def createVideo(title, script, publishTime):
intro = VideoFileClip("clips/intro.mp4")
body = VideoFileClip("clips/body.mp4")
loop = VideoFileClip("clips/loop.mp4")
outro = VideoFileClip("clips/outro.mp4")
titleMP3 = ''.join(e for e in title if e.isalnum())
titleMP3 = titleMP3 + ".mp3"
# title + ".mp3"
audioclip = AudioFileClip(titleMP3)
scriptLen = audioclip.duration
loopLen = loop.duration
multiplier = scriptLen / loopLen
new_audioclip = CompositeAudioClip([body.audio, audioclip])
body.audio = new_audioclip
x = [intro, body]
multiplier = multiplier - 1
while multiplier > 0:
x.extend([loop])
multiplier = multiplier - 1
x.extend([outro])
final_clip = concatenate_videoclips(x)
titleMP4 = ''.join(e for e in title if e.isalnum())
titleMP4 = titleMP4 + ".mp4"
# titleMP4 = title + ".mp4"
final_clip.write_videofile(titleMP4)
uploadVid(title, script, titleMP4, publishTime)
def take(self, options: dict):
if options["with_video"]:
self._take_video(options["video_url"])
if options["with_audio"]:
self._take_audio(options["audio_url"])
if options["with_video"]:
video = VideoFileClip(self.__path_to_save_video)
audio = AudioFileClip(self.__path_to_save_audio)
video.audio = audio.set_duration(video.duration)
os.remove(self.__path_to_save_video)
os.remove(self.__path_to_save_audio)
video.write_videofile(self.__path_to_save_video)
def main(filename, tmp_result):
args = lambda: None
args.video = True
args.youtube_video_url = ''
args.video_speedup = 2
args.webcam = False
a = 0
b = 0
c = 0
for i in range(
0,
len(
os.listdir(
'C:/Users/mmlab/PycharmProjects/UI_pyqt/cluster_people'))):
# print(len(os.listdir('C:/Users/mmlab/PycharmProjects/facenet-pytorch-master/facenet-pytorch-master/models/clusteringfolder/{}'.format(i))))
a += len(
os.listdir(
'C:/Users/mmlab/PycharmProjects/UI_pyqt/cluster_people/{}'.
format(i) + 'human'))
print("asdfasdfdsa")
print(
len(os.listdir(
'C:/Users/mmlab/PycharmProjects/UI_pyqt/cluster_people')))
folder = []
folder_name = []
folder_in_file = []
under_folder = []
for i in range(
0,
len(
os.listdir(
'C:/Users/mmlab/PycharmProjects/UI_pyqt/cluster_people'))):
b = len(
os.listdir(
'C:/Users/mmlab/PycharmProjects/UI_pyqt/cluster_people/{}'.
format(i) + 'human'))
d = int(b / a * 100)
print(d)
folder.append(
'C:/Users/mmlab/PycharmProjects/UI_pyqt/cluster_people/{}'.format(
i) + 'human')
folder_name.append(i)
if d < 30:
under_folder.append(str(i) + 'human')
for i in range(len(under_folder)):
print(under_folder[i])
for i in range(0, len(folder)):
print(folder[i])
print(folder_name[i])
if int(folder_name[i]) > 0:
file = os.path.join(
'C:/Users/mmlab/PycharmProjects/UI_pyqt/cluster_people/{}'.
format(int(folder_name[i])) + 'human',
str(folder_name[i]) + 'human1.png')
folder_in_file.append(file)
#print(folder_in_file[i])
for i in range(0, len(folder_in_file) - 1):
cv = cv2.imread(folder_in_file[i], cv2.IMREAD_COLOR)
#cv2.imwrite('model{}.png'.format(i), cv)
minsize = 20
threshold = [0.6, 0.7, 0.7]
factor = 0.709
image_size = 182
input_image_size = 160
# comment out these lines if you do not want video recording
# USE FOR RECORDING VIDEO
fourcc = cv2.VideoWriter_fourcc(*'FMP4')
# Get the path of the classifier and load it
project_root_folder = os.path.join(
os.path.abspath(__file__), 'C:/Users/mmlab/PycharmProjects/UI_pyqt/')
classifier_path = project_root_folder + 'trained_classifier/video_new_name_test4.pkl'
print(classifier_path)
with open(classifier_path, 'rb') as f:
(model, class_names) = pickle.load(f)
print("Loaded classifier file")
with tf.Graph().as_default():
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.6)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options,
log_device_placement=False))
with sess.as_default():
# Bounding box
pnet, rnet, onet = src.align.detect_face.create_mtcnn(
sess, project_root_folder + "src/align")
# Get the path of the facenet model and load it
facenet_model_path = project_root_folder + "20180402-114759/20180402-114759.pb"
facenet.load_model(facenet_model_path)
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")
embedding_size = embeddings.get_shape()[1]
