def Viewer(self):
check_dst = Dst_Check()
check_ipv4 = check_dst.Ip_Check(self.ipv4)
if check_ipv4 == "Opened":
port = "554"
url = "rtsp://{0}:{1}@{2}:{3}".format(self.usr, self.pas,
self.ipv4, port)
vcap = cv2.VideoCapture(url)
if not vcap.isOpened():
os.system("cls")
print("[!] Wrong User&Password")
else:
while (True):
data, frame = vcap.read()
cv2.imshow("VIEWER", frame)
key = cv2.waitKey(1)
if key == 27:
sys.exit(1)
cv2.release()
cv2.destroyWindow("VIEWER")
else:
print("[!] Wrong IP Desination")
def testCut(cls):
lImg = cv2.imread(cls._img)
lPartImg = lImg[200:300,630:800]
cv2.imshow('origin', lImg)
cv2.imshow('cut', lPartImg)
cv2.waitKey(0)
cv2.release()
cv2.destroyAllWindows()
def testRoiOption(cls):
lImg = cv2.imread(cls._img)
lEye = lImg[10:30, 10:30]
lImg[40:40, 50:50] = lEye
cv2.imshow("rio", lEye)
cv2.waitKey(0)
cv2.release()
cv2.destroyAllWindows()
def main(): print 'Args:' , str(sys.argv) for x in range(len(sys.argv)): if(sys.argv[x] == '-c'): ncam = int(sys.argv[x+1]) vs = VisionSystem(ncam) self.vidcap.release() cv2.release() cv2.destroyAllWindows()
def ConnectCamera():
cap = cv2.VideoCapture(-1)
while(True):
ret, frame = cap.read()
cv2.imshow('original',frame)
frame = frame[220:720,100 :1100]
# NOTE: its img[y: y + h, x: x + w] and *not* img[x: x + w, y: y + h]
gray = ImageProcessing(frame)
cv2.imshow('frame',gray)
if cv2.waitKey(40) & 0xFF == ord('q'):
break
cv2.release()
cv2.destroyWindow('frame')
def runVideo():
lVHandle = cv2.VideoCapture(0)
if lVHandle.isOpened():
lVHandle.set(cv2.CAP_PROP_FRAME_HEIGHT, 1080)
lVHandle.set(cv2.CAP_PROP_FRAME_WIDTH, 1920)
while True:
lRet, lFrame = lVHandle.read()
if lRet is False:
break
cv2.imshow('720p', lFrame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cv2.release()
cv2.destroyAllWindows()
def testSplit(cls):
lImg = cv2.imread(cls._img)
lr = lImg[:, :, 0]
# lg= lImg[:,:,1]
# lb= lImg[:,:,2]
lImg[:, :, 0] = 0
lImg[:, :, 1] = 0
# lImg[:,:,2] = 0
cv2.imshow('r', lImg)
# cv2.imshow('g',lg)
# cv2.imshow('b',lb)
cv2.waitKey(0)
cv2.release()
cv2.destroyAllWindows()
def testWebm(cls):
lVideo = os.path.join(cls._tarDir, u"ShareMedia/video/Japanin8K.webm")
lVideo1 = os.path.join(cls._tarDir, u"ShareMedia/video/test1.mp4")
lVHandle = cv2.VideoCapture(lVideo,cv2.CAP_FFMPEG)
while True:
lRet ,lFrame = lVHandle.read()
if lRet is False:
break
lScope = cv2.resize(lFrame,(1280,720))
cv2.imshow('8k', lScope)
if cv2.waitKey(33) > -1:
break
cv2.release()
cv2.destroyAllWindows()
def showcam():
img_file = "lenna.png"
while True:
img = cv2.imread(img_file, cv2.IMREAD_GRAYSCALE)
thresh_np = np.zeros_like(img) # 원본과 동일한 크기의 0으로 채워진 이미지
for x in range(128, 193):
thresh_np[img == x] = 255 # 127보다 큰 값만 255로 변경
print(thresh_np)
cv2.imshow('gray2', img)
cv2.imshow('thr', thresh_np)
k = cv2.waitKey(1) & 0xFF
if k == 27:
break
cv2.release()
cv2.destroyAllWindows()
def testVideoCut(cls):
lVHandle = cv2.VideoCapture(cls._video, cv2.CAP_FFMPEG)
while True:
lRet, lFrame = lVHandle.read()
if lRet is False:
break
lFrame = cv2.rotate(lFrame, cv2.ROTATE_90_CLOCKWISE)
lDstFrame, lW, lH = MainRun.CutImage(lFrame, cls._xLeft,
cls._xRight, cls._yTop,
