def image(input_image_path, display, gradcam, output_csv_file, screen_size,
device, branch, face_detection):
"""
Receives the full path to a image file and recognizes
facial expressions of the closets face in a frame-based approach.
"""
write_to_file = not (output_csv_file is None)
img = uimage.read(input_image_path)
# Call FER method
fer = cvision.recognize_facial_expression(img, device, face_detection,
gradcam)
if write_to_file:
ufile.create_file(output_csv_file, input_image_path)
ufile.write_to_file(fer, 0.0)
ufile.close_file()
if display:
fer_demo = FERDemo(screen_size=screen_size,
display_individual_classification=branch,
display_graph_ensemble=False)
fer_demo.update(fer)
while fer_demo.is_running():
fer_demo.show()
fer_demo.quit()
def image(input_image_path, display, gradcam, output_csv_file, screen_size, device, branch, face_detection):
"""
Receives the full path to a image file and recognizes
facial expressions of the closets face in a frame-based approach.
TODO: Write docstring
:param input_image_path:
:param display:
:param gradcam:
:param output_csv_file:
:param screen_size:
:param device:
:param branch:
:return:
"""
image = uimage.read(input_image_path)
# Call FER method
fer = cvision.recognize_facial_expression(image, device, face_detection, gradcam)
# TODO: Implement
if output_csv_file:
pass
if display:
fer_demo = FERDemo(screen_size=screen_size, display_individual_classification=branch, display_graph_ensemble=False)
fer_demo.update(fer)
while fer_demo.is_running():
fer_demo.show()
fer_demo.quit()
return fer
def webcam(camera_id, display, gradcam, output_csv_file, screen_size, device,
frames, branch, no_plot, face_detection):
"""
Receives images from a camera and recognizes
facial expressions of the closets face in a frame-based approach.
TODO: Write docstring
:param no_plot:
:param camera_id:
:param display:
:param gradcam:
:param output_csv_file:
:param screen_size:
:param device:
:param frames:
:param branch:
:return:
"""
fer_demo = None
if not uimage.initialize_video_capture(camera_id):
raise RuntimeError("Error on initializing video capture." +
"\nCheck whether a webcam is working or not." +
"In linux, you can use Cheese for testing.")
uimage.set_fps(frames)
# Initialize screen
if display:
fer_demo = FERDemo(screen_size=screen_size,
display_individual_classification=branch,
display_graph_ensemble=(not no_plot))
try:
# Loop to process each frame from a VideoCapture object.
while uimage.is_video_capture_open() and (
(not display) or (display and fer_demo.is_running())):
# Get a frame
image = uimage.get_frame()
fer = None if (
image is None) else cvision.recognize_facial_expression(
image, device, face_detection, gradcam)
# Display blank screen if no face is detected, otherwise,
# display detected faces and perceived facial expression labels
if display:
fer_demo.update(fer)
fer_demo.show()
# TODO: Implement
if output_csv_file:
pass
except Exception as e:
print("Error raised during video mode.")
raise e
finally:
uimage.release_video_capture()
fer_demo.quit()
def video(input_video_path, display, gradcam, output_csv_file, screen_size,
device, frames, branch, no_plot, face_detection):
"""
Receives the full path to a video file and recognizes
facial expressions of the closets face in a frame-based approach.
TODO: Write docstring
:param input_video_path:
:param display:
:param gradcam:
:param output_csv_file:
:param screen_size:
:param device:
:param frames:
:param branch:
:return:
"""
fer_demo = None
candidate = []
if not uimage.initialize_video_capture(input_video_path):
raise RuntimeError(
"Error on initializing video capture." +
"\nCheck whether working versions of ffmpeg or gstreamer is installed."
