Python cm.jet_r方法代码示例 - 纯净天空 (2024)

# 需要导入模块: from matplotlib import cm [as 别名]# 或者: from matplotlib.cm import jet_r [as 别名]def plot_pc_old(pc_np, z_cutoff=70, birds_view=False, color='height', size=0.3, ax=None): # remove large z points valid_index = pc_np[:, 2] < z_cutoff pc_np = pc_np[valid_index, :] if ax is None: fig = plt.figure(figsize=(9, 9)) ax = Axes3D(fig) if color == 'height': c = pc_np[:, 1] ax.scatter(pc_np[:, 0].tolist(), pc_np[:, 1].tolist(), pc_np[:, 2].tolist(), s=size, c=c, cmap=cm.jet_r) elif color == 'reflectance': assert False else: ax.scatter(pc_np[:, 0].tolist(), pc_np[:, 1].tolist(), pc_np[:, 2].tolist(), s=size, c=color) axisEqual3D(ax) if True == birds_view: ax.view_init(elev=0, azim=-90) else: ax.view_init(elev=-45, azim=-90) # ax.invert_yaxis() return ax 

开发者ID:lijx10，项目名称:USIP，代码行数:26，代码来源:vis_tools.py

# 需要导入模块: from matplotlib import cm [as 别名]# 或者: from matplotlib.cm import jet_r [as 别名]def colorize_depth(depth_map): # scale everything to [0, 255] sorted_depth = np.unique(np.sort(depth_map.flatten())) min_depth = sorted_depth[0] max_depth = sorted_depth[len(sorted_depth) - 1] depth_map = np.asarray(map(lambda pixel: (pixel - min_depth) * 1.0 / (max_depth - min_depth), depth_map)) # Apply jet colormap to it depth_map = np.uint8(cm.jet_r(depth_map) * 255) return depth_map[:, :, 0:3]# Given a CSV row of metadata, colorize the image and save into a destination 

开发者ID:nigroup，项目名称:nideep，代码行数:18，代码来源:preprocess_object_recognition_eitel.py

# 需要导入模块: from matplotlib import cm [as 别名]# 或者: from matplotlib.cm import jet_r [as 别名]def save_gradcam(filename, gcam, raw_image, paper_cmap=False): gcam = gcam.cpu().numpy() cmap = cm.jet_r(gcam)[..., :3] * 255.0 if paper_cmap: alpha = gcam[..., None] gcam = alpha * cmap + (1 - alpha) * raw_image else: gcam = (cmap.astype(np.float) + raw_image.astype(np.float)) / 2 cv2.imwrite(filename, np.uint8(gcam)) 

# 需要导入模块: from matplotlib import cm [as 别名]# 或者: from matplotlib.cm import jet_r [as 别名]def draw_node(cls, node, surface, origin, size, config): import pygame cmap = cm.jet_r norm = mpl.colors.Normalize(vmin=0, vmax=1 / (1 - config["gamma"])) color = cmap(norm(node.get_value()), bytes=True) pygame.draw.rect(surface, color, (origin[0], origin[1], size[0], size[1]), 0) 

# 需要导入模块: from matplotlib import cm [as 别名]# 或者: from matplotlib.cm import jet_r [as 别名]def display_highway(cls, agent, surface): """ Particular visualization of the state space that is used for highway_env environments only. :param agent: the agent to be displayed :param surface: the surface on which the agent is displayed. """ import pygame norm = mpl.colors.Normalize(vmin=-2, vmax=2) cmap = cm.jet_r try: grid_shape = agent.mdp.original_shape except AttributeError: grid_shape = cls.highway_module.finite_mdp.compute_ttc_grid(agent.env, time_quantization=1., horizon=10.).shape cell_size = (surface.get_width() // grid_shape[2], surface.get_height() // (grid_shape[0] * grid_shape[1])) speed_size = surface.get_height() // grid_shape[0] value = agent.get_state_value().reshape(grid_shape) for h in range(grid_shape[0]): for i in range(grid_shape[1]): for j in range(grid_shape[2]): color = cmap(norm(value[h, i, j]), bytes=True) pygame.draw.rect(surface, color, ( j * cell_size[0], i * cell_size[1] + h * speed_size, cell_size[0], cell_size[1]), 0) pygame.draw.line(surface, cls.BLACK, (0, h * speed_size), (grid_shape[2] * cell_size[0], h * speed_size), 1) states, actions = agent.plan_trajectory(agent.mdp.state) for state in states: (h, i, j) = np.unravel_index(state, grid_shape) pygame.draw.rect(surface, cls.RED, (j * cell_size[0], i * cell_size[1] + h * speed_size, cell_size[0], cell_size[1]), 1) 

