def reload_images(self, group_method, img_list):
"""
Reloads the image list by replacing the comparative values with those
that the chosen grouping method expects.
:param group_method: str name of the grouping method that will be used.
:param img_list: image list that has been sorted by one of the sort
methods.
:return: img_list but with the comparative values that the chosen
grouping method expects.
"""
import_face_recognition()
input_dir = self.args.input_dir
print("Preparing to group...")
if group_method == 'group_blur':
temp_list = [[x, self.estimate_blur(cv2.imread(x))] for x in tqdm(
self.find_images(input_dir), desc="Reloading", file=sys.stdout)
]
elif group_method == 'group_face':
temp_list = [[
x, face_recognition.face_encodings(cv2.imread(x))
] for x in tqdm(
self.find_images(input_dir), desc="Reloading", file=sys.stdout)
]
elif group_method == 'group_face_cnn':
import_FaceLandmarksExtractor()
temp_list = []
for x in tqdm(self.find_images(input_dir),
desc="Reloading",
file=sys.stdout):
d = FaceLandmarksExtractor.extract(
cv2.imread(x), 'cnn', True, input_is_predetected_face=True)
temp_list.append([
x,
np.array(d[0][1]) if len(d) > 0 else np.zeros((68, 2))
])
elif group_method == 'group_face_yaw':
import_FaceLandmarksExtractor()
temp_list = []
for x in tqdm(self.find_images(input_dir),
desc="Reloading",
file=sys.stdout):
d = FaceLandmarksExtractor.extract(
cv2.imread(x), 'cnn', True, input_is_predetected_face=True)
temp_list.append(
[x, self.calc_landmarks_face_yaw(np.array(d[0][1]))])
elif group_method == 'group_hist':
temp_list = [[
x,
cv2.calcHist([cv2.imread(x)], [0], None, [256], [0, 256])
] for x in tqdm(
self.find_images(input_dir), desc="Reloading", file=sys.stdout)
]
else:
raise ValueError("{} group_method not found.".format(group_method))
return self.splice_lists(img_list, temp_list)
def sort_face_yaw(self):
def calc_landmarks_face_pitch(fl): #unused
t = ((fl[6][1] - fl[8][1]) + (fl[10][1] - fl[8][1])) / 2.0
b = fl[8][1]
return b - t
def calc_landmarks_face_yaw(fl):
l = ((fl[27][0] - fl[0][0]) + (fl[28][0] - fl[1][0]) +
(fl[29][0] - fl[2][0])) / 3.0
r = ((fl[16][0] - fl[27][0]) + (fl[15][0] - fl[28][0]) +
(fl[14][0] - fl[29][0])) / 3.0
return r - l
import_FaceLandmarksExtractor()
input_dir = self.arguments.input_dir
img_list = []
for x in tqdm(self.find_images(input_dir),
desc="Loading",
file=sys.stdout):
d = FaceLandmarksExtractor.extract(cv2.imread(x),
'cnn',
True,
input_is_predetected_face=True)
img_list.append([x, calc_landmarks_face_yaw(np.array(d[0][1]))])
print("Sorting...")
img_list = sorted(img_list, key=operator.itemgetter(1), reverse=True)
return img_list
def sort_face_cnn_dissim(self):
from lib import FaceLandmarksExtractor
input_dir = self.arguments.input_dir
print("Sorting by face-cnn dissimilarity...")
img_list = []
for x in tqdm(self.find_images(input_dir),
desc="Loading",
file=sys.stdout):
d = FaceLandmarksExtractor.extract(cv2.imread(x), 'cnn', True)
img_list.append(
[x,
np.array(d[0][1]) if len(d) > 0 else np.zeros((68, 2)), 0])
img_list_len = len(img_list)
for i in tqdm(range(0, img_list_len - 1),
desc="Sorting",
file=sys.stdout):
score_total = 0
for j in range(i + 1, len(img_list)):
if i == j:
continue
fl1 = img_list[i][1]
fl2 = img_list[j][1]
score_total += np.sum(np.absolute((fl2 - fl1).flatten()))
img_list[i][2] = score_total
print("Sorting...")