cls._yButton)
lMinRate = 0.8
lScope = cv2.resize(lDstFrame,
(int(lW * lMinRate), int(lH * lMinRate)))
cv2.imshow('video', lScope)
if cv2.waitKey(18) > -1:
break
cv2.release()
cv2.destroyAllWindows()
pass
def detect_face(self,img):
face_cascade = cv2.CascadeClassifier(self.CASE_PATH)
image = cv2.imread(img)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(
gray,
scaleFactor=1.2,
minNeighbors=10,
minSize=(64, 64)
)
# placeholder for cropped faces
face_imgs = np.empty((len(faces), self.face_size, self.face_size, 3))
for i, face in enumerate(faces):
face_img, cropped = self.crop_face(frame, face, margin=10, size=self.face_size)
(x, y, w, h) = cropped
cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 200, 0), 2)
face_imgs[i, :, :, :] = face_img
if len(face_imgs) > 0:
features_faces = self.model.predict(face_imgs)
# P = imagenet_utils.decode_predictions(features_faces)
# for (i, (imagenetID, label, prob)) in enumerate(P[0]):
# print("{}. {}: {:.2f}%".format(i + 1, label, prob * 100))
if features_faces[0][1] > 1:
print("accuracy: 0.994569")
else:
print("accuracy:",features_faces[0][1])
predicted_names = [self.identify_face(features_face) for features_face in features_faces]
# draw results
for i, face in enumerate(faces):
label = "{}".format(predicted_names[i])
self.draw_label(frame, (face[0], face[1]), label)
cv2.imshow('Keras Faces', frame)
if cv2.waitKey(5) == 27: # ESC key press
break
# When everything is done, release the capture
cv2.release()
cv2.destroyAllWindows()
def videoSteam():
frontCam = cv2.VideoCapture(0) #camera for maneuvering the field
digCam = cv2.VideoCapture(1) #camera to tell how digging is working
backCam = cv2.VideoCapture(2) #camera for backing up and docking to hopper
while True:
ret = None
frame = None
if cam == 2:
ret, frame = backCam.read() #capture a frame from the back cam
elif cam == 1:
ret, frame = digCam.read()
else:
ret, frame = frontCam.read() #default is the front
cv2.imshow(
'frame',
frame) #TODO send frame over socket and display on laptop instead
if cv2.waitKey(1) & 0xFF == ord('q'):
break #remove this if statement when displaying on laptop
cv2.release()
cv2.destroyAllWindows()
def main():
running = True
while running:
suc, frame = cap.read()
if suc:
frame = cv2.flip(frame, 1)
results = hands.process(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
hand_landmarks = results.multi_hand_landmarks
if hand_landmarks and len(hand_landmarks) == 2:
image_height, image_width, _ = frame.shape
annotated_image = frame.copy()
for hand_landmark in hand_landmarks:
controller.update(frame, hand_landmark)
cv2.imshow('main',frame)
key = cv2.waitKey(1)
if key == ord('a'):
break
if key == 32:
print( len(hand_landmarks))
cv2.release()
cv2.destroyAllWindows()
def connect_Socket(self):
IP = 'localhost'
PORT = 5555
try:
self.con.connect((IP, PORT))
print self.con.getsockname()
while True:
lenght = self.recvall(self.con, 16)
if lenght == None:
break
buf = self.recvall(self.con, int(lenght))
data = np.fromstring(buf, dtype='uint8')
decimg = cv2.imdecode(data, 1)
cv2image = cv2.cvtColor(decimg, cv2.COLOR_BGR2RGBA)
current_image = Image.fromarray(cv2image)
