_model = "retinaface"
landmark_model = "mobilenet"
au_model = "rf"
emotion_model = "resmasknet"
detector = Detector(face_model=face_model,
landmark_model=landmark_model,
au_model=au_model,
emotion_model=emotion_model)
#!/usr/bin/env python3
from feat import Detector
face_model = "retinaface"
landmark_model = "mobilenet"
au_model = "rf"
emotion_model = "resmasknet"
detector = Detector(face_model=face_model,
landmark_model=landmark_model,
au_model=au_model,
emotion_model=emotion_model)
from feat.tests.utils import get_test_data_path
import os
test_data_dir = get_test_data_path()
test_image = os.path.join(test_data_dir, "input.jpg")
from PIL import Image
import matplotlib.pyplot as plt
f, ax = plt.subplots()
im = Image.open(test_image)
ax.imshow(im)
image_prediction = detector.detect_image(test_image)
# Show results
image_prediction
Here is an example of how to use the `Detector` class to detect faces, facial landmarks, Action Units, and emotions, from face images or videos. Let's start by installing Py-FEAT if you have not already done so or usign this from Google Colab !pip install -q py-feat ## Detecting facial expressions from images. First, load the detector class. You can specify which models you want to use. from feat import Detector face_model = "retinaface" landmark_model = "mobilenet" au_model = "rf" emotion_model = "resmasknet" detector = Detector(face_model = face_model, landmark_model = landmark_model, au_model = au_model, emotion_model = emotion_model) Find the file you want to process. In our case, we'll use our test image `input.jpg`. # Find the file you want to process. from feat.tests.utils import get_test_data_path import os test_data_dir = get_test_data_path() test_image = os.path.join(test_data_dir, "input.jpg") Here is what our test image looks like. from PIL import Image import matplotlib.pyplot as plt f, ax = plt.subplots() im = Image.open(test_image)