img_list = sorted(img_list, key=operator.itemgetter(2), reverse=True)
return img_list
def sort_face_cnn_dissim(self):
import_FaceLandmarksExtractor()
input_dir = self.args.input_dir
print("Sorting by face-cnn dissimilarity...")
img_list = []
for x in tqdm(self.find_images(input_dir), desc="Loading", file=sys.stdout):
d = FaceLandmarksExtractor.extract(cv2.imread(x), 'cnn', True, input_is_predetected_face=True)
img_list.append([x, np.array(d[0][1]) if len(d) > 0 else np.zeros((68, 2)), 0])
img_list_len = len(img_list)
for i in tqdm(range(0, img_list_len - 1), desc="Sorting", file=sys.stdout):
score_total = 0
for j in range(i + 1, len(img_list)):
if i == j:
continue
fl1 = img_list[i][1]
fl2 = img_list[j][1]
score_total += np.sum(np.absolute((fl2 - fl1).flatten()))
img_list[i][2] = score_total
print("Sorting...")
img_list = sorted(img_list, key=operator.itemgetter(2), reverse=True)
return img_list
def sort_face_cnn(self):
import_FaceLandmarksExtractor()
input_dir = self.args.input_dir
print("Sorting by face-cnn similarity...")
img_list = []
for x in tqdm(self.find_images(input_dir), desc="Loading", file=sys.stdout):
d = FaceLandmarksExtractor.extract(cv2.imread(x), 'cnn', True, input_is_predetected_face=True)
img_list.append([x, np.array(d[0][1]) if len(d) > 0 else np.zeros((68, 2))])
img_list_len = len(img_list)
for i in tqdm(range(0, img_list_len - 1), desc="Sorting", file=sys.stdout):
min_score = float("inf")
j_min_score = i + 1
for j in range(i + 1, len(img_list)):
fl1 = img_list[i][1]
fl2 = img_list[j][1]
score = np.sum(np.absolute((fl2 - fl1).flatten()))
if score < min_score:
min_score = score
j_min_score = j
img_list[i + 1], img_list[j_min_score] = img_list[j_min_score], img_list[i + 1]
return img_list
def reload_images(self, group_method, img_list):
"""
Reloads the image list by replacing the comparative values with those
that the chosen grouping method expects.
:param group_method: str name of the grouping method that will be used.
:param img_list: image list that has been sorted by one of the sort
methods.
:return: img_list but with the comparative values that the chosen
grouping method expects.
"""
import_face_recognition()
input_dir = self.args.input_dir
print("Preparing to group...")
if group_method == 'group_blur':
temp_list = [[x, self.estimate_blur(cv2.imread(x))]
for x in
tqdm(self.find_images(input_dir), desc="Reloading", file=sys.stdout)]
elif group_method == 'group_face':
temp_list = [[x, face_recognition.face_encodings(cv2.imread(x))]
for x in
tqdm(self.find_images(input_dir), desc="Reloading", file=sys.stdout)]
elif group_method == 'group_face_cnn':
import_FaceLandmarksExtractor()
temp_list = []
for x in tqdm(self.find_images(input_dir), desc="Reloading", file=sys.stdout):
d = FaceLandmarksExtractor.extract(cv2.imread(x), 'cnn', True,
input_is_predetected_face=True)
temp_list.append([x, np.array(d[0][1]) if len(d) > 0 else np.zeros((68, 2))])
elif group_method == 'group_face_yaw':
import_FaceLandmarksExtractor()
temp_list = []
for x in tqdm(self.find_images(input_dir), desc="Reloading", file=sys.stdout):
d = FaceLandmarksExtractor.extract(cv2.imread(x), 'cnn', True,
input_is_predetected_face=True)
temp_list.append([x, self.calc_landmarks_face_yaw(np.array(d[0][1]))])
elif group_method == 'group_hist':
temp_list = [
[x, cv2.calcHist([cv2.imread(x)], [0], None, [256], [0, 256])]
for x in
tqdm(self.find_images(input_dir), desc="Reloading", file=sys.stdout)
]
else:
raise ValueError("{} group_method not found.".format(group_method))
return self.splice_lists(img_list, temp_list)
def detect_faces(frame, model="hog"):
fd = FaceLandmarksExtractor.extract(frame,
True if model == "cnn" else False)
for face in fd:
x, y, right, bottom, landmarks = face[0][0], face[0][1], face[0][
2], face[0][3], face[1]
yield DetectedFace(frame[y:bottom, x:right],
x,
right - x,
y,
bottom - y,
landmarksXY=landmarks)
def sort_face_yaw(self):
import_FaceLandmarksExtractor()
input_dir = self.args.input_dir
img_list = []
for x in tqdm(self.find_images(input_dir), desc="Loading", file=sys.stdout):
d = FaceLandmarksExtractor.extract(cv2.imread(x), 'cnn', True, input_is_predetected_face=True)
img_list.append([x, self.calc_landmarks_face_yaw(np.array(d[0][1]))])
print("Sorting by face-yaw...")