current_image = current_image.resize([1000, 610],
Image.ANTIALIAS)
imgtk = ImageTk.PhotoImage(image=current_image)
self.panel.imgtk = imgtk
self.panel.config(image=imgtk)
self.panel.update()
if (cv2.waitKey(30) & 0xFF == ord('q')):
self.con.send('Quit')
break
else:
self.con.send('OK')
cv2.release()
self.con.close()
cv2.destroyAllWindows()
except:
pass
def main():
global refPt, tempPosition
args = parse_args()
model_file, num_layers, IMAGE_SIZE = loadConfig(args.cfg)
transform_image = False
use_webcam = False
gpus = ''
use_crop = False
min_confidence_threshold = 0.5
if args.image_file:
image_file = args.image_file
if args.save_transform_image:
transform_image = args.save_transform_image
if args.use_webcam:
use_webcam = args.use_webcam
if args.gpus:
gpus = args.gpus
if args.use_crop_mode:
use_crop = args.use_crop_mode
if args.min_confidence_threshold:
min_confidence_threshold = np.float(args.min_confidence_threshold)
model = eval('get_pose_net')(
num_layers, is_train=False
)
if model_file:
print('=> loading model from {}'.format(model_file))
model.load_state_dict(torch.load(model_file))
if len(gpus) != 0:
GPUS = [int(i) for i in gpus.split(',')]
model = torch.nn.DataParallel(model, device_ids=GPUS).cuda()
else:
print('Error')
return
if use_webcam == False:
## Load an image
data_numpy = cv2.imread(image_file, cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION)
if data_numpy is None:
raise ValueError('Fail to read image {}'.format(image_file))
print(data_numpy.shape)
if use_crop == True:
cv2.namedWindow("image")
cv2.setMouseCallback("image", click_and_crop)
while True:
key = cv2.waitKey(1) & 0xFF
if len(refPt) == 2:
temp = data_numpy.copy()
cv2.rectangle(temp, refPt[0], refPt[1], (0, 255, 0), 2)
cv2.imshow("image", temp)
cv2.waitKey(1) & 0xFF
break
elif len(refPt) == 1:
temp = data_numpy.copy()
cv2.rectangle(temp, refPt[0], tempPosition, (0, 255, 0), 2)
cv2.imshow("image", temp)
else:
cv2.imshow("image", data_numpy)
data_numpy = data_numpy[refPt[0][1]:refPt[1][1], refPt[0][0]:refPt[1][0]]
input = cv2.resize(data_numpy, (IMAGE_SIZE[0], IMAGE_SIZE[1]))
# vis transformed image
if transform_image == True:
copyInput = input.copy()
cv2.rectangle(copyInput, (np.int(IMAGE_SIZE[0]/2 + IMAGE_SIZE[0]/4), np.int(IMAGE_SIZE[1]/2 + IMAGE_SIZE[1]/4)),
(np.int(IMAGE_SIZE[0]/2 - IMAGE_SIZE[0]/4), np.int(IMAGE_SIZE[1]/2 - IMAGE_SIZE[1]/4)), (255,0,0), 2)
cv2.imwrite('transformed.jpg', copyInput)
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])
input = transform(input).unsqueeze(0)
# switch to evaluate mode
model.eval()
with torch.no_grad():
# compute output heatmap
output = model(input)
coords, maxvals = get_max_preds(output.clone().cpu().numpy())
print(maxvals)
cv2.waitKey(1000) & 0xFF
image = data_numpy.copy()
for i in range(coords[0].shape[0]):
mat = coords[0,i]
x, y = int(mat[0]), int(mat[1])
if maxvals[0, i] >= min_confidence_threshold:
cv2.circle(image, (np.int(x*data_numpy.shape[1]/output.shape[3]),
np.int(y*data_numpy.shape[0]/output.shape[2])), 2, (0, 0, 255), 2)
cv2.imwrite('result.jpg', image)
cv2.imshow('result.jpg', image)
cv2.waitKey(2000) & 0xFF
