def receive_stream(self):
"""
Displays displayed stream in a window if no arguments are passed.
Keeps updating the 'current_frame' attribute with the most recent frame, this can be accessed using 'self.current_frame'
:param display: boolean, If False no stream output will be displayed.
:return: None
"""
global oldpoch, current_frames
self.keep_running = True
size = 0
count_frame = 0
while self.footage_socket and self.keep_running:
try:
frame = self.footage_socket.recv_string()
count_frame = count_frame + 1
size += self.utf8len(frame)
if self.second_passed(oldpoch):
oldpoch = time.time()
print(str(size) + " " + str(count_frame))
size = 0
count_frame = 0
current_frames.append(string_to_image(frame))
# if display and len(self.current_frames) >= 30:
# for img in range(len(self.current_frames)):
# cv2.imshow("Stream", self.current_frames.pop())
# cv2.waitKey(1)
except KeyboardInterrupt:
cv2.destroyAllWindows()
break
print("Streaming Stopped!")
def receive_stream(self, display=True):
"""
Displays displayed stream in a window if no arguments are passed.
Keeps updating the 'current_frame' attribute with the most recent frame, this can be accessed using 'self.current_frame'
:param display: boolean, If False no stream output will be displayed.
:return: None
"""
self.keep_running = True
fps = FPS()
fps.start()
while self.footage_socket and self.keep_running:
try:
frame = self.footage_socket.recv_string()
self.current_frame = string_to_image(frame)
if display:
frame = imutils.resize(self.current_frame, width=400)
(h, w) = frame.shape[:2]
blob = cv2.dnn.blobFromImage(cv2.resize(frame, (300, 300)),
0.007843, (300, 300), 127.5)
net.setInput(blob)
detections = net.forward()
detection_string = []
for i in np.arange(0, detections.shape[2]):
confidence = detections[0, 0, i, 2]
if confidence > args["confidence"]:
idx = int(detections[0, 0, i, 1])
box = detections[0, 0, i, 3:7] * np.array(
[w, h, w, h])
(startX, startY, endX, endY) = box.astype("int")
label = "{}: {:.2f}%".format(
CLASSES[idx], confidence * 100)
detection_string.append(label)
cv2.rectangle(frame, (startX, startY),
(endX, endY), COLORS[idx], 2)
y = startY - 15 if startY - 15 > 15 else startY + 15
cv2.putText(frame, label, (startX, y),
cv2.FONT_HERSHEY_SIMPLEX, 0.5,
COLORS[idx], 2)
fps.update()
fps.stop()
text = "FPS: {:.2f}".format(fps.fps())
cv2.putText(frame, text, (10, h - 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
cv2.imshow("Stream", frame)
key = cv2.waitKey(1)
response = ','.join(detection_string)
self.footage_socket.send_string(response)
except:
cv2.destroyAllWindows()
break
print("Streaming Stopped!")
def receive_payload_image_only(self, payload):
frame, id, timestamp = payload.split(self.separator)
if time.time() - float(timestamp) > 0.5:
print("Dropping %s" % id)
return False
id = int(id)
print(id)
self.current_frame = string_to_image(frame)
return True
def receive_stream(self, display=True):
self.keep_running = True
while self.footage_socket and self.keep_running:
print(self.footage_socket)
try:
print('try')
frame = self.footage_socket.recv_string()
print('fname', frame)
self.current_frame = string_to_image(frame)
if display:
cv2.imshow("Stream", self.current_frame)
cv2.waitKey(1)
except KeyboardInterrupt:
cv2.destroyAllWindows()
break
print("Streaming Stopped!")
def receive_stream(self, display=True):
"""
Displays displayed stream in a window if no arguments are passed.
Keeps updating the 'current_frame' attribute with the most recent frame, this can be accessed using 'self.current_frame'
:param display: boolean, If False no stream output will be displayed.
:return: None
"""
self.keep_running = True
while self.footage_socket and self.keep_running:
try:
frame = self.footage_socket.recv_string()
self.current_frame = string_to_image(frame)
with self.frame_deque_lock:
self.frame_deque.append(self.current_frame)
except KeyboardInterrupt:
cv2.destroyAllWindows()
break
print("Streaming Stopped!")
def receive_stream(self, display=True):
"""
Displays displayed stream in a window if no arguments are passed.