img_list = sorted(img_list, key=operator.itemgetter(1), reverse=True)
return img_list
def sort_face_yaw(self):
import_FaceLandmarksExtractor()
input_dir = self.args.input_dir
img_list = []
for x in tqdm(self.find_images(input_dir),
desc="Loading",
file=sys.stdout):
d = FaceLandmarksExtractor.extract(cv2.imread(x),
'cnn',
True,
input_is_predetected_face=True)
img_list.append(
[x, self.calc_landmarks_face_yaw(np.array(d[0][1]))])
print("Sorting by face-yaw...")
img_list = sorted(img_list, key=operator.itemgetter(1), reverse=True)
return img_list
def sort_face_cnn(self):
import_FaceLandmarksExtractor()
input_dir = self.arguments.input_dir
print("Sorting by face-cnn similarity...")
img_list = []
for x in tqdm(self.find_images(input_dir),
desc="Loading",
file=sys.stdout):
d = FaceLandmarksExtractor.extract(cv2.imread(x),
'cnn',
True,
input_is_predetected_face=True)
img_list.append(
[x, np.array(d[0][1]) if len(d) > 0 else np.zeros((68, 2))])
img_list_len = len(img_list)
for i in tqdm(range(0, img_list_len - 1),
desc="Sorting",
file=sys.stdout):
min_score = float("inf")
j_min_score = i + 1
for j in range(i + 1, len(img_list)):
fl1 = img_list[i][1]
fl2 = img_list[j][1]
score = np.sum(np.absolute((fl2 - fl1).flatten()))
if score < min_score:
min_score = score
j_min_score = j
img_list[
i +
1], img_list[j_min_score] = img_list[j_min_score], img_list[i +
1]
return img_list
def sort_face_yaw(self):
def calc_landmarks_face_pitch(fl): # unused
t = ((fl[6][1] - fl[8][1]) + (fl[10][1] - fl[8][1])) / 2.0
b = fl[8][1]
return b - t
def calc_landmarks_face_yaw(fl):
l = ((fl[27][0] - fl[0][0]) + (fl[28][0] - fl[1][0]) + (fl[29][0] - fl[2][0])) / 3.0
r = ((fl[16][0] - fl[27][0]) + (fl[15][0] - fl[28][0]) + (fl[14][0] - fl[29][0])) / 3.0
return r - l
import_FaceLandmarksExtractor()
input_dir = self.args.input_dir
img_list = []
for x in tqdm(self.find_images(input_dir), desc="Loading", file=sys.stdout):
d = FaceLandmarksExtractor.extract(cv2.imread(x), 'cnn', True, input_is_predetected_face=True)
img_list.append([x, calc_landmarks_face_yaw(np.array(d[0][1]))])
print("Sorting...")
img_list = sorted(img_list, key=operator.itemgetter(1), reverse=True)
return img_list
def detect_faces(frame, detector, verbose, rotation=0):
fd = FaceLandmarksExtractor.extract (frame, detector, verbose)
for face in fd:
x, y, right, bottom, landmarks = face[0][0], face[0][1], face[0][2], face[0][3], face[1]
yield DetectedFace(frame[y: bottom, x: right], rotation, x, right - x, y, bottom - y, landmarksXY=landmarks)