def init_tensor_flow(self):
print("Initializing Tensor-flow ...")
self.graph = tf.Graph()
self.sess = tf.Session()
print("Tensorflow session created ")
self.pnet, self.rnet, self.onet = detect_and_align.create_mtcnn(
self.sess, None)
print("Loading model file...")
# Load the model
self.load_model('./model/')
print("Model file loaded...")
self.images_placeholder = tf.get_default_graph().get_tensor_by_name(
"input:0")
self.embeddings = tf.get_default_graph().get_tensor_by_name(
"embeddings:0")
self.phase_train_placeholder = tf.get_default_graph(
).get_tensor_by_name("phase_train:0")
self.id_dataset = id_data.get_id_data('./ids/', self.pnet, self.rnet,
self.onet, self.sess,
self.embeddings,
self.images_placeholder,
self.phase_train_placeholder)
self.print_id_dataset_table()
def reload_id_set(self):
print("reloading id set")
self.id_dataset = id_data.get_id_data('./ids/', self.pnet, self.rnet,
self.onet, self.sess,
self.embeddings,
self.images_placeholder,
self.phase_train_placeholder)
self.print_id_dataset_table()
def main(image_path, ready=False):
id_folder = ['ids']
test_folder = None
model = './model/'
with tf.Graph().as_default():
with tf.Session() as sess:
pnet, rnet, onet = detect_and_align.create_mtcnn(sess, None)
load_model(model)
images_placeholder = tf.get_default_graph().get_tensor_by_name("input:0")
embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0")
phase_train_placeholder = tf.get_default_graph().get_tensor_by_name("phase_train:0")
id_dataset = id_data.get_id_data(id_folder[0], pnet, rnet, onet, sess, embeddings, images_placeholder, phase_train_placeholder)
print_id_dataset_table(id_dataset)
test_run(pnet, rnet, onet, sess, images_placeholder, phase_train_placeholder, embeddings, id_dataset, test_folder)
show_landmarks = False
show_bb = False
show_id = True
show_fps = False
if ready is False:
frame = cv2.imread(image_path)
elif ready is True:
frame = image_path
face_patches, padded_bounding_boxes, landmarks = detect_and_align.align_image(frame, pnet, rnet, onet)
if len(face_patches) > 0:
face_patches = np.stack(face_patches)
feed_dict = {images_placeholder: face_patches, phase_train_placeholder: False}
embs = sess.run(embeddings, feed_dict=feed_dict)
print('Matches in frame:')
for i in range(len(embs)):
bb = padded_bounding_boxes[i]
matching_id, dist = find_matching_id(id_dataset, embs[i, :])
if matching_id:
print('Hi %s! Distance: %1.4f' % (matching_id, dist))
else:
matching_id = 'Unknown'
print('Unkown! Couldn\'t fint match.')
return matching_id
else:
matching_id = 'Face Unfound'
print(matching_id)
return matching_id
def gen1():
"""Video streaming generator function."""
print("Starting to generate frames!!!")
with tf.Graph().as_default():
with tf.Session() as sess:
print("Initialize tensor")
pnet, rnet, onet = detect_and_align.create_mtcnn(sess, None)
print("Loading model file...")
# Load the model
load_model('./model/')
print("Model file loaded...")
images_placeholder = tf.get_default_graph().get_tensor_by_name(
"input:0")
embeddings = tf.get_default_graph().get_tensor_by_name(
"embeddings:0")
phase_train_placeholder = tf.get_default_graph(
).get_tensor_by_name("phase_train:0")
id_dataset = id_data.get_id_data('./ids/', pnet, rnet, onet, sess,
embeddings, images_placeholder,
phase_train_placeholder)
print_id_dataset_table(id_dataset)
while True:
frame = camera.get_frame()
print("Processing frame...")
face_patches, padded_bounding_boxes, landmarks = detect_and_align.align_image(
frame, pnet, rnet, onet)
if len(face_patches) > 0:
face_patches = np.stack(face_patches)
feed_dict = {
images_placeholder: face_patches,
phase_train_placeholder: False
}
embs = sess.run(embeddings, feed_dict=feed_dict)
print('Matches in frame:')
for i in range(len(embs)):
bb = padded_bounding_boxes[i]
matching_id, dist = find_matching_id(
id_dataset, embs[i, :])
if matching_id:
print('Hi %s! Distance: %1.4f' %
(matching_id, dist))
else:
matching_id = 'Unkown'
print('Unkown! Couldn\'t fint match.')