# Start video capture
people_detected = set()
person_detected = collections.Counter()
video_path = project_root_folder
video_name = filename # 영상
full_original_video_path_name = filename
print(filename)
video_capture_path = full_original_video_path_name
if not os.path.isfile(full_original_video_path_name):
print('Video not found at path ' +
full_original_video_path_name +
'. Commencing download from YouTube')
# Note if the video ever gets removed this will cause issues
#YouTube(args.youtube_video_url).streams.first().download(output_path =video_path, filename=video_name)
video_capture = cv2.VideoCapture(full_original_video_path_name)
width = video_capture.get(cv2.CAP_PROP_FRAME_WIDTH) # float
height = video_capture.get(cv2.CAP_PROP_FRAME_HEIGHT) # float
videoclip = VideoFileClip(full_original_video_path_name)
audioclip = videoclip.audio
#저장파일 이름
video_recording = cv2.VideoWriter(
project_root_folder + 'final_video_mosaic.avi', fourcc, 15,
(int(width), int(height)))
output_video_name = project_root_folder + 'final_video_mosaic.avi'
total_frames_passed = 0
while True:
try:
ret, frame = video_capture.read()
except Exception as e:
break
if ret:
# Skip frames if video is to be sped up
if args.video_speedup:
total_frames_passed += 1
if total_frames_passed % args.video_speedup != 0:
continue
bounding_boxes, _ = src.align.detect_face.detect_face(
frame, minsize, pnet, rnet, onet, threshold, factor)
frame_track = []
faces_found = bounding_boxes.shape[0]
#known_name=under_folder
known_name = ['4human']
if faces_found > 0:
det = bounding_boxes[:, 0:4]
bb = np.zeros((faces_found, 4), dtype=np.int32)
for i in range(faces_found):
bb[i][0] = det[i][0]
bb[i][1] = det[i][1]
bb[i][2] = det[i][2]
bb[i][3] = det[i][3]
# inner exception
if bb[i][0] <= 0 or bb[i][1] <= 0 or bb[i][
2] >= len(
frame[0]) or bb[i][3] >= len(frame):
print('face is inner of range!')
continue
cropped = frame[bb[i][1]:bb[i][3],
bb[i][0]:bb[i][2], :]
scaled = cv2.resize(
cropped, (input_image_size, input_image_size),
interpolation=cv2.INTER_CUBIC)
# cv2.imshow("Cropped and scaled", scaled)
# cv2.waitKey(1)
scaled = facenet.prewhiten(scaled)
# cv2.imshow("\"Whitened\"", scaled)
# cv2.waitKey(1)
scaled_reshape = scaled.reshape(
-1, input_image_size, input_image_size, 3)
feed_dict = {
images_placeholder: scaled_reshape,
phase_train_placeholder: False
}
emb_array = sess.run(embeddings,
feed_dict=feed_dict)
predictions = model.predict_proba(emb_array)
best_class_indices = np.argmax(predictions, axis=1)
best_class_probabilities = predictions[
np.arange(len(best_class_indices)),
best_class_indices]
best_name = class_names[best_class_indices[0]]
if best_class_probabilities > 0.09:
for y in range(0, len(known_name)):
if class_names[best_class_indices[
0]] == known_name[y]:
frame[bb[i][1] + 5:bb[i][3] - 5,
bb[i][0] + 2:bb[i][2] -
2] = cv2.blur(
frame[bb[i][1] + 5:bb[i][3] -
5, bb[i][0] +
2:bb[i][2] - 2],
(50, 50))
for j in range(100):
c = +1
person_detected[best_name] += 1
else:
cv2.rectangle(frame, (bb[i][0], bb[i][1]),
(bb[i][2], bb[i][3]),
(0, 255, 0), 2)
# total_frames_passed += 1
# if total_frames_passed == 2:
for person, count in person_detected.items():
if count > 4:
print("Person Detected: {}, Count: {}".format(
person, count))
people_detected.add(person)
# person_detected.clear()
# total_frames_passed = 0
#cv2.putText(frame, "People detected so far:", (20, 20), cv2.FONT_HERSHEY_PLAIN,
#1, (255, 0, 0), thickness=1, lineType=2)
'''
currentYIndex = 40
for idx, name in enumerate(people_detected):
cv2.putText(frame, name, (20, currentYIndex + 20 * idx), cv2.FONT_HERSHEY_PLAIN,
1, (0, 0, 255), thickness=1, lineType=2)
'''
cv2.imshow("Face Detection and Identification", frame)
video_recording.write(frame)
frame_track.append(frame)
#if cv2.waitKey(1) & 0xFF == ord('q'):
# break
else:
break
print("mosaiced")
video_recording.release()
video_capture.release()
cv2.destroyAllWindows()
videoclip2 = VideoFileClip(output_video_name)
videoclip2.audio = audioclip
#저장파일 일므
videoclip2.write_videofile("tmp_result1.mp4")
print("done")
def test_issue_334():
