def __init__(self, model_path=None):
if model_path == None:
return
self.sess = tf.Session()
self.detect = face.Detection() # make a detector (using triple CNN)
with self.sess.as_default():
facenet.load_model(model_path)
if not os.path.exists(flat_dir):
os.makedirs(flat_dir)
pics = [f for f in listdir(screenshot_dir) if isfile(join(screenshot_dir, f))]
for i, pic in enumerate(pics):
file = os.path.join(screenshot_dir, pic)
try:
curr_img = imread(file)
except:
# failed to read image
# sometimes this happens because the correct file type is not returned
print('Image Read Failed.')
continue
detector = face.Detection()
detector.minsize = 10
detector.face_crop_margin = 16
try:
faces = detector.find_faces(curr_img)
except:
# Something in detector failed. Move on to next image
print('Skipped', pic, ' - Something went wrong finding faces')
if len(faces) == 0:
print('Skipped', pic, ' - No faces found')
else:
copy(file, os.path.join(raw_dir, pic))
for j, curr_face in enumerate(faces):
imwrite(os.path.join(
face_dir, '{}_{}-{}.jpg'.format(pic.split('.')[0], i + 1, j)),
def __init__(self, model_path):
self.detect = face.Detection()
self.encoder = Encoder(model_path)
import matplotlib.patches as patches
test_path = '../test_data/FaceDetect/WiderDataset'
img_filenames = ['1', '3', '4', '6', '43', '48']
test_img_vec = list()
fig_pre = plt.figure()
figlab = 'abcdef'
detectors = list() # List of detectors, one per image
faces_list = list() # List of facelists from each image
for i, filename in enumerate(img_filenames):
test_img_vec.append(imageio.imread(test_path + '/' + filename + '.jpg'))
fig_pre.add_subplot(2, 3, i + 1)
plt.imshow(test_img_vec[i])
plt.xlabel('({})'.format(figlab[i]))
# Create face detector
detectors.append(face.Detection())
detectors[-1].minsize = 10
detectors[-1].face_crop_margin = 16
faces_list.append(detectors[-1].find_faces(test_img_vec[i]))
plt.tight_layout()
plt.suptitle('Example Images')
plt.subplots_adjust(top=0.90)
plt.savefig('example_input_combined.png')
plt.show()
# Separate figure for face bound box and points
fig, ax = plt.subplots(2, 3)
plt.suptitle('Example Images with Bounding Boxes and Points')
for i, test_img in enumerate(test_img_vec):
faces = faces_list[i]
def main(movie_name, val, num_actors, num_images):
# Get IMDB instance
ia = IMDb()
# Get movie search
#movie_name = input('Search for the movie the database will be based on. (enter title): ')
with NoStdStreams():
results = ia.search_movie(movie_name)
# Print the movie title search results
print('\nSearch Results:\n')
print('Title, Year, Kind')
print('-----------------')
for i, result in enumerate(results):
if i > 9:
break
try:
print(
str(i + 1) + '. ' + result.data['title'] + ', (' +
str(result.data['year']) + '), ' + result.data['kind'])
except:
print('Skipped ' + result.data['title'] + ' because of an issue.')
#val = input('\nEnter a title number (non-matching characters will exit program): ')
# Select a title
try:
val = int(val)
if val in range(1, min(len(results), 10)):
movie = ia.get_movie(results[val - 1].movieID)
else:
print('Please enter a value between 1 and {}'.format(
min(len(results), 10)))
raise Exception
except:
quit('Quit signal given. Program Exited')
#
# Select number of actors from the selected title
#
print('\nTop 30 Billed Actors:\n')
print('Cast, Character')
print('---------------')
for i, castmember in enumerate(movie['cast']):
if i > 29:
break
print(str(i + 1) + '. ' + castmember['name'] + ', ' + castmember.notes)
#num_actors = input('\nHow many characters do you want to include? (Taken from top to bottom): ')
try:
num_actors = int(num_actors)
if num_actors not in range(1, min(len(movie['cast']), 30)):
print('Please enter a value between 1 and {}'.format(
min(movie['cast'], 30)))
raise Exception
except:
quit('Quit signal given. Program Exited')
# Select number of images to download
#num_images = input('\nHow many images per actors? (1-100): ')
try:
num_images = int(num_images)
if num_images not in range(1, 100):
print('Please enter a value between 1 and 100')
raise Exception
except:
quit('Quit signal given. Program Exited')
#
# Start Building the database
#
# Connor adjusted the path for his computer !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
output_root = '/home/connor/Documents/Stanford_Classes/CS230/CS230DeepActor/train_data/FaceID/'
movie_dir = output_root + '_'.join(movie['title'].split() +
[str(movie['year'])] +
[str(movie['kind'])]) + '/'
downloads_dir = movie_dir + 'downloaded/'
raw_dir = movie_dir + 'raw/'
face_dir = movie_dir + 'face/'
flat_dir = movie_dir + 'flattened/'
for i in range(num_actors):
character = movie['cast'][i].notes.split('/')[0]
char_actor = '--'.join([
'_'.join(character.split()), '_'.join(
movie['cast'][i]['name'].split())
]) + '/'
# Create directories for other outputs
if not os.path.exists(raw_dir + char_actor):
os.makedirs(raw_dir + char_actor)
if not os.path.exists(face_dir + char_actor):
os.makedirs(face_dir + char_actor)
if not os.path.exists(flat_dir + char_actor):
os.makedirs(flat_dir + char_actor)
if not os.path.exists(downloads_dir):
os.makedirs(downloads_dir)
query = ' '.join([
character, movie['title'], movie['cast'][i]['name'],
str(movie['year'])
])
query_dir = downloads_dir + '\"{}\"'.format(query)
print('\nDownloading {} images for {}'.format(
str(num_images), movie['cast'][i]['name']))
print(os.getcwd())
subprocess.check_output([
'googleimagesdownload', '-k', '\"{}\"'.format(query), '-l',
str(num_images), '-o', downloads_dir, '-f', 'jpg', '-s', 'medium'
])
print('Downloads finished')
print('Processing images and finding faces')
# Get a list of the pics for the current actor
noQuote_query_dir = downloads_dir + query
if not os.path.exists(query_dir):
query_dir = noQuote_query_dir # check if the directory has quotes or not
pics = [f for f in listdir(query_dir) if isfile(join(query_dir, f))
] # THere was an issue with using " " for the file
for j, pic in enumerate(pics):
file = query_dir + '/' + pic # Changed to noQuote query dir
try:
curr_img = imread(file)
except:
# failed to read image
# sometimes this happens because the correct file type is not returned
print('Image Read Failed.')
continue
detector = face.Detection()
detector.minsize = 10
detector.face_crop_margin = 16
faces = detector.find_faces(curr_img)
if len(faces) > 1:
print('Skipped', pic, ' - More than one face')
elif len(faces) == 0:
print('Skipped', pic, ' - No faces found')
else:
copy(file, raw_dir + char_actor + pic)
imwrite(face_dir + char_actor + '_'.join(character.split()) +
'{}.jpg'.format(j + 1),
faces[0].image,
format='jpg')
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=FutureWarning)
print('Flattening faces')
# Flatten faces
pwd = os.getcwd()
align_path = '/home/connor/Documents/CS230/CS230DeepActor/facenet/src/align/'
try:
os.chdir(align_path)
subprocess.check_output(
['python', 'align_dataset_mtcnn.py', face_dir, flat_dir])
os.chdir(pwd)
except:
os.chdir(pwd)
print('Database creation complete!')