def getFaces(images):
encs = []
faces = []
for n, img in enumerate(images):
print('Working on image [{}]'.format(n))
dets, scores, idx = detector.run(img, 1, -0.5)
print('{} faces detected'.format(len(dets)))
faces_n = []
boxes = []
encs_n = []
for i, d in enumerate(dets):
face = Face()
x1 = d.top(); y2 = d.right(); x2 = d.bottom(); y1 = d.left();
face.bound_box = (x1, y1, x2, y2)
boxes.append((x1, y2, x2, y1))
face.img_id = n
face.myFace = img[x1:x2, y1:y2]
faces_n.append(face)
cv2.rectangle(img, (y1,x1), (y2,x2), (0, 0, 255), 5)
print('{:.02f}'.format(scores[i]), idx[i], x1, y1, x2, y2)
encs_n = fr.face_encodings(img, boxes)
for i, e in enumerate(encs_n):
faces_n[i].enc = e
faces.append(faces_n[i])
encs.append(e)
plt.imshow(img)
plt.axis('off')
plt.show()
return faces, np.array(encs)
def getFaces(images):
'''
images :: tuple(name, numpy array)
n :: ground_truth number of faces in an image
'''
faces = []
for img in images:
print("Working on new image")
fls = fr.face_locations(img[1], model='hog')
encs = fr.face_encodings(img[1], fls)
if len(fls) == 0:
print("GOT NO FACES IN IMAGE")
print(len(encs), len(fls))
b = 1
for (fl, enc) in zip(fls, encs):
face = Face()
face.bound_box = fl
face.img_name = img[0]
face.box_no = b
b += 1
face.enc = enc
faces.append(face)
x1, y2, x2, y2 = fl
#print(x1,y1,x2,y2)
#if len(fls)>10:
# cv2.rectangle(img[1], (y1,x1), (y2,x2), (0, 0, 255), 5)
#if len(fls)>10:
# plt.imshow(img[1])
# plt.show()
return faces
def getFaces(images, n=-1):
'''
images :: tuple(name, numpy array)
n :: ground_truth number of faces in an image
'''
faces = []
possible_people = []
for img in images:
print("Working on new image")
fls = fr.face_locations(img[1], model='hog')
encs = fr.face_encodings(img[1], fls)
if len(fls) == 0:
print("GOT NO FACES IN IMAGE")
if (len(fls) == n):
start = len(faces)
end = start + n
possible_people.append(list(range(start, end)))
print(len(encs), len(fls))
b = 1
fig, ax = plt.subplots(1)
ax.imshow(img[1])
for (fl, enc) in zip(fls, encs):
face = Face()
face.bound_box = fl
face.img_name = img[0]
face.box_no = b
b += 1
face.enc = enc
faces.append(face)
tlx, tly, brx, bry = fl
#x1, y2, x2, y1
x1 = tlx
x2 = brx
y1 = bry
y2 = tly
face.myFace = img[1][x1:x2, y1:y2]
#print(x1,y1,x2,y2)
if len(fls) > -1:
cv2.rectangle(img[1], (y1, x1), (y2, x2), (0, 0, 255), 5)
rect = patches.Rectangle((x1, y1), (x2 - x1), (y2 - y1),
linewidth=2,
edgecolor='b')
#ax.add_patch(rect)
if len(fls) > -1:
y = cv2.resize(np.array(img[1]), (0, 0), fx=0.4, fy=0.4)
cv2.imshow('disp', y)
cv2.waitKey(1)
#plt.show()
#cv2.imshow(img[0], img[1])
#cv2.waitKey()
#cv2.destroyAllWindows()
if (possible_people):
return faces, possible_people[0]
else:
print("NO IMAGE WITH ALL PHOTOS")
#exit(0)
return faces, possible_people
def detect_embedd(self, images):
faces = []
for ind, image in enumerate(images):
print("Detecting faces in image {}/{}".format(
ind + 1, len(images)))
newFaces = []
det_arr, emb_arr = get_faces(image)
for fl, enc in zip(det_arr, emb_arr):
face = Face()
face.img_name = img.fileName
face.enc = emb_arr
x0, x1, x2, x3 = fl
face.bound_box = (x1, x2, x3, x0)
face.myFace = img.getImage()[x1:x3, x0:x2]
newFaces.append(face)
image.faces = np.array(newFaces)
faces += newFaces
return faces
def detect(self, images):
faces = []
for ind, img in enumerate(images):
print("Detecting faces in image {}/{}".format(
ind + 1, len(images)))
newfaces = []
fls = fr.face_locations(img.getImage(), model=self.model_name)
encs = fr.face_encodings(img.getImage(), fls)
for (fl, enc) in zip(fls, encs):
face = Face()
face.bound_box = fl
face.img_name = img.fileName
face.enc = enc
tlx, tly, brx, bry = fl
x1 = tlx
x2 = brx
y1 = bry
y2 = tly
face.myFace = img.image[x1:x2, y1:y2]
newfaces.append(face)
img.faces = np.array(newfaces)
faces += newfaces
return faces