# NOTE: this is horrible. Any simpler version ?
last_move = None
last_move1 = None
lis = [
(0.0, 113, 167, 47),
(0.32, 138, 159, 47),
(0.44, 152, 144, 47),
(0.48, 193, 148, 47),
(0.6, 193, 148, 47),
(0.76, 205, 138, 55),
(0.88, 204, 121, 63),
(0.92, 190, 31, 127),
(1.2, 183, 59, 127),
(1.4, 137, 22, 127),
(1.52, 137, 22, 127),
(1.72, 129, 67, 127),
(1.88, 123, 69, 127),
(2.04, 131, 123, 63),
(2.24, 130, 148, 63),
(2.48, 130, 148, 63),
(2.8, 138, 180, 63),
(3.0, 138, 180, 63),
(3.2, 146, 192, 63),
(3.28, 105, 91, 151),
(3.44, 105, 91, 151),
(3.72, 11, 48, 151),
(3.96, 5, 78, 151),
(4.32, 4, 134, 1),
(4.6, 149, 184, 48),
(4.8, 145, 188, 48),
(5.0, 154, 217, 48),
(5.08, 163, 199, 48),
(5.2, 163, 199, 48),
(5.32, 164, 187, 48),
(5.48, 163, 200, 48),
(5.76, 163, 200, 48),
(5.96, 173, 199, 48),
(6.0, 133, 172, 48),
(6.04, 128, 165, 48),
(6.28, 128, 165, 48),
(6.4, 129, 180, 48),
(6.52, 133, 166, 48),
(6.64, 133, 166, 48),
(6.88, 144, 183, 48),
(7.0, 153, 174, 48),
(7.16, 153, 174, 48),
(7.24, 153, 174, 48),
(7.28, 253, 65, 104),
(7.64, 253, 65, 104),
(7.8, 279, 116, 80),
(8.0, 290, 105, 80),
(8.24, 288, 124, 80),
(8.44, 243, 102, 80),
(8.56, 243, 102, 80),
(8.8, 202, 107, 80),
(8.84, 164, 27, 104),
(9.0, 164, 27, 104),
(9.12, 121, 9, 104),
(9.28, 77, 33, 104),
(9.32, 52, 23, 104),
(9.48, 52, 23, 104),
(9.64, 33, 46, 104),
(9.8, 93, 49, 104),
(9.92, 93, 49, 104),
(10.16, 173, 19, 104),
(10.2, 226, 173, 48),
(10.36, 226, 173, 48),
(10.48, 211, 172, 48),
(10.64, 208, 162, 48),
(10.92, 220, 171, 48),
]
lis1 = [
(0.0, 113, 167, 47),
(0.32, 138, 159, 47),
(0.44, 152, 144, 47),
(0.48, 193, 148, 47),
(0.6, 193, 148, 47),
(0.76, 205, 138, 55),
(0.88, 204, 121, 63),
(0.92, 190, 31, 127),
(1.2, 183, 59, 127),
(1.4, 137, 22, 127),
(1.52, 137, 22, 127),
(1.72, 129, 67, 127),
(1.88, 123, 69, 127),
(2.04, 131, 123, 63),
(2.24, 130, 148, 63),
(2.48, 130, 148, 63),
(2.8, 138, 180, 63),
(3.0, 138, 180, 63),
(3.2, 146, 192, 63),
(3.28, 105, 91, 151),
(3.44, 105, 91, 151),
(3.72, 11, 48, 151),
(3.96, 5, 78, 151),
(4.32, 4, 134, 1),
(4.6, 149, 184, 48),
(4.8, 145, 188, 48),
(5.0, 154, 217, 48),
(5.08, 163, 199, 48),
(5.2, 163, 199, 48),
(5.32, 164, 187, 48),
(5.48, 163, 200, 48),
(5.76, 163, 200, 48),
(5.96, 173, 199, 48),
(6.0, 133, 172, 48),
(6.04, 128, 165, 48),
(6.28, 128, 165, 48),
(6.4, 129, 180, 48),
(6.52, 133, 166, 48),
(6.64, 133, 166, 48),
(6.88, 144, 183, 48),
(7.0, 153, 174, 48),
(7.16, 153, 174, 48),
(7.24, 153, 174, 48),
(7.28, 253, 65, 104),
(7.64, 253, 65, 104),
(7.8, 279, 116, 80),
(8.0, 290, 105, 80),
(8.24, 288, 124, 80),
(8.44, 243, 102, 80),
(8.56, 243, 102, 80),
(8.8, 202, 107, 80),
(8.84, 164, 27, 104),
(9.0, 164, 27, 104),
(9.12, 121, 9, 104),
(9.28, 77, 33, 104),
(9.32, 52, 23, 104),
(9.48, 52, 23, 104),
(9.64, 33, 46, 104),
(9.8, 93, 49, 104),
(9.92, 93, 49, 104),
(10.16, 173, 19, 104),
(10.2, 226, 173, 48),
(10.36, 226, 173, 48),
(10.48, 211, 172, 48),
(10.64, 208, 162, 48),
(10.92, 220, 171, 48),
]
def posi(t):
global last_move
if len(lis) == 0:
return (last_move[1], last_move[2])
if t >= lis[0][0]:
last_move = item = lis.pop(0)
return (item[1], item[2])
else:
if len(lis) > 0:
dura = lis[0][0] - last_move[0]
now = t - last_move[0]
w = (lis[0][1] - last_move[1]) * (now / dura)
h = (lis[0][2] - last_move[2]) * (now / dura)
# print t, last_move[1] + w, last_move[2] + h
return (last_move[1] + w, last_move[2] + h)
return (last_move[1], last_move[2])
def size(t):
global last_move1
if len(lis1) == 0:
return (last_move1[3], last_move1[3] * 1.33)
if t >= lis1[0][0]:
last_move1 = item = lis1.pop(0)
return (item[3], item[3] * 1.33)
else:
if len(lis) > 0:
dura = lis1[0][0] - last_move1[0]
now = t - last_move1[0]
s = (lis1[0][3] - last_move1[3]) * (now / dura)
nsw = last_move1[3] + s
nsh = nsw * 1.33
# print t, nsw, nsh
return (nsw, nsh)
return (last_move1[3], last_move1[3] * 1.33)
avatar = VideoFileClip("media/big_buck_bunny_432_433.webm", has_mask=True)
avatar.audio = None
maskclip = ImageClip("media/afterimage.png", ismask=True, transparent=True)
avatar.set_mask(maskclip) # must set maskclip here..
