cv2.namedWindow("Left")
cv2.namedWindow("Right")
img_counter = 0
while True:
frame_left = stream_left.read()
frame_right = stream_right.read()
cv2.imshow("Left", frame_left)
cv2.imshow("Right", frame_left)
k = cv2.waitKey(1)
if k % 256 == 27:
# ESC pressed
print("Escape hit, closing...")
break
elif k % 256 == 32:
# SPACE pressed
left_path = "left/chessplate_calibration_{}.png".format(img_counter)
right_path = "right/chessplate_calibration_{}.png".format(img_counter)
start_thread(frame_left, left_path)
start_thread(frame_right, right_path)
print("{} and {} written!".format(left_path, right_path))
img_counter += 1
stream_left.release()
stream_right.release()
cv2.destroyAllWindows()
while self.started:
(grabbed, frame) = self.stream.read()
self.read_lock.acquire()
self.grabbed, self.frame = grabbed, frame
self.read_lock.release()
def stop(self):
self.started = False
self.thread.join()
def __exit__(self, exc_type, exc_value, traceback):
self.stream.release()
if __name__ == "__main__":
vs = WebcamVideoStream().start()
while True:
frame = vs.read()
results = model.detect([frame], verbose=0)
r = results[0]
frame = display_instances(frame, r['rois'], r['masks'], r['class_ids'],
class_names, r['scores'])
cv2.imshow('webcam', frame)
if cv2.waitKey(1) == 27:
break
vs.release()
cv2.destroyAllWindows()
if self.num_frames < 30:
if self.background is None:
self.background = gray.copy().astype("float")
cv2.accumulateWeighted(gray, self.background, 0.5)
self.num_frames += 1
else:
self.calibrated = True
if __name__ == "__main__":
rec = Recognition()
db = DBHelper()
cam = WebcamVideoStream(src=0).start()
user = ""
response = input("Register new user? y or n \n")
if response == 'y':
rec.is_registering = True
user = input("Enter a username: "******"", "", "", "", ""])
else:
rec.is_registering = False
while (True):
frame = cam.read()
frame = cv2.resize(frame, (640, 480))
out, user, gest = rec.processFrame(frame, user)
cv2.imshow("out", out)
cv2.waitKey(1)
cam.release()
cv2.destroyAllWindows()
# postprocessing
for i in range(detections.shape[2]):
confidence = detections[0, 0, i, 2]
if confidence > conf_threshold:
x1 = int(detections[0, 0, i, 3] * w)
y1 = int(detections[0, 0, i, 4] * h)
x2 = int(detections[0, 0, i, 5] * w)
y2 = int(detections[0, 0, i, 6] * h)
# draw rects
cv2.rectangle(result_img, (x1, y1), (x2, y2), (255, 255, 255),
int(round(h / 150)), cv2.LINE_AA)
cv2.putText(result_img, '%.2f%%' % (confidence * 100.),
(x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 1,
(255, 255, 255), 2, cv2.LINE_AA)
# inference time
fps.update()
cv2.putText(result_img, 'FPS(dnn): %.2f' % (fps), (10, 30),
cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)
# visualize
if args["display"] > 0:
cv2.imshow("Frame", frame)
key = cv2.waitKey(1) & 0xFF
fps.stop()
cap.release()
cv2.destroyAllWindows()
