class RunFaceID(object):
def __init__(self):
super(RunFaceID, self).__init__()
self.face_detection = FaceDetection()
self.face_recognition = FaceRecognition()
def predict(self, array_embeddings, embeddings_source):
return "unknown"
def processing(self, images, embeddings_source):
frame = copy.deepcopy(images)
faces = self.face_detection.detect_faces(frame)
if faces is None or len(faces) < 1:
return None
data = {}
array_img = []
labels = []
for x, y, w, h in faces:
if w > 0 and h > 0:
img_crop = frame[y:y + h, x:x + w, :]
array_img.append(img_crop)
# labels.append("unknown")
array_img = np.array(array_img)
# data["labels"] = labels
if count >= NUMBER_FRAME:
array_embeddings = self.face_recognition.embedding_image(array_img)
data["labels"] = self.predict_labels(array_embeddings,
embeddings_source)
data["bounding_boxs"] = faces
return data
class FaceFrameMorpher:
def __init__(self):
self.animated_scale = 0.0
self.animated_direction = 1
self.face_detection = FaceDetection()
self.MAX_ANIMATION_SCALE = 50.0
def morph_frame_faces(self, frames):
for frame in frames:
faces = self.face_detection.detect_faces(frame)
if len(faces) == 0:
self.reset_animation()
for (x, y, w, h) in faces:
self.morph_pixels_in_area_animated(frame, x, y, w, h)
return frames
def animated_scale_tick(self):
self.animated_scale += self.animated_direction
if self.animated_scale == -self.MAX_ANIMATION_SCALE or self.animated_scale == self.MAX_ANIMATION_SCALE:
self.animated_direction = -1 * self.animated_direction
def reset_animation(self):
self.animated_scale = 0.0
def morph_pixels_in_area_animated(self, img, x_pos, y_pos, width, height):
section = img[y_pos:y_pos + height, x_pos:x_pos + width]
self.animated_scale_tick()
section = FaceFrameMorpher.vertical_wave(section, self.animated_scale)
img[y_pos:y_pos + height, x_pos:x_pos + len(section)] = section
@staticmethod
def vertical_wave(img, wave_factor=20.0):
img_output = np.zeros(img.shape, dtype=img.dtype)
rows, cols = img.shape
for i in range(rows):
for j in range(cols):
offset_x = int(wave_factor * math.sin(2 * 3.14 * i / 180))
offset_y = int(wave_factor * math.sin(2 * 3.14 * j / 180))
# print(offset_x, offset_y, i, rows)
if j + offset_x < rows:
img_output[i, j] = img[abs((i + offset_y) % cols),
(j + offset_x) % cols]
else:
img_output[i, j] = img[i, j]
return img_output
class RunFaceID(object):
def __init__(self):
super(RunFaceID, self).__init__()
self.face_detection = FaceDetection()
self.face_recognition = FaceRecognition()
self.arr_embeddings = pickle.load(open("data_embeddings", "rb"))
self.labels = pickle.load(open("labels", "rb"))
def predict_labels(self, embeddings):
dis_cs = cs(embeddings, self.arr_embeddings)
index_list = np.argmax(dis_cs, axis=-1)
label_pred = []
for i, index in enumerate(index_list):
if dis_cs[i][index] > 0.6:
label_pred.append(self.labels[index])
else:
label_pred.append("unknown")
return label_pred
def processing(self, images, count):
frame = copy.deepcopy(images)
faces = self.face_detection.detect_faces(frame)
if faces is None or len(faces) < 1:
return None
data = {}
array_img = []
labels = []
for x, y, w, h in faces:
if w > 0 and h > 0:
img_crop = img_crop = frame[y:y + h, x:x + w, :]
array_img.append(img_crop)
labels.append("unknown")
array_img = np.array(array_img)
# data["labels"] = labels
if count >= 5:
array_embeddings = self.face_recognition.embedding_image(array_img)
data["labels"] = self.predict_labels(array_embeddings)
data["bounding_boxs"] = faces
return data
# labels.append("unknown")
array_img = np.array(array_img)
# data["labels"] = labels
if count >= NUMBER_FRAME:
array_embeddings = self.face_recognition.embedding_image(array_img)
data["labels"] = self.predict_labels(array_embeddings,
embeddings_source)
data["bounding_boxs"] = faces
return data
# runfaceid = RunFaceID()
cap = cv2.VideoCapture(0)
face_detect = FaceDetection()
i = 0
while (True):
# Capture frame-by-frame
ret, frame = cap.read()
faces = face_detect.detect_faces(frame)
if len(faces) > 0:
cv2.imwrite(str(i) + ".png", frame)
i += 1
if i == 5:
break
# Display the resulting frame
cv2.imshow('frame', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
class RunFaceID(object):
def __init__(self):
super(RunFaceID, self).__init__()
self.face_detection = FaceDetection()
self.face_recognition = FaceRecognition()
# self.arr_embeddings = pickle.load(open("data_embeddings", "rb"))
# self.labels = pickle.load(open("labels", "rb"))
def predict_labels(self, embeddings, embeddings_source, labels_index,
labels_name):
dis_cs = cs(embeddings, embeddings_source)
index_list = np.argmax(dis_cs, axis=-1)
label_pred = []
for i, index in enumerate(index_list):
if dis_cs[i][index] > 0.6:
label_index = labels_index[index]
for i, (index_tmp, name_tmp) in enumerate(labels_name):
if label_index == index_tmp:
label_pred.append(labels_name[i])
else:
label_pred.append([-1, "unknown"])
return label_pred
def processing(self, images, count, embeddings_source, labels_index,
labels_name, message_status):
frame = copy.deepcopy(images)
faces = self.face_detection.detect_faces(frame)
if faces is None or len(faces) < 1:
return None
data = {}
array_img = []
labels = []
for x, y, w, h in faces:
if w > 0 and h > 0:
img_crop = frame[y:y + h, x:x + w, :]
array_img.append(img_crop)
# labels.append("unknown")
array_img = np.array(array_img)
# data["labels"] = labels
if count >= NUMBER_FRAME:
array_embeddings = self.face_recognition.embedding_image(array_img)
data["labels"] = self.predict_labels(array_embeddings,
embeddings_source,
labels_index, labels_name)
data["bounding_boxs"] = faces
return data
def get_faces(self, images):
faces = self.face_detection.detect_faces(images)
return list(faces)
def get_bb_embeddings(self, images):
faces = self.get_faces(images)
if len(faces) != 1:
return None
array_img = []
data = {}
for x, y, w, h in faces:
if w > 0 and h > 0:
img_crop = images[y:y + h, x:x + w, :]
array_img.append(img_crop)
embeddings = self.face_recognition.embedding_image(array_img)[0]
data["bounding_box"] = json.dumps(faces[0].tolist())
data['embeddings'] = json.dumps(embeddings.tolist())
return data