concatenated = concatenate_videoclips([avatar] * 3)
tt = VideoFileClip("media/big_buck_bunny_0_30.webm").subclip(0, 3)
# TODO: Setting mask here does not work:
# .set_mask(maskclip).resize(size)])
final = CompositeVideoClip(
[tt, concatenated.set_position(posi).resize(size)])
final.duration = tt.duration
final.write_videofile(os.path.join(TMP_DIR, "issue_334.mp4"), fps=10)
def main(filename,tmp_result, known_name):
args = lambda: None
args.video = True
args.youtube_video_url = ''
args.video_speedup = 2
args.webcam = False
minsize = 20
threshold = [0.6, 0.7, 0.7]
factor = 0.709
image_size = 182
input_image_size = 160
img = cv2.imread("test7.jpg")
img = cv2.resize(img, (int(img.shape[1] * 0.6), int(img.shape[0] * 0.6)))
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
mask = np.zeros_like(img_gray)
indexes_triangles = []
# dlib에 있는 정면 얼굴 검출기(detector)로 입력 사진에서 얼굴을 검출해 faces로 반환
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
# Face 1
faces = detector(img_gray)
for face in faces:
# print(face)
landmarks = predictor(img_gray, face)
landmarks_points = []
for n in range(0, 68):
x = landmarks.part(n).x
y = landmarks.part(n).y
landmarks_points.append((x, y))
points = np.array(landmarks_points, np.int32)
convexhull = cv2.convexHull(points)
# cv2.polylines(img, [convexhull], True, (255, 0, 0), 3)
cv2.fillConvexPoly(mask, convexhull, 255)
face_image_1 = cv2.bitwise_and(img, img, mask=mask)
# Delaunay triangulation
rect = cv2.boundingRect(convexhull)
subdiv = cv2.Subdiv2D(rect)
subdiv.insert(landmarks_points)
triangles = subdiv.getTriangleList() # landmarks_point 값을 배열로 변환
triangles = np.array(triangles, dtype=np.int32)
for t in triangles:
pt1 = (t[0], t[1]) # 삼각형의 좌표를 배열로 저장
pt2 = (t[2], t[3])
pt3 = (t[4], t[5])
# 삼각형의 좌표와 landmarks_point 가 만나는 곳의 점을 저장(0~68)
index_pt1 = np.where((points == pt1).all(axis=1))
index_pt1 = extract_index_nparray(index_pt1)
index_pt2 = np.where((points == pt2).all(axis=1))
index_pt2 = extract_index_nparray(index_pt2)
index_pt3 = np.where((points == pt3).all(axis=1))
index_pt3 = extract_index_nparray(index_pt3)
if index_pt1 is not None and index_pt2 is not None and index_pt3 is not None:
triangle = [index_pt1, index_pt2, index_pt3] # 삼각형마다의 landmarks_point
indexes_triangles.append(triangle)
# comment out these lines if you do not want video recording
# USE FOR RECORDING VIDEO
fourcc = cv2.VideoWriter_fourcc(*'FMP4')
# Get the path of the classifier and load it
project_root_folder = os.path.join(os.path.abspath(__file__), "C:/Users/mmlab/PycharmProjects/UI_pyqt/")
classifier_path = project_root_folder + 'trained_classifier/video_new_name_test4.pkl'
print (classifier_path)
with open(classifier_path, 'rb') as f:
(model, class_names) = pickle.load(f)
print("Loaded classifier file")
with tf.Graph().as_default():
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.6)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False))
with sess.as_default():
# Bounding box
pnet, rnet, onet = src.align.detect_face.create_mtcnn(sess, project_root_folder + "src/align")
# Get the path of the facenet model and load it
facenet_model_path = project_root_folder + "20180402-114759/20180402-114759.pb"
facenet.load_model(facenet_model_path)
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")
embedding_size = embeddings.get_shape()[1]
# Start video capture
people_detected = set()
person_detected = collections.Counter()
if args.webcam is True:
video_capture = cv2.VideoCapture(0)
else:
video_path = project_root_folder
video_name = "vlog"
full_original_video_path_name = filename
video_capture_path = full_original_video_path_name
if not os.path.isfile(full_original_video_path_name):
print('Video not found at path ' + full_original_video_path_name + '. Commencing download from YouTube')
# Note if the video ever gets removed this will cause issues
#YouTube(args.youtube_video_url).streams.first().download(output_path =video_path, filename=video_name)
