gridshape = (30, 60)
rows, cols = np.indices(gridshape)
rows = (1080 * rows / gridshape[0]).astype(np.int32)
cols = (1920 * cols / gridshape[1]).astype(np.int32)
y_coords = rows.flatten()
x_coords = cols.flatten()
else:
y_coords = np.ones([1920]) * 1080 / 2
x_coords = np.arange(1920)
k = Kinect()
k.start()
for i in range(500):
start = time.time()
rgb, d = k.get_current_rgbd_frame(copy=False)
rgb[..., 2] = np.logical_or(d > 5000, d < 150) * 255
print("kinect time: %f\t" % (time.time() - start), end="")
cv2.imshow('color', rgb)
cv2.waitKey(1)
start = time.time()
if GRID_VS_LINE:
d_coord = d[rows, cols].flatten()
else:
d_coord = d[540, :]
print(d_coord.shape, x_coords.shape, y_coords.shape)
valid = np.logical_and(d_coord < 5000, d_coord > 150)
d_coord_i = d_coord[valid]
import time
import pickle
from project_gl import Projector
width, height = 1920, 1080
M = pickle.load(open('direct_model/correspondances/one_matrix.pickle', 'rb'))
M = np.concatenate([M, [[0, 0, 0, 0]]], 0)
p = Projector(M,
x_res=width,
y_res=height,
proj_x_res=1366,
proj_y_res=768,
monitor=1)
from kinect import Kinect
k = Kinect()
k.start()
# depth = 1000 * np.ones([height, width], dtype=np.float32)
while True:
# rgb = 255*np.random.rand(height, width, 3)
# rgb = np.random.randint(0, 255, size=[height, width, 3])
rgb, depth = k.get_current_rgbd_frame()
if p.draw_frame(255 - rgb[..., :3], depth):
p.stop()
k.stop()
import numpy as np
from kinect import Kinect
k = Kinect()
k.start()
config_file = "mask_rcnn_demo/maskrcnn-benchmark/configs/caffe2/e2e_mask_rcnn_R_50_FPN_1x_caffe2.yaml"
# update the config options with the config file
cfg.merge_from_file(config_file)
# manual override some options
cfg.merge_from_list(["MODEL.DEVICE", "cuda"])
coco_demo = COCODemo(
cfg,
min_image_size=800,
confidence_threshold=0.7,
)
# load image and then run prediction
while True:
print('a')
image, d = k.get_current_rgbd_frame(copy=True)
image = image[..., :3]
print('d')
predictions = coco_demo.run_on_opencv_image(image)
print('c')
print(predictions.shape, predictions.dtype)
k.stop()
M = pickle.load(open('direct_model/correspondances/one_matrix.pickle', 'rb'))
# M = np.array([
# [1, 0, 0, 0],
# [0, 1, 0, 0],
# [0, 0, 1, 0],
# ])
k = Kinect()
k.start()
dr = DrawingWindow()
dr.update()
for i in range(100):
print(i)
rgb, _ = k.get_current_rgbd_frame(copy=True)
dr.last_kinect_frame = rgb[..., :3]
dr.update()
dr.close()
w = Fullscreen_Window()
w.clear()
w.update()
for i in range(500):
rgb, d = k.get_current_rgbd_frame(copy=False)
locs = np.array(dr.circles)
print('circle shapes', locs.shape)
rgb[..., 2] = np.logical_or(d > 5000, d < 150) * 255