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(frame, matching_id, (bb[0], bb[3]), font,
1, (255, 255, 255), 1, cv2.LINE_AA)
cv2.rectangle(frame, (bb[0], bb[1]), (bb[2], bb[3]),
(255, 0, 0), 2)
else:
print("No face patches")
# cnt = cv2.imencode('.jpeg', frame)[1]
# b64 = base64.encodebytes(cnt)
encimg = cv2.imencode('.jpg', frame)[1].tostring()
yield (b'--frame\r\n'
b'Content-Type: image/jpeg\r\n\r\n' + encimg + b'\r\n')
def main(args):
with tf.Graph().as_default():
with tf.Session() as sess:
pnet, rnet, onet = detect_and_align.create_mtcnn(sess, None)
load_model(args.model)
images_placeholder = tf.get_default_graph().get_tensor_by_name("input:0")
embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0")
phase_train_placeholder = tf.get_default_graph().get_tensor_by_name("phase_train:0")
id_dataset = id_data.get_id_data(args.id_folder[0], pnet, rnet, onet, sess, embeddings, images_placeholder, phase_train_placeholder)
print_id_dataset_table(id_dataset)
test_run(pnet, rnet, onet, sess, images_placeholder, phase_train_placeholder, embeddings, id_dataset, args.test_folder)
cap = cv2.VideoCapture(0)
frame_height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT) # AREA TO BE EDITED
show_landmarks = False
show_bb = False
show_id = True
show_fps = False
find_match = False
while(True):
start = time.time()
_, frame = cap.read()
face_patches, padded_bounding_boxes, landmarks = detect_and_align.align_image(frame, pnet, rnet, onet)
if len(face_patches) > 0:
face_patches = np.stack(face_patches)
feed_dict = {images_placeholder: face_patches, phase_train_placeholder: False}
embs = sess.run(embeddings, feed_dict=feed_dict)
print('Matches in frame:')
for i in range(len(embs)):
bb = padded_bounding_boxes[i]
matching_id, dist = find_matching_id(id_dataset, embs[i, :])
if matching_id:
print('Hi %s! Distance: %1.4f' %(matching_id, dist))
find_match = True
else:
matching_id = 'Unkown'
print('Unkown! Couldn\'t fint match.')
find_match = False
if show_id:
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(frame, matching_id, (bb[0], bb[3]), font, 1, (255, 255, 255), 1, cv2.LINE_AA)
if show_bb:
cv2.rectangle(frame, (bb[0], bb[1]), (bb[2], bb[3]), (255, 0, 0), 2)
if show_landmarks:
for j in range(5):
size = 1
top_left = (int(landmarks[i, j]) - size, int(landmarks[i, j + 5]) - size)
bottom_right = (int(landmarks[i, j]) + size, int(landmarks[i, j + 5]) + size)
cv2.rectangle(frame, top_left, bottom_right, (255, 0, 255), 2)
else:
print('Couldn\'t find a face')
if find_match is True:
end = time.time()
seconds = end - start
fps = round(1 / seconds, 2)
if show_fps:
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(frame, str(fps), (0, int(frame_height) - 5), font, 1, (255, 255, 255), 1, cv2.LINE_AA)
cv2.imshow('frame', frame)
key = cv2.waitKey(1)
if key == ord('q'):
break
elif key == ord('l'):
show_landmarks = not show_landmarks
elif key == ord('b'):
show_bb = not show_bb
elif key == ord('i'):
show_id = not show_id
elif key == ord('f'):
show_fps = not show_fps
cap.release()
cv2.destroyAllWindows()
def parse_arguments(argv):
parser = argparse.ArgumentParser()
parser.add_argument('model', type=str, help='Could be either a directory containing the meta_file and ckpt_file or a model protobuf (.pb) file')
parser.add_argument('id_folder', type=str, nargs='+', help='Folder containing ID folders')
parser.add_argument('--test_folder', type=str, help='Folder containing test images.', default=None)
return parser.parse_args(argv)