video_capture = cv2.VideoCapture(full_original_video_path_name)
width = video_capture.get(cv2.CAP_PROP_FRAME_WIDTH) # float
height = video_capture.get(cv2.CAP_PROP_FRAME_HEIGHT) # float
videoclip = VideoFileClip(full_original_video_path_name)
audioclip = videoclip.audio
video_recording = cv2.VideoWriter(project_root_folder + 'final_video_swap.avi', fourcc, 13,(int(width), int(height)))
output_video_name = project_root_folder + 'final_video_swap.avi'
total_frames_passed = 0
while True:
try:
ret, frame = video_capture.read()
except Exception as e:
break
if ret:
# Skip frames if video is to be sped up
if args.video_speedup:
total_frames_passed += 1
if total_frames_passed % args.video_speedup != 0:
continue
bounding_boxes, _ = src.align.detect_face.detect_face(frame, minsize, pnet, rnet, onet,threshold, factor)
if bounding_boxes is not None:
print('maps:' + str(bounding_boxes))
faces_found = bounding_boxes.shape[0]
#number = len(under_folder)
#for n in range(number):
#known_name[n] = under_folder[n]
#known_name = ['2human']
if faces_found > 0:
det = bounding_boxes[:, 0:4]
bb = np.zeros((faces_found, 4), dtype=np.int32)
for i in range(faces_found):
bb[i][0] = det[i][0]
bb[i][1] = det[i][1]
bb[i][2] = det[i][2]
bb[i][3] = det[i][3]
if bb[i][0] <= 0 or bb[i][1] <= 0 or bb[i][2] >= len(frame[0]) or bb[i][3] >= len(frame):
print('face is inner of range!')
continue
cropped = frame[bb[i][1]:bb[i][3], bb[i][0]:bb[i][2], :]
scaled = cv2.resize(cropped, (input_image_size, input_image_size), interpolation=cv2.INTER_CUBIC)
# cv2.imshow("Cropped and scaled", scaled)
# cv2.waitKey(1)
scaled = facenet.prewhiten(scaled)
# cv2.imshow("\"Whitened\"", scaled)
# cv2.waitKey(1)
scaled_reshape = scaled.reshape(-1, input_image_size, input_image_size, 3)
feed_dict = {images_placeholder: scaled_reshape, phase_train_placeholder: False}
emb_array = sess.run(embeddings, feed_dict=feed_dict)
predictions = model.predict_proba(emb_array)
best_class_indices = np.argmax(predictions, axis=1)
best_class_probabilities = predictions[np.arange(len(best_class_indices)), best_class_indices]
best_name = class_names[best_class_indices[0]]
print("Name: {}, Probability: {}".format(best_name, best_class_probabilities))
if best_class_probabilities > 0.09:
#cv2.rectangle(frame, (bb[i][0], bb[i][1]), (bb[i][2], bb[i][3]), (0, 255, 0), 2) #boxing face
text_x = bb[i][0]
text_y = bb[i][3] + 20
for j in range(len(known_name)):
if class_names[best_class_indices[0]] == known_name[j]:
img2=frame[bb[i][1]-10 : bb[i][3]+20, bb[i][0]-10: bb[i][2]+20]
try:
img2_gray=cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
img2_new_face = np.zeros_like(img2)
faces2 = detector(img2_gray)
print(len(faces2))
if len(faces2)>0:
face1=faces2[0]
landmarks = predictor(img2_gray, face1)
landmarks_points2 = []
for n in range(0, 68):
x = landmarks.part(n).x
y = landmarks.part(n).y
landmarks_points2.append((x, y))
# for face in faces2:
# landmarks = predictor(img2_gray, face)
# landmarks_points2 = []
# for n in range(0, 68):
# x = landmarks.part(n).x
# y = landmarks.part(n).y
# landmarks_points2.append((x, y))
# cv2.circle(img2, (x, y), 3, (0, 255, 0), -1)
points2 = np.array(landmarks_points2, np.int32)
convexhull2 = cv2.convexHull(points2)
lines_space_mask = np.zeros_like(img_gray)
lines_space_new_face = np.zeros_like(img2)
# Triangulation of both faces
for triangle_index in indexes_triangles:
# Triangulation of the first face
tr1_pt1 = landmarks_points[triangle_index[0]]
tr1_pt2 = landmarks_points[triangle_index[1]]
tr1_pt3 = landmarks_points[triangle_index[2]]
triangle1 = np.array([tr1_pt1, tr1_pt2, tr1_pt3], np.int32)
rect1 = cv2.boundingRect(triangle1)
(x, y, w, h) = rect1
cropped_triangle = img[y: y + h, x: x + w]
cropped_tr1_mask = np.zeros((h, w), np.uint8)
points = np.array([[tr1_pt1[0] - x, tr1_pt1[1] - y],[tr1_pt2[0] - x, tr1_pt2[1] - y],[tr1_pt3[0] - x, tr1_pt3[1] - y]], np.int32)
cv2.fillConvexPoly(cropped_tr1_mask, points, 255)