Keeps updating the 'current_frame' attribute with the most recent frame, this can be accessed using 'self.current_frame'
:param display: boolean, If False no stream output will be displayed.
:return: None
"""
self.keep_running = True
fps = FPS()
fps.start()
initBB = None
pastBB = None
ite_counter = 0
overall_confidence = None
success = None
tracker = cv2.TrackerMedianFlow_create()
while self.footage_socket and self.keep_running:
try:
client_response = []
f = self.footage_socket.recv_string()
self.current_frame = string_to_image(f)
if display:
frame = self.current_frame
(H, W) = frame.shape[:2]
#matrix = cv2.getRotationMatrix2D((W/2, H/2), 270, 1)
#frame = cv2.warpAffine(frame,matrix,(W,H))
#(H, W) = frame.shape[:2]
if initBB is not None:
(success, box) = tracker.update(frame)
if success:
(x, y, w, h) = [int(v) for v in box]
cv2.rectangle(frame, (x, y), (x + w, y + h),
(255, 0, 0), 2)
center = (x + w / 2, y + h / 2)
try:
offset = (W / 2 - center[0], H / 2 - center[1],
z)
except:
offset = (W / 2 - center[0], H / 2 - center[1])
old_center = (pastBB[0] + pastBB[2] / 2,
pastBB[1] + pastBB[3] / 2)
old_offset = (W / 2 - old_center[0],
H / 2 - old_center[1])
offset_w = 2 * float(W / w)
offset_h = 5 * float(H / h)
if float(W * H) / float(w * h) > 2:
try:
if offset[0] > offset_w: #right
client_response.append('6')
elif offset[0] < -offset_w: #left
client_response.append('4')
except ValueError:
pass
try:
if offset[1] > offset_h: #up
client_response.append("8")
elif offset[1] < -offset_h: #down
client_response.append("2")
except ValueError:
pass
pastBB = (x, y, w, h)
if ite_counter > 20:
ite_counter = 0
try:
target = frame[y:y + h, x:x + w]
blob = cv2.dnn.blobFromImage(
cv2.resize(target, (300, 300)),
0.007843, (300, 300), 127.5)
net.setInput(blob)
detections = net.forward()
for i in np.arange(0, detections.shape[2]):
confidence = detections[0, 0, i, 2]
if confidence > 0.8:
idx = int(detections[0, 0, i, 1])
box = detections[0, 0, i,
3:7] * np.array(
[w, h, w, h])
(startX, startY, endX,
endY) = box.astype("int")
if CLASSES[idx] != TARGET_CLASS:
initBB = None
tracker = cv2.TrackerMedianFlow_create(
)
else:
if (float(
abs(startX - endX) *
abs(startY - endY)) /
float(w * h) < 0.8):
initBB = None
tracker = cv2.TrackerMedianFlow_create(
)
else:
overall_confidence = confidence
else:
initBB = None
tracker = cv2.TrackerMedianFlow_create(
)
except:
initBB = None
tracker = cv2.TrackerMedianFlow_create()
else:
ite_counter += 1
else:
initBB = None
tracker = cv2.TrackerMedianFlow_create()
else:
blob = cv2.dnn.blobFromImage(
cv2.resize(frame, (300, 300)), 0.007843,
(300, 300), 127.5)
net.setInput(blob)
detections = net.forward()
for i in np.arange(0, detections.shape[2]):
confidence = detections[0, 0, i, 2]
if confidence > 0.8:
idx = int(detections[0, 0, i, 1])
box = detections[0, 0, i, 3:7] * np.array(
[W, H, W, H])
(startX, startY, endX,
endY) = box.astype("int")
if CLASSES[idx] != TARGET_CLASS:
overall_confidence = 0
pass
else:
overall_confidence = confidence
initBB = (startX, startY,
abs(endX - startX),
abs(endY - startY))
pastBB = initBB
tracker.init(frame, initBB)
else:
overall_confidence = 0
fps.update()
fps.stop()
info = [("Tracker", "medianflow"),
("Track success", "Yes" if success else "No"),
("FPS", "{:.2f}".format(fps.fps())),
("Object", TARGET_CLASS),
("Confidence", "{:.2f}".format(overall_confidence))
]
for (i, (k, v)) in enumerate(info):
text = "{}: {}".format(k, v)
cv2.putText(frame, text, (10, H - ((i * 20) + 20)),
cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0),
2)
cv2.imshow("Object Tracker: {}".format(TARGET_CLASS),
frame)
key = cv2.waitKey(1)
for i in range(len(client_response), 2):
client_response.append('0')
self.footage_socket.send_string(','.join(client_response))
except KeyboardInterrupt:
cv2.destroyAllWindows()
break
print("Streaming Stopped!")
