def _render_scene(self, camera, objects): """ Renders the window to 2D grayscale image Called from render function for each viewpoint """ scene = vapory.Scene(camera=camera, objects=objects) img = scene.render(width=self.render_size[0], height=self.render_size[1], quality=3) img = rgb2gray(img) return img
def _render_window_to2D(self): """ Renders the window to 2D black and white image Called from render function for each viewpoint """ self.vtkrender_window.Render() self.vtkwin_im = vtk.vtkWindowToImageFilter() self.vtkwin_im.SetInput(self.vtkrender_window) self.vtkwin_im.Update() vtk_image = self.vtkwin_im.GetOutput() height, width, _ = vtk_image.GetDimensions() vtk_array = vtk_image.GetPointData().GetScalars() components = vtk_array.GetNumberOfComponents() arr = vtk_to_numpy(vtk_array).reshape(height, width, components) arr = rgb2gray(arr) return arr
def _render_window_to2D(self, rgb=False): """ Renders the window to 2D grayscale image Called from render function for each viewpoint """ self.vtkrender_window.Render() self.vtkwin_im = vtk.vtkWindowToImageFilter() self.vtkwin_im.SetInput(self.vtkrender_window) self.vtkwin_im.Update() vtk_image = self.vtkwin_im.GetOutput() width, height, _ = vtk_image.GetDimensions() vtk_array = vtk_image.GetPointData().GetScalars() components = vtk_array.GetNumberOfComponents() arr = vtk_to_numpy(vtk_array).reshape(height, width, components) if rgb: arr = arr.transpose([2, 1, 0]) else: arr = rgb2gray(arr) return arr
't1_cs_d1', 't1_cs_d2', 't2_ap_d1', 't2_ap_d2', 't2_mf_d1', 't2_mf_d2', 't2_rp_d1', 't2_rp_d2' ] variations = [o + '_' + t for t in transformations for o in objects] img_size = (100, 100) output = np.zeros((90, np.prod(img_size))) row_labels = [] i = 0 for obj in objects: print(obj) target_file = "{0:s}/{1:s}.png".format(stimuli_folder, obj) target_img = misc.imread(target_file) target_img = hlp.rgb2gray(target_img) target_img = misc.imresize(target_img, img_size) output[i, :] = np.ravel(target_img) row_labels.append(obj) i += 1 for obj in variations: print(obj) target_file = "{0:s}/{1:s}.png".format(stimuli_folder, obj) target_img = misc.imread(target_file) target_img = hlp.rgb2gray(target_img) target_img = misc.imresize(target_img, img_size)
objects = ["o1", "o2", "o3", "o4", "o5", "o6", "o7", "o8", "o9", "o10"] transformations = ["t1_cs_d1", "t1_cs_d2", "t2_ap_d1", "t2_ap_d2", "t2_mf_d1", "t2_mf_d2", "t2_rp_d1", "t2_rp_d2"] variations = [o + "_" + t for t in transformations for o in objects] img_size = (100, 100) output = np.zeros((90, np.prod(img_size))) row_labels = [] i = 0 for obj in objects: print(obj) target_file = "{0:s}/{1:s}.png".format(stimuli_folder, obj) target_img = misc.imread(target_file) target_img = hlp.rgb2gray(target_img) target_img = misc.imresize(target_img, img_size) output[i, :] = np.ravel(target_img) row_labels.append(obj) i += 1 for obj in variations: print(obj) target_file = "{0:s}/{1:s}.png".format(stimuli_folder, obj) target_img = misc.imread(target_file) target_img = hlp.rgb2gray(target_img) target_img = misc.imresize(target_img, img_size)