# Triangulation of second face
tr2_pt1 = landmarks_points2[triangle_index[0]]
tr2_pt2 = landmarks_points2[triangle_index[1]]
tr2_pt3 = landmarks_points2[triangle_index[2]]
triangle2 = np.array([tr2_pt1, tr2_pt2, tr2_pt3], np.int32)
rect2 = cv2.boundingRect(triangle2)
(x, y, w, h) = rect2
if x<0:
x=0
rect2=(x, y, w, h)
(x,y,w,h)=rect2
if y<0:
y=0
rect2 = (x, y, w, h)
(x, y, w, h) = rect2
if w<0:
w=0
rect2 = (x, y, w, h)
(x, y, w, h) = rect2
if h<0:
h=0
rect2 = (x, y, w, h)
(x, y, w, h) = rect2
print(rect2)
cropped_tr2_mask = np.zeros((h, w), np.uint8)
points2 = np.array([[tr2_pt1[0] - x, tr2_pt1[1] - y],[tr2_pt2[0] - x, tr2_pt2[1] - y],[tr2_pt3[0] - x, tr2_pt3[1] - y]], np.int32)
cv2.fillConvexPoly(cropped_tr2_mask, points2, 255)
# Warp triangles
points = np.float32(points)
points2 = np.float32(points2)
M = cv2.getAffineTransform(points, points2)
warped_triangle = cv2.warpAffine(cropped_triangle, M, (w, h))
warped_triangle = cv2.bitwise_and(warped_triangle, warped_triangle,mask=cropped_tr2_mask)
# Reconstructing destination face
img2_new_face_rect_area = img2_new_face[y: y + h, x: x + w]
img2_new_face_rect_area_gray = cv2.cvtColor(img2_new_face_rect_area,cv2.COLOR_BGR2GRAY)
_, mask_triangles_designed = cv2.threshold(img2_new_face_rect_area_gray, 1,255, cv2.THRESH_BINARY_INV)
_, mask_triangles_designed2 = cv2.threshold(warped_triangle,1, 255,cv2.THRESH_BINARY_INV)
print(len(warped_triangle))
print(len(mask_triangles_designed))
if len(warped_triangle) == len(mask_triangles_designed):
warped_triangle = cv2.bitwise_and(warped_triangle, warped_triangle,mask=mask_triangles_designed)
else:
warped_triangle = warped_triangle
img2_new_face_rect_area = cv2.add(img2_new_face_rect_area, warped_triangle)
img2_new_face[y: y + h, x: x + w] = img2_new_face_rect_area
img2_face_mask = np.zeros_like(img2_gray)
img2_head_mask = cv2.fillConvexPoly(img2_face_mask, convexhull2, 255)
img2_face_mask = cv2.bitwise_not(img2_head_mask)
img2_head_noface = cv2.bitwise_and(img2, img2, mask=img2_face_mask)
result = cv2.add(img2_head_noface, img2_new_face)
(x, y, w, h) = cv2.boundingRect(convexhull2)
center_face2 = (int((x + x + w) / 2), int((y + y + h) / 2))
seamlessclone = cv2.seamlessClone(result, img2, img2_head_mask, center_face2, cv2.MIXED_CLONE)
frame[bb[i][1]-10 : bb[i][3]+20, bb[i][0]-10: bb[i][2]+20]=seamlessclone
cv2.imshow("result", result)
except Exception as e:
print(e)
pass
# cv2.putText(frame, class_names[best_class_indices[0]], (text_x, text_y),cv2.FONT_HERSHEY_COMPLEX_SMALL,1, (0, 0, 255), thickness=1, lineType=2)
# person_detected[best_name] += 1
# total_frames_passed += 1
# if total_frames_passed == 2:
for person, count in person_detected.items():
if count > 4:
print("Person Detected: {}, Count: {}".format(person, count))
people_detected.add(person)
# person_detected.clear()
total_frames_passed = 0
# cv2.putText(frame, "People detected so far:", (20, 20), cv2.FONT_HERSHEY_PLAIN,
# 1, (255, 0, 0), thickness=1, lineType=2)
currentYIndex = 40
for idx, name in enumerate(people_detected):
cv2.putText(frame, name, (20, currentYIndex + 20 * idx), cv2.FONT_HERSHEY_PLAIN,
1, (0, 0, 255), thickness=1, lineType=2)
cv2.imshow("Face Detection and Identification", frame)
video_recording.write(frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
else:
break
video_recording.release()
video_capture.release()
cv2.destroyAllWindows()
videoclip2 = VideoFileClip(output_video_name)
videoclip2.audio = audioclip
videoclip2.write_videofile(tmp_result)
def main(filename, tmp_result):
args = lambda: None
args.video = True
args.youtube_video_url = ''
args.video_speedup = 2
args.webcam = False
a = 0
b = 0
c = 0
for i in range(
0,
len(
os.listdir(
'C:/Users/mmlab/PycharmProjects/UI_pyqt/cluster_people'))):
# print(len(os.listdir('C:/Users/mmlab/PycharmProjects/facenet-pytorch-master/facenet-pytorch-master/models/clusteringfolder/{}'.format(i))))
a += len(
os.listdir(
'C:/Users/mmlab/PycharmProjects/UI_pyqt/cluster_people/{}'.
