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()
# Here is what our test image looks like. # In[4]: from PIL import Image import matplotlib.pyplot as plt f, ax = plt.subplots() im = Image.open(test_image) ax.imshow(im) # Now we use our initialized `detector` instance to make predictions with the `detect_image()` method. # In[5]: image_prediction = detector.detect_image(test_image) # Show results image_prediction # The output is a `Fex` class instance which allows you to run the built-in methods for `Fex`. # # ## Visualizing detection results. # # For example, you can easily plot the detection results. # In[6]: image_prediction.plot_detections() # If you are interested in visualizing the head pose, you can do so simply by setting `pose=True`. This setting will overlay the x,y, and z-axis of the head onto the image.
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) ax.imshow(im); Now we use our initialized `detector` instance to make predictions with the `detect_image()` method. image_prediction = detector.detect_image(test_image) # Show results image_prediction The output is a `Fex` class instance which allows you to run the built-in methods for `Fex`. ## Visualizing detection results. For example, you can easily plot the detection results. image_prediction.plot_detections(); ## Accessing face expression columns of interest. You can also access the columns of interests (AUs, emotion) quickly.
#!/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