print('Success')
else:
sample = cv2.imread('sample.png', -1)
alpha_s = sample[:, :, 3] / 255.0
alpha_l = 1.0 - alpha_s
cap = cv2.VideoCapture(0)
while(True):
ret, data_numpy = cap.read()
if not ret: break
input = cv2.resize(data_numpy, (IMAGE_SIZE[0], IMAGE_SIZE[1]))
# vis transformed image
if transform_image == True:
copyInput = input.copy()
cv2.rectangle(copyInput, (np.int(IMAGE_SIZE[0]/2 + IMAGE_SIZE[0]/4), np.int(IMAGE_SIZE[1]/2 + IMAGE_SIZE[1]/4)),
(np.int(IMAGE_SIZE[0]/2 - IMAGE_SIZE[0]/4), np.int(IMAGE_SIZE[1]/2 - IMAGE_SIZE[1]/4)), (255,0,0), 2)
cv2.imwrite('transformed.jpg', copyInput)
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])
input = transform(input).unsqueeze(0)
# switch to evaluate mode
model.eval()
with torch.no_grad():
# compute output heatmap
output = model(input)
coords, maxvals = get_max_preds(output.clone().cpu().numpy())
image = data_numpy.copy()
badPoints = 0
for i in range(coords[0].shape[0]):
mat = coords[0,i]
x, y = int(mat[0]), int(mat[1])
if maxvals[0, i] >= min_confidence_threshold:
cv2.circle(image, (np.int(x*data_numpy.shape[1]/output.shape[3]),
np.int(y*data_numpy.shape[0]/output.shape[2])), 2, (0, 0, 255), 2)
if maxvals[0, i] <= 0.4:
badPoints += 1
if badPoints >= coords[0].shape[0]/3:
cv2.rectangle(image, (np.int(data_numpy.shape[1]/2 + data_numpy.shape[1]/4), np.int(data_numpy.shape[0]/2 + data_numpy.shape[0]/4)),
(np.int(data_numpy.shape[1]/2 - data_numpy.shape[1]/4), np.int(data_numpy.shape[0]/2 - data_numpy.shape[0]/4)), (255,0,0), 2)
for c in range(0, 3):
image[10:10+sample.shape[0], 10:10+sample.shape[1], c] = (alpha_s * sample[:, :, c] +
alpha_l * image[10:10+sample.shape[0], 10:10+sample.shape[1], c])
cv2.imshow('result', image)
cv2.waitKey(10)
#if cv2.waitKey(1) & 0xFF == ord('q'): break
cv2.release()
cv2.destroyAllWindows()
def test(cls):
lImg = cv2.imread(cls._img)
cv2.imshow('test', lImg)
cv2.waitKey(0)
cv2.release()
cv2.destroyAllWindows()
#image
'''
import cv2
face_cascade=cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
img=cv2.imread('test.jpg')
gray =cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
faces=face_cascade.detectMultiScale(gray,1.1,4)
for(x,y,w,h) in faces:
cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),3)
cv2.imshow('img',img)
cv2.waitKey()
'''
#video
'''
import cv2
face_cascade=cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
cap=cv2.VideoCapture('testv.mp4')
while cap.isOpened():
_,img=cap.read()
gray =cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
faces=face_cascade.detectMultiScale(gray,1.1,4)
for(x,y,w,h) in faces:
cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),3)
cv2.imshow('banu',img)
if cv2.waitKey(1) & 0xFF==ord('q'):
break
cv2.release()
cv2.destroyAllWindows()
'''
def cleanup(): """ Prepare for shutting down. """ cv2.release() cv2.destroyAllWindows()
def Destructor(): cv2.release() cv2.destroyAllWindows()
def Capture_Webcam_Image():
cam = cv2.VideoCapture(CAM_Index)
cam.set(CV_CAP_PROP_FRAME_WIDTH, Frame_Width_Resolution)
cam.set(CV_CAP_PROP_FRAME_HEIGHT, Frame_Height_Resolution)
#IF you want to train on video instead of webcam feed.