format(i) + 'human'))
print(
len(os.listdir(
'C:/Users/mmlab/PycharmProjects/UI_pyqt/cluster_people')))
folder = []
folder_name = []
folder_in_file = []
under_folder = []
for i in range(
0,
len(
os.listdir(
'C:/Users/mmlab/PycharmProjects/UI_pyqt/cluster_people'))):
b = len(
os.listdir(
'C:/Users/mmlab/PycharmProjects/UI_pyqt/cluster_people/{}'.
format(i) + 'human'))
d = int(b / a * 100)
print(d)
folder.append(
'C:/Users/mmlab/PycharmProjects/UI_pyqt/cluster_people/{}'.format(
i) + 'human')
folder_name.append(i)
if d < 30:
under_folder.append(str(i) + 'human')
for i in range(len(under_folder)):
print(under_folder[i])
for i in range(0, len(folder)):
print(folder[i])
print(folder_name[i])
if int(folder_name[i]) > 0:
file = os.path.join(
'C:/Users/mmlab/PycharmProjects/UI_pyqt/cluster_people/{}'.
format(int(folder_name[i])) + 'human',
str(folder_name[i]) + 'human1.png')
folder_in_file.append(file)
minsize = 20
threshold = [0.6, 0.7, 0.7]
factor = 0.709
input_image_size = 160
# comment out these lines if you do not want video recording
# USE FOR RECORDING VIDEO
fourcc = cv2.VideoWriter_fourcc(*'FMP4')
# Get the path of the classifier and load it
project_root_folder = os.path.join(
os.path.abspath(__file__), 'C:/Users/mmlab/PycharmProjects/UI_pyqt/')
classifier_path = project_root_folder + 'trained_classifier/video_new_name_test4.pkl'
with open(classifier_path, 'rb') as f:
(model, class_names) = pickle.load(f)
with tf.Graph().as_default():
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.6)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options,
log_device_placement=False))
with sess.as_default():
# Bounding box
pnet, rnet, onet = src.align.detect_face.create_mtcnn(
sess, project_root_folder + "src/align")
# Get the path of the facenet model and load it
facenet_model_path = project_root_folder + "20180402-114759/20180402-114759.pb"
facenet.load_model(facenet_model_path)
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")
# Start video capture
video_capture = cv2.VideoCapture(filename)
width = video_capture.get(cv2.CAP_PROP_FRAME_WIDTH) # float
height = video_capture.get(cv2.CAP_PROP_FRAME_HEIGHT) # float
videoclip = VideoFileClip(filename)
audioclip = videoclip.audio
#저장파일 이름
video_recording = cv2.VideoWriter(
project_root_folder + 'final_video_mosaic.avi', fourcc, 15,
(int(width), int(height)))
output_video_name = project_root_folder + 'final_video_mosaic.avi'
total_frames_passed = 0
while True:
try:
ret, frame = video_capture.read()
except Exception as e:
break
if ret:
# Skip frames if video is to be sped up
if args.video_speedup:
total_frames_passed += 1
if total_frames_passed % args.video_speedup != 0:
continue
bounding_boxes, _ = src.align.detect_face.detect_face(
frame, minsize, pnet, rnet, onet, threshold, factor)
frame_track = []
faces_found = bounding_boxes.shape[0]
#known_name=under_folder
known_name = ['1human']
if faces_found > 0:
det = bounding_boxes[:, 0:4]
bb = np.zeros((faces_found, 4), dtype=np.int32)
for i in range(faces_found):
bb[i][0] = det[i][0]
bb[i][1] = det[i][1]
bb[i][2] = det[i][2]
bb[i][3] = det[i][3]
# inner exception
if bb[i][0] <= 0 or bb[i][1] <= 0 or bb[i][
2] >= len(
frame[0]) or bb[i][3] >= len(frame):
print('face is inner of range!')