# 需要导入模块: from matplotlib import cm [as 别名]# 或者: from matplotlib.cm import jet_r [as 别名]def display(cls, agent, surface, sim_surface=None, display_text=True): """ Display the action-values for the current state :param agent: the DQNAgent to be displayed :param surface: the pygame surface on which the agent is displayed :param sim_surface: the pygame surface on which the env is rendered :param display_text: whether to display the action values as text """ import pygame action_values = agent.get_state_action_values(agent.previous_state) action_distribution = agent.action_distribution(agent.previous_state) cell_size = (surface.get_width() // len(action_values), surface.get_height()) pygame.draw.rect(surface, cls.BLACK, (0, 0, surface.get_width(), surface.get_height()), 0) # Display node value for action, value in enumerate(action_values): cmap = cm.jet_r norm = mpl.colors.Normalize(vmin=0, vmax=1/(1-agent.config["gamma"])) color = cmap(norm(value), bytes=True) pygame.draw.rect(surface, color, (cell_size[0]*action, 0, cell_size[0], cell_size[1]), 0) if display_text: font = pygame.font.Font(None, 15) text = "v={:.2f} / p={:.2f}".format(value, action_distribution[action]) text = font.render(text, 1, (10, 10, 10), (255, 255, 255)) surface.blit(text, (cell_size[0]*action, 0)) if sim_surface and hasattr(agent.value_net, "get_attention_matrix"): cls.display_vehicles_attention(agent, sim_surface) 

# 需要导入模块: from matplotlib import cm [as 别名]# 或者: from matplotlib.cm import jet_r [as 别名]def live(config_path, model_path, cuda, crf, camera_id): """ Inference from camera stream """ # Setup CONFIG = OmegaConf.load(config_path) device = get_device(cuda) torch.set_grad_enabled(False) torch.backends.cudnn.benchmark = True classes = get_classtable(CONFIG) postprocessor = setup_postprocessor(CONFIG) if crf else None model = eval(CONFIG.MODEL.NAME)(n_classes=CONFIG.DATASET.N_CLASSES) state_dict = torch.load(model_path, map_location=lambda storage, loc: storage) model.load_state_dict(state_dict) model.eval() model.to(device) print("Model:", CONFIG.MODEL.NAME) # UVC camera stream cap = cv2.VideoCapture(camera_id) cap.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc(*"YUYV")) def colorize(labelmap): # Assign a unique color to each label labelmap = labelmap.astype(np.float32) / CONFIG.DATASET.N_CLASSES colormap = cm.jet_r(labelmap)[..., :-1] * 255.0 return np.uint8(colormap) def mouse_event(event, x, y, flags, labelmap): # Show a class name of a mouse-overed pixel label = labelmap[y, x] name = classes[label] print(name) window_name = "{} + {}".format(CONFIG.MODEL.NAME, CONFIG.DATASET.NAME) cv2.namedWindow(window_name, cv2.WINDOW_AUTOSIZE) while True: _, frame = cap.read() image, raw_image = preprocessing(frame, device, CONFIG) labelmap = inference(model, image, raw_image, postprocessor) colormap = colorize(labelmap) # Register mouse callback function cv2.setMouseCallback(window_name, mouse_event, labelmap) # Overlay prediction cv2.addWeighted(colormap, 0.5, raw_image, 0.5, 0.0, raw_image) # Quit by pressing "q" key cv2.imshow(window_name, raw_image) if cv2.waitKey(10) == ord("q"): break