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