continue
cropped = frame[bb[i][1]:bb[i][3],
bb[i][0]:bb[i][2], :]
scaled = cv2.resize(
cropped, (input_image_size, input_image_size),
interpolation=cv2.INTER_CUBIC)
scaled = facenet.prewhiten(scaled)
scaled_reshape = scaled.reshape(
-1, input_image_size, input_image_size, 3)
feed_dict = {
images_placeholder: scaled_reshape,
phase_train_placeholder: False
}
emb_array = sess.run(embeddings,
feed_dict=feed_dict)
predictions = model.predict_proba(emb_array)
best_class_indices = np.argmax(predictions, axis=1)
best_class_probabilities = predictions[
np.arange(len(best_class_indices)),
best_class_indices]
if best_class_probabilities > 0.09:
for y in range(0, len(known_name)):
if class_names[best_class_indices[
0]] != known_name[y]:
frame[bb[i][1] + 5:bb[i][3] - 5,
bb[i][0] + 2:bb[i][2] -
2] = cv2.blur(
frame[bb[i][1] + 5:bb[i][3] -
5, bb[i][0] +
2:bb[i][2] - 2],
(50, 50))
video_recording.write(frame)
frame_track.append(frame)
else:
break
print("mosaiced")
video_recording.release()
video_capture.release()
cv2.destroyAllWindows()
videoclip2 = VideoFileClip(output_video_name)
videoclip2.audio = audioclip
videoclip2.write_videofile("tmp_result1.mp4")
print("done")
audio_output_path = filename.replace('.mp3', '-manipulated.wav')
print("SLOWING AND REVERBING")
slow_and_reverb(input_path=filename, output_path=audio_output_path)
os.remove(filename)
print("DONE SLOWING AND REVERBING")
print("MAKING VIDEO")
video_output_path = filename.replace('.mp3', '.gif')
download_gif(video_output_path)
soundtrack = AudioFileClip(audio_output_path)
videoclip = VideoFileClip(video_output_path).loop(
duration=soundtrack.duration)
final_path = download_dir + '/' + unique_id + '/' + video_title + ' - Slowed And Reverbed.mp4'
videoclip.audio = soundtrack
videoclip.write_videofile(final_path,
codec='mpeg4',
audio_bitrate="320k")
print("DONE MAKING VIDEO")
youtube_title = video_title + ' - Slowed And Reverbed'
os.remove(audio_output_path)
os.remove(video_output_path)
processed_submissions.append(submission)
keywords = youtube_title.split(' ').extend(['slowed', 'reverbed'])
options = {
'snippet': {
'title': youtube_title,
'description': '😈',
def downloadRedditVideos(subreddit, time=1000, filter="month", output="output.mp4"):
for filename in os.listdir("videos/"):
file_path = os.path.join("videos/", filename)
try:
if os.path.isfile(file_path) or os.path.islink(file_path):
os.unlink(file_path)
except Exception as e:
print("Failed to delete %s. Reason: %s" % (file_path, e))
addTime = 0
i = 0
if subreddit is 1:
subreddit = "perfectlycutscreams"
elif subreddit is 2:
subreddit = "watchpeopledieinside"
elif subreddit is 3:
subreddit = "contagiouslaughter"
elif subreddit is 4:
subreddit = "livestreamfail"
elif subreddit is 5:
subreddit = "whatcouldgowrong"
for submission in reddit.subreddit(subreddit).hot(limit=500):
if submission.media is not None:
if (
"https://clips.twitch.tv/" in submission.url
and "tt_" not in submission.url
):
if addTime < time:
dl_clip(submission.url, str(i).rjust(2, "0"))
videoD = VideoFileClip(
"videos/" + str(i).rjust(2, "0") + ".mp4"
).duration
addTime += videoD
i += 1
elif "reddit_video" in submission.media:
if (
addTime < time
and submission.media["reddit_video"]["duration"] < 200
):
video = submission.media["reddit_video"]["fallback_url"]
v = requests.get(video)
open("tmp/video.mp4", "wb").write(v.content)
a = requests.get(re.sub("[^/]*$", "audio", video, 1))
if a.status_code != 200:
b = requests.get(re.sub("[^/]*$", "DASH_audio.mp4", video, 1))
if b.status_code != 200:
open("videos/" + str(i).rjust(2, "0") + ".mp4", "wb").write(
v.content
)
else:
open("tmp/audio.mp4", "wb").write(b.content)
combined = VideoFileClip("tmp/video.mp4")
combined.audio = CompositeAudioClip(
[AudioFileClip("tmp/audio.mp4")]
)
combined.write_videofile(
"videos/" + str(i).rjust(2, "0") + ".mp4",
temp_audiofile="tmp/tmp_audio.mp3",
)
else:
open("tmp/audio.mp4", "wb").write(a.content)
combined = VideoFileClip("tmp/video.mp4")
combined.audio = CompositeAudioClip(
[AudioFileClip("tmp/audio.mp4")]
)
combined.write_videofile(
"videos/" + str(i).rjust(2, "0") + ".mp4",
temp_audiofile="tmp/tmp_audio.mp3",
)
os.unlink("tmp/video.mp4")
os.unlink("tmp/audio.mp4")
addTime += submission.media["reddit_video"]["duration"]
print("Video seconds: " + str(addTime))
i += 1