if __name__ == '__main__':
main(parse_arguments(sys.argv[1:]))
def main(args):
with tf.Graph().as_default():
with tf.Session() as sess:
faceCascade = cv2.CascadeClassifier(
'classifier/haarcascade_frontalface_default.xml')
file = os.path.join(os.getcwd(), "log.csv")
pnet, rnet, onet = detect_and_align.create_mtcnn(sess, None)
load_model(args.model)
images_placeholder = tf.get_default_graph().get_tensor_by_name(
"input:0")
embeddings = tf.get_default_graph().get_tensor_by_name(
"embeddings:0")
phase_train_placeholder = tf.get_default_graph(
).get_tensor_by_name("phase_train:0")
id_dataset = id_data.get_id_data(args.id_folder[0], pnet, rnet,
onet, sess, embeddings,
images_placeholder,
phase_train_placeholder)
print_id_dataset_table(id_dataset)
test_run(pnet, rnet, onet, sess, images_placeholder,
phase_train_placeholder, embeddings, id_dataset,
args.test_folder)
cap = cv2.VideoCapture(0)
cap.set(4, 9600)
cap.set(3, 12800)
frame_height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
int_frame_height = int(frame_height)
# img_path = os.path.join(args.id_folder)
# f = open("D:\\Apps\\ml-comm\\smile\\facerecog\\FaceRecognition-master\\log.csv", 'a')
f = open(
"C:\\Users\\Siva-Datta.Mannava\\OneDrive - Shell\\Apps\\ml-comm\\smile\\facerecog\\FaceRecognition-master\\log.csv",
'a')
# df = pd.read_csv("D:\\Apps\\ml-comm\\smile\\facerecog\\FaceRecognition-master\\log.csv")
df = pd.read_csv(
"C:\\Users\\Siva-Datta.Mannava\\OneDrive - Shell\\Apps\\ml-comm\\smile\\facerecog\\FaceRecognition-master\\log.csv"
)
# df = pd.read_csv(f)
# print("df", df)
# columns = pd.read_csv("D:\\Apps\\ml-comm\\smile\\facerecog\\FaceRecognition-master\\log.csv", nrows = 0)
# print('columns', columns)
count = 0
show_landmarks = False
show_bb = False
show_id = True
show_fps = False
total_people = 10
matched_ids = []
ids = os.listdir('ids')
while (True):
start = time.time()
_, frames = cap.read()
gray = cv2.cvtColor(frames, cv2.COLOR_BGR2GRAY)
count_msg = "Evacuated staff: " + str(count)
emergency_msg = "Emergency Evacuation"
remaining_msg = "Remaining staff: " + str(total_people - count)
cv2.putText(frames, emergency_msg, (0, 80),
cv2.FONT_HERSHEY_SIMPLEX, 3, (0, 0, 255), 3,
cv2.LINE_AA)
faces = faceCascade.detectMultiScale(
gray,
scaleFactor=1.05,
minNeighbors=8,
minSize=(55, 55),
flags=cv2.CASCADE_SCALE_IMAGE)
imgs = []
if len(ids) < 5:
show_missing(frames, ids, 0, int_frame_height)
for (x, y, w, h) in faces:
crop_img = frames[y - 50:y + h + 25, x - 25:x + w + 25]
imgs.append(crop_img)
for frame in imgs:
face_patches, padded_bounding_boxes, landmarks = detect_and_align.align_image(
frame, pnet, rnet, onet)
id_dataset = id_data.get_id_data(args.id_folder[0], pnet,
rnet, onet, sess,
embeddings,
images_placeholder,
phase_train_placeholder)
if len(face_patches) > 0:
face_patches = np.stack(face_patches)
feed_dict = {
images_placeholder: face_patches,
phase_train_placeholder: False
}
embs = sess.run(embeddings, feed_dict=feed_dict)
print('Matches in frame:')
for i in range(len(embs)):
bb = padded_bounding_boxes[i]
matching_id, dist = find_matching_id(
id_dataset, embs[i, :])
if matching_id:
print('Hi %s! Distance: %1.4f' %
(matching_id, dist))
if matching_id not in matched_ids:
matched_ids.append(matching_id)
count = count + 1
count_msg = "Evacuated staff: " + str(
count)
remaining_msg = "Remaining staff: " + str(
total_people - count)
if matching_id in ids:
ids.remove(matching_id)
else:
matching_id = 'Unkown'
print('Unkown! Couldn\'t fint match.')