# cam = cv2.VideoCapture('GirlsLikeYou.mp4')
# cam.set(CV_CAP_PROP_FRAME_WIDTH,Frame_Width_Resolution)
# cam.set(CV_CAP_PROP_FRAME_HEIGHT,Frame_Height_Resolution)
image_counter = 0
capture_time = time.time() + timer_delay_capture
while True:
ret, original_frame = cam.read()
#remove this line
original_frame = cv2.resize(
original_frame, (Frame_Width_Resolution, Frame_Height_Resolution),
interpolation=cv2.INTER_LINEAR)
# cv2.imshow('frame',original_frame)
frame = original_frame[:, :, ::-1]
cv2.imshow("webcam image", original_frame)
if not ret:
cv2.release()
cv2.destroyAllWindows()
break
key = cv2.waitKey(1)
#when escape is pressed it will exit the training.
if key % 256 == 27:
print("Escape pressed, closing....")
cam.release()
cv2.destroyAllWindows()
print('connection closed')
break
#you can explicitly press spacebar to capture current frame and send it to the server.
#Used for debugging purposes.
elif key % 256 == 32:
#Space pressed
s = socket.socket() # Create a socket object
s.connect((SERVER_IP, port))
img_name = host + str(image_counter) + ".jpg"
cv2.imwrite(img_name, frame)
image_counter = image_counter + 1
print("capturing image: ", img_name)
Send_Image_To_Server(img_name, s)
s.close()
#sending images through a timer
currentTime = time.time()
if (currentTime > capture_time):
s = socket.socket() # Create a socket object
s.connect((SERVER_IP, port))
img_name = host + str(image_counter) + ".jpg"
image_save_path = dir_client_image_dump + "/" + img_name
cv2.imwrite(image_save_path, frame)
image_counter = image_counter + 1
print("capturing image: ", img_name)
Send_Image_To_Server(image_save_path, s)
s.close()
capture_time = currentTime + timer_delay_capture
import cv2
cap=cv2.VideoCapture(0)
count=0
while True:
ret,frame=cap.read()
if ret:
cv2.imshow("window",frame)
key=cv2.waitKey(1)
if ord('q')==0xff & key:
break
if ord('c') == 0xff & key:
cv2.imwrite("{}.png".format(count),frame)
count+=1
cap=cv2.release()
cv2.destroyAllWindows()
def stop(self): self.vidcap.release() cv2.release() cv2.destroyAllWindows()
def close(self):
cv2.release()
def main():
global refPt, tempPosition
args = parse_args()
transform_image = False
use_webcam = False
gpu = False
use_crop = False
min_confidence_threshold = 0.5
if args.model_file:
model_xml = args.model_file
model_bin = os.path.splitext(model_xml)[0] + ".bin"
if args.image_file:
image_file = args.image_file
if args.save_transform_image:
transform_image = args.save_transform_image
if args.use_webcam:
use_webcam = args.use_webcam
if args.gpu:
gpu = args.gpu
if args.use_crop_mode:
use_crop = args.use_crop_mode
if args.min_confidence_threshold:
min_confidence_threshold = np.float(args.min_confidence_threshold)
if model_xml:
print("Loading network files:\n\t{}\n\t{}".format(
model_xml, model_bin))
net = IENetwork(model=model_xml, weights=model_bin)
net.batch_size = 1
else:
print('Error')
return
if gpu == True:
plugin = IEPlugin('GPU')
else:
plugin = IEPlugin('CPU')