+ "\nSupported file format: MPEG-4 (*.mp4).")
uimage.set_fps(frames)
# Initialize screen
if display:
fer_demo = FERDemo(screen_size=screen_size,
display_individual_classification=branch,
display_graph_ensemble=(not no_plot))
try:
# Loop to process each frame from a VideoCapture object.
while uimage.is_video_capture_open() and (
(not display) or (display and fer_demo.is_running())):
# Get a frame
image = uimage.get_frame()
fer = None if (
image is None) else cvision.recognize_facial_expression(
image, device, face_detection, gradcam)
candidate.append(fer)
#print(candidate)
# Display blank screen if no face is detected, otherwise,
# display detected faces and perceived facial expression labels
if display:
fer_demo.update(fer)
fer_demo.show()
# TODO: Implement
if output_csv_file:
pass
return candidate
except Exception as e:
#print("Error raised during video mode.")
return candidate
def predict_single_image(img_path):
# Read an image
image = cv2.imread(img_path, cv2.IMREAD_COLOR)
# Recognize a facial expression if a face is detected.
# The boolean argument set to False indicates that the process runs on CPU
fer = cvision.recognize_facial_expression(image, True, None, False)
# Print list of emotions (individual classification from 9 convolutional branches and the ensemble classification)
# Last prediction is ensemble prediction
# print(fer.list_emotion)
voted_emotion = fer.list_emotion[-1] if fer.list_emotion is not None else None
return voted_emotion
def webcam(camera_id, display, gradcam, output_csv_file, screen_size, device,
frames, branch, no_plot, face_detection):
"""
Receives images from a camera and recognizes
facial expressions of the closets face in a frame-based approach.
"""
fer_demo = None
write_to_file = not (output_csv_file is None)
starting_time = time.time()
if not uimage.initialize_video_capture(camera_id):
raise RuntimeError("Error on initializing video capture." +
"\nCheck whether a webcam is working or not." +
"In linux, you can use Cheese for testing.")
uimage.set_fps(frames)
# Initialize screen
if display:
fer_demo = FERDemo(screen_size=screen_size,
display_individual_classification=branch,
display_graph_ensemble=(not no_plot))
else:
print("Press 'Ctrl + C' to quit.")
try:
if write_to_file:
ufile.create_file(output_csv_file, str(time.time()))
# Loop to process each frame from a VideoCapture object.
while uimage.is_video_capture_open() and (
(not display) or (display and fer_demo.is_running())):
# Get a frame
img, _ = uimage.get_frame()
fer = None if (
img is None) else cvision.recognize_facial_expression(
img, device, face_detection, gradcam)
# Display blank screen if no face is detected, otherwise,
# display detected faces and perceived facial expression labels
if display:
fer_demo.update(fer)
fer_demo.show()
if write_to_file:
ufile.write_to_file(fer, time.time() - starting_time)
except Exception as e:
print("Error raised during video mode.")
raise e
except KeyboardInterrupt as qe:
print("Keyboard interrupt event raised.")
finally:
uimage.release_video_capture()
if display:
fer_demo.quit()
if write_to_file:
ufile.close_file()
def video(input_video_path, display, gradcam, output_csv_file, screen_size,
device, frames, branch, no_plot, face_detection):
"""
Receives the full path to a video file and recognizes
facial expressions of the closets face in a frame-based approach.
"""
fer_demo = None
write_to_file = not (output_csv_file is None)
if not uimage.initialize_video_capture(input_video_path):
raise RuntimeError(
"Error on initializing video capture." +
"\nCheck whether working versions of ffmpeg or gstreamer is installed."
+ "\nSupported file format: MPEG-4 (*.mp4).")
uimage.set_fps(frames)
# Initialize screen
if display:
fer_demo = FERDemo(screen_size=screen_size,
display_individual_classification=branch,
display_graph_ensemble=(not no_plot))
try:
if write_to_file:
ufile.create_file(output_csv_file, input_video_path)
# Loop to process each frame from a VideoCapture object.
while uimage.is_video_capture_open() and (
(not display) or (display and fer_demo.is_running())):
# Get a frame
img, timestamp = uimage.get_frame()
# Video has been processed
if img is None:
break
else: # Process frame
fer = None if (
img is None) else cvision.recognize_facial_expression(
img, device, face_detection, gradcam)
# Display blank screen if no face is detected, otherwise,
# display detected faces and perceived facial expression labels
if display:
fer_demo.update(fer)
fer_demo.show()
if write_to_file:
ufile.write_to_file(fer, timestamp)
except Exception as e:
print("Error raised during video mode.")
raise e
finally:
uimage.release_video_capture()
if display:
fer_demo.quit()
if write_to_file:
ufile.close_file()