def Lyrebird():
global Capturing
### Noticed DPI scaling is off at times on Windows
### If it still doesn't work correctly, right click python.exe in Explorer
### and set DPI compatibility as workaround under Compatibility settings
if platform == "win32":
ctypes.windll.shcore.SetProcessDpiAwareness(2)
### Take a screenshot and convert it so opencv can process it
screenshot_temp = ImageGrab.grab()
screenshot = np.array(screenshot_temp)
screenshot = cv2.cvtColor(screenshot, cv2.COLOR_BGR2RGB)
### create a new fullscreen window and load the screenshot
WindowName = "Lyrebird"
cv2.namedWindow(WindowName, cv2.WINDOW_NORMAL)
cv2.setWindowProperty(WindowName, cv2.WND_PROP_FULLSCREEN,
cv2.WINDOW_FULLSCREEN)
cv2.imshow(WindowName, screenshot)
Capturing = True
### Goal is to take a picture when mouse or keyboard is clicked
cv2.setMouseCallback(WindowName, mouseAction)
while Capturing:
if (cv2.waitKey(1) & 0xFF) == 27:
webcamCapture()
break
cv2.destroyAllWindows()
def set_unset_full_screen(self):
if not self.fullScreen:
cv2.setWindowProperty(self.presentationName,
cv2.WND_PROP_FULLSCREEN,
cv2.WINDOW_FULLSCREEN)
self.fullScreen = True
else:
cv2.setWindowProperty(self.presentationName,
cv2.WND_PROP_FULLSCREEN, cv2.WINDOW_NORMAL)
self.fullScreen = False
except Full: # when the queue is full, ignore it
continue # next loop
if __name__ == "__main__":
""" mirror the image of the Viewport onto the Porthole """
try:
# opencv-python
import cv2.cv2 as cv
except ImportError:
import cv2 as cv
# create the Viewport window
viewport = 'Viewport'
cv.namedWindow(viewport, cv.WINDOW_GUI_NORMAL)
cv.setWindowProperty(viewport, cv.WND_PROP_AUTOSIZE, cv.WINDOW_NORMAL)
cv.setWindowProperty(viewport, cv.WND_PROP_ASPECT_RATIO,
cv.WINDOW_FREERATIO)
set_start_method('spawn') # windows default
img_queue = Queue(maxsize=3) # always up to date with a buffer of 3 frames
# shared memory array with 4 places (left, upper, right, lower)
monitor = Array('i', (1, 1, 2, 2))
# start making screenshots
screen_capture = ScreenCapture(img_queue, monitor)
screen_capture.start()
# use mss to get the width and height of the monitor(s)
mon: dict = mss.mss().monitors[0]
lineType=8,
shift=0)
contador_circulo_click -= 1
#Este for es para mostrar los nombres, lo reemplazo con los cuadritos.
cv2.imshow('Video', frame)
#Funcion para guardar la cara con un click
cv2.setMouseCallback('Video', save_face_click)
cv2.imshow('Video', frame)
#estas 2 lineas son para sacar la barra de la imagen
cv2.namedWindow('Video', cv2.WINDOW_NORMAL)
cv2.setWindowProperty('Video', cv2.WND_PROP_FULLSCREEN,
cv2.WINDOW_FULLSCREEN)
cv2.moveWindow('Video', -35, -20)
datos_ventana_video = cv2.getWindowImageRect(
'Video') #This will return a tuple of (x, y, w, h)
#Esto es la grilla con fondo negro y datos
cv2.imshow('grilla', grilla)
#estas 2 lineas son para sacar la barra de la imagen
cv2.namedWindow('grilla', cv2.WINDOW_NORMAL)
cv2.setWindowProperty('grilla', cv2.WND_PROP_FULLSCREEN,
cv2.WINDOW_FULLSCREEN)
#linea para mover la ventana y acomodarlas en la pantalla
cv2.moveWindow('grilla', datos_ventana_video[0] + datos_ventana_video[2],
def main():
button_pin = 7
GPIO.setmode(GPIO.BCM)
# load our serialized model from disk
print("[INFO] loading model...")
net = cv2.dnn.readNetFromCaffe(prototxt, model)
# initialize the video stream, allow the cammera sensor to warmup,
# and initialize the FPS counter
print("[INFO] starting video stream...")
vs = VideoStream(src=0).start()
fps = FPS().start()
ultrasonic = UltrasonicSystem({1: [8, 25], 2: [24, 23], 3: [15, 14]}, 3)
ultrasonic.add_sensors()
ultrasonic_spawn = threading.Thread(target=ultrasonic.spawn_sensor_threads,
daemon=True)
ultrasonic_spawn.start()
GPIO.add_event_detect(button_pin, GPIO.RISING)
try:
# loop over the frames from the video stream
while True:
# grab the frame from the threaded video stream and resize it
# to have a maximum width of 500 pixels
frame = vs.read()
frame = cv2.flip(frame, 1)
# grab the frame dimensions and convert it to a blob
(h, w) = frame.shape[:2]
blob = cv2.dnn.blobFromImage(cv2.resize(frame, (300, 300)),
0.007843, (300, 300), 127.5)
# pass the blob through the network and obtain the detections and
# predictions
net.setInput(blob)
detections = net.forward()
# loop over the detections
for i in np.arange(0, detections.shape[2]):
# extract the confidence (i.e., probability) associated with
# the prediction
confidence = detections[0, 0, i, 2]
# filter out weak detections by ensuring the `confidence` is
# greater than the minimum confidence
if confidence > confidence_threshold:
# extract the index of the class label from the
# `detections`, then compute the (x, y)-coordinates of
# the bounding box for the object
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")
# draw the prediction on the frame
label = "{}: {:.2f}%".format(CLASSES[idx],
confidence * 100)
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)
frame = ultrasonic.write_measurements_to_frame(frame)
# show the output frame
cv2.namedWindow("Frame", cv2.WND_PROP_FULLSCREEN)
cv2.setWindowProperty("Frame", cv2.WND_PROP_FULLSCREEN,
cv2.WINDOW_FULLSCREEN)
# cv2.resizeWindow("Frame", 640, 480)
cv2.imshow("Frame", frame)
key = cv2.waitKey(1) & 0xFF
if GPIO.event_detected(button_pin):
ultrasonic.stop = True
time.sleep(1)
break
# update the FPS counter
fps.update()
cleanup(fps, vs)
except KeyboardInterrupt:
cleanup(fps, vs)