'''Make folder and store the image'''
try:
# os.makedirs(os.path.join(args.id_folder, str(count)))
# dir1 = "D:\\Apps\\ml-comm\\smile\\facerecog\\FaceRecognition-master\\ids\\" + str(count)
dir1 = "C:\\Users\\Siva-Datta.Mannava\\OneDrive - Shell\\Apps\\ml-comm\\smile\\facerecog\\FaceRecognition-master\\ids\\" + str(
count)
os.makedirs(dir1)
except OSError as e:
if e.errno != errno.EEXIST:
raise
# path_img = "D:\\Apps\\ml-comm\\smile\\facerecog\\FaceRecognition-master\\ids\\" + str(count) + "\\"
path_img = "C:\\Users\\Siva-Datta.Mannava\\OneDrive - Shell\\Apps\\ml-comm\\smile\\facerecog\\FaceRecognition-master\\ids\\" + str(
count) + "\\"
img_name = path_img + str(count) + '.png'
cv2.imwrite(img_name, frame)
# start = time.time()
entry_time = datetime.datetime.fromtimestamp(
start).strftime('%c')
df_tmp = pd.DataFrame(
[[str(count), entry_time, "Still_Inside"]],
columns=df.columns)
count = count + 1
count_msg = "Evacuated staff: " + str(count)
remaining_msg = "Remaining staff: " + str(
total_people - count)
df = df.append(df_tmp)
df.to_csv(f, header=False, index=False)
if show_id:
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(frame, matching_id, (bb[0], bb[3]),
font, 1, (255, 255, 255), 1,
cv2.LINE_AA)
if show_bb:
cv2.rectangle(frame, (bb[0], bb[1]),
(bb[2], bb[3]), (255, 0, 0), 2)
if show_landmarks:
for j in range(5):
size = 1
top_left = (int(landmarks[i, j]) - size,
int(landmarks[i, j + 5]) - size)
bottom_right = (int(landmarks[i, j]) + size,
int(landmarks[i, j + 5]) +
size)
cv2.rectangle(frame, top_left, bottom_right,
(255, 0, 255), 2)
else:
print('Couldn\'t find a face')
if len(ids) < 5:
show_missing(frames, ids, 0, int_frame_height)
cv2.putText(frames, remaining_msg, (0, int_frame_height - 15),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 1,
cv2.LINE_AA)
cv2.putText(frames, count_msg, (0, 145),
cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 255, 0), 2,
cv2.LINE_AA)
end = time.time()
seconds = end - start
fps = round(1 / seconds, 2)
if show_fps:
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(frames, str(fps), (0, int(frame_height) - 5),
font, 1, (255, 255, 255), 1, cv2.LINE_AA)
cv2.imshow('frame', frames)
key = cv2.waitKey(1)
if key == ord('q'):
f.close()
break
elif key == ord('l'):
show_landmarks = not show_landmarks
elif key == ord('b'):
show_bb = not show_bb
elif key == ord('i'):
show_id = not show_id
elif key == ord('f'):
show_fps = not show_fps
cap.release()
cv2.destroyAllWindows()