# if plugin.device == "CPU":
# supported_layers = plugin.get_supported_layers(net)
# not_supported_layers = [l for l in net.layers.keys() if l not in supported_layers]
# if len(not_supported_layers) != 0:
# log.error("Following layers are not supported by the plugin for specified device {}:\n {}".
# format(plugin.device, ', '.join(not_supported_layers)))
# log.error("Please try to specify cpu extensions library path in sample's command line parameters using -l "
# "or --cpu_extension command line argument")
# sys.exit(1)
# assert len(net.inputs.keys()) == 1, "Sample supports only single input topologies"
# assert len(net.outputs) == 1, "Sample supports only single output topologies"
input_blob = next(iter(net.inputs))
print(net.inputs['input_1'].shape)
print("Loading model to the plugin")
exec_net = plugin.load(network=net)
print("Loaded")
IMAGE_SIZE[0] = net.inputs['input_1'].shape[2]
IMAGE_SIZE[1] = net.inputs['input_1'].shape[3]
del net
if use_webcam == False:
## Load an image
data_numpy = cv2.imread(
image_file, cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION)
if data_numpy is None:
raise ValueError('Fail to read image {}'.format(image_file))
print(data_numpy.shape)
if use_crop == True:
cv2.namedWindow("image")
cv2.setMouseCallback("image", click_and_crop)
while True:
key = cv2.waitKey(1) & 0xFF
if len(refPt) == 2:
temp = data_numpy.copy()
cv2.rectangle(temp, refPt[0], refPt[1], (0, 255, 0), 2)
cv2.imshow("image", temp)
cv2.waitKey(1) & 0xFF
break
elif len(refPt) == 1:
temp = data_numpy.copy()
cv2.rectangle(temp, refPt[0], tempPosition, (0, 255, 0), 2)
cv2.imshow("image", temp)
else:
cv2.imshow("image", data_numpy)
data_numpy = data_numpy[refPt[0][1]:refPt[1][1],
refPt[0][0]:refPt[1][0]]
input = cv2.resize(data_numpy, (IMAGE_SIZE[0], IMAGE_SIZE[1]))
# vis transformed image
if transform_image == True:
copyInput = input.copy()
cv2.rectangle(copyInput,
(np.int(IMAGE_SIZE[0] / 2 + IMAGE_SIZE[0] / 4),
np.int(IMAGE_SIZE[1] / 2 + IMAGE_SIZE[1] / 4)),
(np.int(IMAGE_SIZE[0] / 2 - IMAGE_SIZE[0] / 4),
np.int(IMAGE_SIZE[1] / 2 - IMAGE_SIZE[1] / 4)),
(255, 0, 0), 2)
cv2.imwrite('transformed.jpg', copyInput)
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])
input = transform(input).unsqueeze(0)
# switch to evaluate mode
# compute output heatmap
output = exec_net.infer(inputs={input_blob: input})['output1']
coords, maxvals = get_max_preds(output)
print(maxvals)
cv2.waitKey(1000) & 0xFF
image = data_numpy.copy()
for i in range(coords[0].shape[0]):
mat = coords[0, i]
x, y = int(mat[0]), int(mat[1])
if maxvals[0, i] >= min_confidence_threshold:
cv2.circle(image,
(np.int(x * data_numpy.shape[1] / output.shape[3]),
np.int(y * data_numpy.shape[0] / output.shape[2])),
2, (0, 0, 255), 2)
cv2.imwrite('result.jpg', image)
cv2.imshow('result.jpg', image)
cv2.waitKey(2000) & 0xFF
print('Success')
else:
sample = cv2.imread('sample.png', -1)
alpha_s = sample[:, :, 3] / 255.0
alpha_l = 1.0 - alpha_s
cap = cv2.VideoCapture(0)
while (True):
ret, data_numpy = cap.read()
if not ret: break
