# SVM-RGBD-PCL-Python (70%) ## PCL-Python을 이용한 분류 > Jupyter 버젼은 [\[이곳\]](https://github.com/adioshun/gitBook_Tutorial_PCL/blob/master/Intermediate/Part02-Chapter04-SVM-RGBD-PCL-Python.ipynb), [\[이곳-Pileline\]](https://github.com/adioshun/gitBook_Tutorial_PCL/blob/master/Intermediate/Part02-Chapter04-SVM-RGBD-PCL-Python-Full.ipynb)에서 확인 가능 합니다. > > 예측에 사용된 pcd샘플은 [\[이곳\]](https://github.com/adioshun/gitBook_Tutorial_PCL/tree/master/Intermediate/sample)의 `table_scene_inliers_0.pcd` \~ `table_scene_inliers_6.pcd`를 사용 하였습니다. ```python #!/usr/bin/env python import pickle import itertools import numpy as np import matplotlib.pyplot as plt from sklearn import svm from sklearn.preprocessing import LabelEncoder, StandardScaler ``` ## [features.py](https://github.com/mkhuthir/RoboND-Perception-Project/blob/master/src/sensor_stick/src/sensor_stick/features.py) ```python import matplotlib.colors def rgb_to_hsv(rgb_list): rgb_normalized = [1.0*rgb_list[0]/255, 1.0*rgb_list[1]/255, 1.0*rgb_list[2]/255] hsv_normalized = matplotlib.colors.rgb_to_hsv([[rgb_normalized]])[0][0] return hsv_normalized def compute_color_histograms_PCD(cloud, using_hsv=False): # Compute histograms for the clusters point_colors_list = [] """ # Step through each point in the point cloud for ROS msg for point in pc2.read_points(cloud, skip_nans=True): rgb_list = float_to_rgb(point[3]) if using_hsv: point_colors_list.append(rgb_to_hsv(rgb_list) * 255) else: point_colors_list.append(rgb_list) """ # Step through each point in the point cloud for PCD for point in cloud[:,3]: # for PCD file rgb_list = float_to_rgb(point) if using_hsv: point_colors_list.append(rgb_to_hsv(rgb_list) * 255) else: point_colors_list.append(rgb_list) # Populate lists with color values channel_1_vals = [] channel_2_vals = [] channel_3_vals = [] for color in point_colors_list: channel_1_vals.append(color[0]) channel_2_vals.append(color[1]) channel_3_vals.append(color[2]) # Compute histograms nbins=32 bins_range=(0, 256) # Compute the histogram of the channels separately channel_1_hist = np.histogram(channel_1_vals, bins=nbins, range=bins_range) channel_2_hist = np.histogram(channel_2_vals, bins=nbins, range=bins_range) channel_3_hist = np.histogram(channel_3_vals, bins=nbins, range=bins_range) # Concatenate the histograms into a single feature vector hist_features = np.concatenate((channel_1_hist[0], channel_2_hist[0], channel_1_hist[0])).astype(np.float64) # Normalize the result normed_features = hist_features / np.sum(hist_features) # Generate random features for demo mode. # Replace normed_features with your feature vector #normed_features = np.random.random(96) # Return the feature vector return normed_features ``` ```python def get_normals(cloud_path): """ The actual *compute* call from the NormalEstimation class does nothing internally but: for each point p in cloud P 1. get the nearest neighbors of p 2. compute the surface normal n of p 3. check if n is consistently oriented towards the viewpoint and flip otherwise # normals: pcl._pcl.PointCloud_Normal,size: 26475 # cloud: pcl._pcl.PointCloud """ cloud = pcl.load(cloud_path) feature = cloud.make_NormalEstimation() #feature.set_RadiusSearch(0.1) #Use all neighbors in a sphere of radius 3cm feature.set_KSearch(3) normals = feature.compute() return normals def compute_normal_histograms(normal_cloud, nbins=32, nrange=(-1,1)): ''' Computes and bins the point-cloud data using the objects distribution of surface normals. :param: normal_cloud, point cloud containing the filtered clusters. :param: nbins,number of bins that data will be pooled into. :param: nrange, value range of the data to be pooled. :return: the normalised histogram of surface normals ''' norm_x_vals = [] norm_y_vals = [] norm_z_vals = [] for I in range(0,normal_cloud.size): norm_x_vals.append(normal_cloud[I][0]) norm_y_vals.append(normal_cloud[I][1]) norm_z_vals.append(normal_cloud[I][2]) # Compute histograms of normal values (just like with color) norm_x_hist = np.histogram(norm_x_vals, bins=nbins, range=nrange) norm_y_hist = np.histogram(norm_y_vals, bins=nbins, range=nrange) norm_z_hist = np.histogram(norm_z_vals, bins=nbins, range=nrange) # Concatenate and normalize the histograms hist_features = np.concatenate((norm_x_hist[0], norm_y_hist[0], norm_z_hist[0])).astype(np.float64) normed_features = hist_features / np.sum(hist_features) return normed_features ``` ## 예측 ```python cloud = pcl.load_XYZRGB("tabletop.pcd") sample_cloud = cloud.to_array() ``` ```python # Generate Color Histogram for the spawned model # Enable using_hsv for better results c_hists = compute_color_histograms_PCD(sample_cloud, using_hsv=True) # Generate normals and notmal histograms for the spawned model normals = get_normals("table_scene_inliers_0.pcd") n_hists = compute_normal_histograms(normals) # Generate feature by concatenate of color and normals. feature = np.concatenate((c_hists, n_hists)) ``` ```python detected_objects = [] # Make the prediction, retrieve the label for the result # and add it to detected_objects_labels list ################################ model = pickle.load(open('model.sav', 'rb')) #https://raw.githubusercontent.com/mkhuthir/RoboND-Perception-Project/master/model.sav clf = model['classifier'] encoder = LabelEncoder() encoder.classes_ = model['classes'] scaler = model['scaler'] prediction = clf.predict(scaler.transform(feature.reshape(1,-1))) label = encoder.inverse_transform(prediction)[0] print("Predicted Result : ", label) ``` ``` ('Predicted Result : ', 'snacks') ``` --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. 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