def receive_camera_payload(self, payload):
frame, id = payload.split(self.separator)
id = int(id)
frame = string_to_image(frame)
self.all_frames[id] = frame
def receive_stream(self, display=True):
from firebase import firebase
"""
Displays displayed stream in a window if no arguments are passed.
Keeps updating the 'current_frame' attribute with the most recent frame, this can be accessed using 'self.current_frame'
:param display: boolean, If False no stream output will be displayed.
:return: None
"""
cap = cv2.VideoCapture(0)
state = [0, 0, 0, 0, 0] # fivefingers, 0 miss, 1 straight, 2 bent
area = [0, 0, 0, 0, 0]
length = [0, 0, 0, 0, 0]
application = firebase.FirebaseApplication('https://iotproj-510ee.firebaseio.com', None)
self.keep_running = True
while self.footage_socket and self.keep_running:
try:
frame = self.footage_socket.recv_string()
self.current_frame = string_to_image(frame)
imgs = []
blank_image = np.zeros((frame.shape[0], frame.shape[1], 3), np.float32)
for i in range(0,3):
img = string_to_image(self.footage_socket.recv_string())
imgs.append(img.astype(np.float32))
for img in imgs:
blank_image = img/3 + blank_image
aveimg = blank_image.astype(np.uint8)
img = aveimg
width = img.shape[1]
height = img.shape[0]
thumb, forefinger, midfinger, ringfinger, pinky, rectarea, length_c, tops = detectfingers(img)
fingers = [thumb, forefinger, midfinger, ringfinger, pinky]
for i, finger in enumerate(fingers):
if finger != 6:
# print(length_c[finger], length[i])
if state[i] == 0:
state[i] = 1
elif state[i] == 2 and length_c[finger] >= 1.2 * length[i]:
# if length_c[finger] >= 1.2 * length[i] or rectarea[finger] >= 1.2 * area[i]:
state[i] = 1
elif state[i] == 1 and length_c[finger] <= 0.86 * length[i]:
# if length_c[finger] <= 0.8 * length[i] or rectarea[finger] >= 1.2 * area[i]:
# print(length_c[finger], length[i])
x = int(tops[finger][0] * 408 / width)
# print(width, tops[finger][0], x)
# print(height)
y = int((height - tops[finger][1]) * 408 / height)
data = {'x': x, 'y': y}
print(data)
globaldata.append(data)
state[i] = 2
length[i] = length_c[finger]
area[i] = rectarea[finger]
else:
state[i] = 0
except KeyboardInterrupt:
cv2.destroyAllWindows()
break
print("Streaming Stopped!")
# Obtem uma referência para a webcam
video_capture = cv2.VideoCapture(0)
# Criando lista de rostos conhecidos
known_face_encodings = []
# Convertendo a lista de string para int
users_directories = list(map(int, users_directories))
# Carregando imagem do cliente e aprendendo reconhecê-lo
for i in range(len(users_directories)):
basedir = 'Images/' + str(users_directories[i])
diretorio = listdir(basedir)
base64img = open(basedir + '/' + diretorio[0]).read()
image = utils.string_to_image(base64img)
cliente_image = face_recognition.load_image_file(image)
known_face_encodings.append(
face_recognition.face_encodings(cliente_image)[0])
users_directories[i] = users_directories[i] + 1
# Initializando variáveis
face_locations = []
face_encodings = []
face_names = []
process_this_frame = True
def gen_frames():
process_this_frame = True