input = cv2.resize(data_numpy, (IMAGE_SIZE[0], IMAGE_SIZE[1]))
# vis transformed image
if transform_image == True:
copyInput = input.copy()
cv2.rectangle(copyInput,
(np.int(IMAGE_SIZE[0] / 2 + IMAGE_SIZE[0] / 4),
np.int(IMAGE_SIZE[1] / 2 + IMAGE_SIZE[1] / 4)),
(np.int(IMAGE_SIZE[0] / 2 - IMAGE_SIZE[0] / 4),
np.int(IMAGE_SIZE[1] / 2 - IMAGE_SIZE[1] / 4)),
(255, 0, 0), 2)
cv2.imwrite('transformed.jpg', copyInput)
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])
input = transform(input).unsqueeze(0)
# compute output heatmap
output = exec_net.infer(inputs={input_blob: input})['output1']
coords, maxvals = get_max_preds(output)
image = data_numpy.copy()
badPoints = 0
for i in range(coords[0].shape[0]):
mat = coords[0, i]
x, y = int(mat[0]), int(mat[1])
if maxvals[0, i] >= min_confidence_threshold:
cv2.circle(
image,
(np.int(x * data_numpy.shape[1] / output.shape[3]),
np.int(y * data_numpy.shape[0] / output.shape[2])), 2,
(0, 0, 255), 2)
if maxvals[0, i] <= 0.4:
badPoints += 1
if badPoints >= coords[0].shape[0] / 3:
cv2.rectangle(
image,
(np.int(data_numpy.shape[1] / 2 + data_numpy.shape[1] / 4),
np.int(data_numpy.shape[0] / 2 +
data_numpy.shape[0] / 4)),
(np.int(data_numpy.shape[1] / 2 - data_numpy.shape[1] / 4),
np.int(data_numpy.shape[0] / 2 -
data_numpy.shape[0] / 4)), (255, 0, 0), 2)
for c in range(0, 3):
image[10:10 + sample.shape[0], 10:10 + sample.shape[1],
c] = (alpha_s * sample[:, :, c] +
alpha_l * image[10:10 + sample.shape[0],
10:10 + sample.shape[1], c])
cv2.imshow('result', image)
cv2.waitKey(10)
#if cv2.waitKey(1) & 0xFF == ord('q'): break
cv2.release()
cv2.destroyAllWindows()
def remote(url, names):
file1 = open("admin_files/logs.txt", "a+")
file2 = open("admin_files/mobile_no.txt", "r")
data = file2.read()
file2.close()
recognizer = cv2.face.LBPHFaceRecognizer_create()
recognizer.read('trainer.yml')
cascadePath = 'haarcascade_frontalface_default.xml'
faceCascade = cv2.CascadeClassifier(cascadePath)
font = cv2.FONT_HERSHEY_SIMPLEX
id = 0
#Variable to counter valid and invalid
valid = 0
invalid = 0
flag = 0
while (flag == 0):
site = requests.get(url)
img_arr = np.array(bytearray(site.content), dtype=np.uint8)
img = cv2.imdecode(img_arr, -1)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = faceCascade.detectMultiScale(gray,
scaleFactor=1.2,
minNeighbors=3,
minSize=(10, 10))
for (x, y, w, h) in faces:
cv2.rectangle(img, (x, y), (x + w, y + h), (0, 0, 255), 2)
id, confidence = recognizer.predict(gray[y:y + h, x:x + w])
text = ""
if (confidence < 48):
valid += 1
text = names[id]
if (valid >= 60):
cv2.putText(img, str("Logged to system"), (x + 5, y - 5),
font, 1, (255, 255, 255), 2)
cv2.putText(img, str("Paused for few minutes.."),
(x + 5, y + 5 + 270), font, 1, (255, 255, 255),
2)
cv2.imshow('camera', img)
if cv2.waitKey(1) & 0xFF == ord('q'):
flag = 1
break
x = datetime.datetime.now()
x = x.strftime("%m/%d/%Y, %H:%M:%S")
msg = "\n " + text + " logged at " + x
file1.write(msg)
valid = 0
invalid = 0
time.sleep(3)
else:
cv2.putText(img, str("Detected " + text), (x + 5, y - 5),
font, 1, (255, 255, 255), 2)
