def identify_actors(video_path, clf, clf_th, out_name):
""" Identifies the actors using a classifier and generates an output video
Arguments:
----------
video_path:
type: string
info: path to where the video is stored
clf:
type: FaceRecognizer object
info: trained classifier to identify faces
clf_th:
type: int / float
info: threshold to identify a face as 'Unknown'
out_name:
type: string
info: name of the generated video file
"""
video = cv2.VideoCapture(video_path)
# The new generated video is prepared to be saved
video_w = int(video.get(3))
video_h = int(video.get(4))
video_fps = int(video.get(5))
out_path = compute_path(out_name + '.mp4', 'video')
out = cv2.VideoWriter(
filename=out_path,
fourcc=cv2.VideoWriter_fourcc('X', '2', '6', '4'),
fps=video_fps,
frameSize=(video_w, video_h)
)
not_finished, frame = video.read()
while not_finished:
# Modify the frame for each detected face
frame = modify_frame(frame, clf, clf_th)
# Write the frame into the new video file
out.write(frame)
not_finished, frame = video.read()
video.release()
out.release()
def save_image(image, output_folder, output_name):
""" Stores the given image in the output path with the output name
Arguments:
----------
image:
type: PIL image
info: image to store
output_path:
type: string
info: folder to store the image
output_name:
type: string
info: name of the image file
"""
folder_path = compute_path(output_folder, 'dataset')
os.makedirs(folder_path, exist_ok=True)
file_path = os.path.join(folder_path, output_name + '.png')
image.save(file_path)
def testComputePath(self):
self.assertEqual('dc=example,dc=com', utils.compute_path('OU=somewhere,DC=example,DC=com'))
self.assertEqual('', utils.compute_path('DC=com'))