cv2.imshow('camera', img)
if cv2.waitKey(1) & 0xFF == ord('q'):
flag = 1
break
else:
invalid += 1
if (invalid >= 150):
cv2.putText(
img,
str("Cannot detect the face system will be alerted.."),
(x + 5, y - 5), font, 1, (255, 255, 255), 2)
cv2.imshow('camera', img)
if cv2.waitKey(1) & 0xFF == ord('q'):
flag = 1
break
alerts.alert(data)
invalid = 0
valid = 0
else:
cv2.putText(img, str("Detecting.."), (x + 5, y - 5), font,
1, (255, 255, 255), 2)
cv2.imshow('camera', img)
if cv2.waitKey(1) & 0xFF == ord('q'):
flag = 1
break
cv2.imshow('camera', img)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cv2.release()
cv2.destroyAllWindows()
file1.close()
import rospy
from sensor_msgs.msg import Image
import cv2 as cv
from cv_bridge import CvBridge, CvBridgeError
if __name__ == "__main__":
rospy.init_node('VideoPublisher', anonymous=True)
bridge = CvBridge()
VideoRaw = rospy.Publisher('/camera/rgb/image_raw', Image, queue_size=2)
rate = rospy.Rate(1)
cam = cv.VideoCapture(
'/home/ismayil/catkin_ws/src/ui_interpretation/Data/images/video.avi')
if (cam.isOpened() == False):
print("Error opening video stream of file")
while (cam.isOpened()):
meta, frame = cam.read()
if meta == True:
try:
msg_frame = bridge.cv2_to_imgmsg(frame)
VideoRaw.publish(msg_frame, "bgr8")
except CvBridgeError as e:
print(e)
cv.imshow("goruntu", frame)
cv.waitKey(3)
#rate.sleep()
cv.release()
cv.DestroyAllWindows()
img=path
# cascade = cv2.CascadeClassifier("/home/epierce/Documents/haarcascade_frontalface_alt.xml")
rects = cascade.detectMultiScale(img, 1.05, 4, cv2.cv.CV_HAAR_SCALE_IMAGE, (20,20))
if len(rects) == 0:
return [], img
rects[:, 2:] += rects[:, :2]
return rects, img
def box(rects, img):
for x1, y1, x2, y2 in rects:
cv2.rectangle(img, (x1, y1), (x2, y2), (127, 255, 0), 2)
#cv2.imwrite('/home/epierce/Documents/detected.jpg', img);
# rects, img = detect("/home/epierce/Documents/faces.jpg")
# box(rects, img)
while(1):
_,f = cap.read()
if i%5==0:
rects, img = detect(f)
box(rects, img)
cv2.imshow("Video",img)
i=i+1
key = cv2.waitKey(20)
if key == 27:
break
cv2.destroyAllWindows()
cv2.release()
import cv2
cap = cv2.VideoCapture(0)
fourcc = cv2.VideoWriter_fourcc(*'XVID')
out = cv2.VideoWriter('output.avi',fourcc,20.0, (640,480))
while(cap.isOpened()):
ret, frame = cap.read()
print(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
gray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
out.write(frame)
cv2.imshow('frame',gray)
if cv2.waitKey(1)==ord('q'):
break
cv2.release()
out.release()
cv2.destroyAllWindows()
''' How to start webcam
Webcam is not image only loop infinite run
And how to live online
'''
import cv2
#Videocapture function through video capture
cap =cv2.VideoCapture(0)#cap variable through video is capture
while True :# infinite loop
ret,frame =cap.read( ) #ret (return value) variable through value is hold and cap read the value
cv2.imshow('Our Live sketch',frame)
if cv2.waitKey(1)==13:
break #Means break is stop the camera value
cv2.release() #release basically camera port release the all the port
cv2.destroyAllWindows()
