##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ ## Created by: RainbowSecret ## Modified from: https://github.com/AlexHex7/Non-local_pytorch ## Microsoft Research ## yuyua@microsoft.com ## Copyright (c) 2018 ## ## This source code is licensed under the MIT-style license found in the ## LICENSE file in the root directory of this source tree ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ import matplotlib matplotlib.use('Agg') import torch import os import sys import pdb import cv2 import numpy as np from torch import nn from torch.nn import functional as F import functools import matplotlib.pyplot as plt from sklearn import svm, datasets from sklearn.model_selection import train_test_split from sklearn.metrics import confusion_matrix from PIL import Image as PILImage torch_ver = torch.__version__[:3] ignore_label = 255 id_to_trainid = {-1: ignore_label, 0: ignore_label, 1: ignore_label, 2: ignore_label, 3: ignore_label, 4: ignore_label, 5: ignore_label, 6: ignore_label, 7: 0, 8: 1, 9: ignore_label, 10: ignore_label, 11: 2, 12: 3, 13: 4, 14: ignore_label, 15: ignore_label, 16: ignore_label, 17: 5, 18: ignore_label, 19: 6, 20: 7, 21: 8, 22: 9, 23: 10, 24: 11, 25: 12, 26: 13, 27: 14, 28: 15, 29: ignore_label, 30: ignore_label, 31: 16, 32: 17, 33: 18} class_name_dict = {0:'road', 1:'sidewalk', 2:'building', 3:'wall', 4:'fence', 5:'pole', 6:'trafficlight', 7:'trafficsign', 8:'vegetation', 9:'terrian', 10:'sky', 11:'person', 12:'rider', 13:'car', 14:'truck', 15:'bus', 16:'train', 17:'motorcycle', 18:'bicycle', 255: 'none'} def get_palette(num_cls): """ Returns the color map for visualizing the segmentation mask. Args: num_cls: Number of classes Returns: The color map """ palette = [0] * (num_cls * 3) palette[0:3] = (128, 64, 128) # 0: 'road' palette[3:6] = (244, 35,232) # 1 'sidewalk' palette[6:9] = (70, 70, 70) # 2''building' palette[9:12] = (102,102,156) # 3 wall palette[12:15] = (190,153,153) # 4 fence palette[15:18] = (153,153,153) # 5 pole palette[18:21] = (250,170, 30) # 6 'traffic light' palette[21:24] = (220,220, 0) # 7 'traffic sign' palette[24:27] = (107,142, 35) # 8 'vegetation' palette[27:30] = (152,251,152) # 9 'terrain' palette[30:33] = ( 70,130,180) # 10 sky palette[33:36] = (220, 20, 60) # 11 person palette[36:39] = (255, 0, 0) # 12 rider palette[39:42] = (0, 0, 142) # 13 car palette[42:45] = (0, 0, 70) # 14 truck palette[45:48] = (0, 60,100) # 15 bus palette[48:51] = (0, 80,100) # 16 train palette[51:54] = (0, 0,230) # 17 'motorcycle' palette[54:57] = (119, 11, 32) # 18 'bicycle' palette[57:60] = (105, 105, 105) return palette palette = get_palette(20) def id2trainId(label, id_to_trainid, reverse=False): label_copy = label.copy() if reverse: for v, k in id_to_trainid.items(): label_copy[label == k] = v else: for k, v in id_to_trainid.items(): label_copy[label == k] = v return label_copy def down_sample_target(target, scale): row, col = target.shape step = scale r_target = target[0:row:step, :] c_target = r_target[:, 0:col:step] return c_target def visualize_map(atten, shape, out_path): atten_np = atten.cpu().data.numpy() # c x hw (h, w) = shape for row in range(2): for col in range(9): # plt.subplot(5,8,9+row*8+col) # pdb.set_trace() cm = atten_np[row*8+col] cm = np.reshape(cm, (h, w)) plt.tight_layout() plt.imshow(cm, cmap='Blues', interpolation='nearest') plt.axis('off') plt.savefig(out_path+'regionmap_'+str(row*8+col)+'png', bbox_inches='tight', pad_inches = 0) pdb.set_trace() def Vis_A2_Atten(img_path, label_path, image, label, atten, shape, cmap=plt.cm.Blues, index=1, choice=1, maps_count=32): """ This function prints and plots the attention weight matrix. Input: choice: 1 represents plotting the histogram of the weights' distribution 2 represents plotting the attention weights' map """ atten_np = atten.cpu().data.numpy() # c x hw (h, w) = shape if choice == 1: # read image/ label from the given paths image = cv2.imread(img_path[index], cv2.IMREAD_COLOR) #1024x2048x3 image = image[:, :, -1] image = cv2.resize(image, dsize=(h, w),interpolation=cv2.INTER_CUBIC) label = cv2.imread(label_path[index], cv2.IMREAD_GRAYSCALE) #1024x2048 label = id2trainId(label, id_to_trainid) label = down_sample_target(label, 8) else: # use the image crop directly. image = image.astype(np.float)[index] #3x1024x2048 image = np.transpose(image, (1,2,0)) mean = (102.9801, 115.9465, 122.7717) image += mean image = image.astype(np.uint8) image = cv2.resize(image, dsize=(w, h),interpolation=cv2.INTER_CUBIC) label = label.cpu().numpy().astype(np.uint8)[index] label = down_sample_target(label, 8) img_label = PILImage.fromarray(label) img_label.putpalette(palette) plt.tight_layout() plt.figure(figsize=(48, 24)) plt.axis('off') plt.subplot(5,8,1) plt.imshow(image) plt.axis('off') plt.subplot(5,8,2) plt.imshow(img_label) plt.axis('off') for row in range(4): for col in range(8): plt.subplot(5,8,9+row*8+col) cm = atten_np[row*8+col] cm = np.reshape(cm, (h, w)) plt.imshow(cm, cmap='Blues', interpolation='nearest') plt.axis('off') plt.gca().set_title("Attention Map %d" %(row*8+col)) # plt.subplot(3,7,1) # plt.imshow(image) # plt.axis('off') # plt.subplot(3,7,2) # plt.imshow(img_label) # plt.axis('off') # for row in range(3): # for col in range(7): # if (row*7+col) == 0 or (row*7+col) == 1: # continue # plt.subplot(3,7,row*7+col+1) # cm = atten_np[row*7+col-2] # cm = np.reshape(cm, (h, w)) # plt.imshow(cm, cmap='Blues', interpolation='nearest') # plt.axis('off') # plt.gca().set_title("Attention Map %d" %(row*7+col-2)) plt.show() outpath='./object_context_vis/a2map_32/' plt.savefig(outpath+'a2map_'+str(img_path[0][0:-3].split('/')[-1])+'png', bbox_inches='tight', pad_inches = 0) print("image id: {}".format(img_path[0][0:-3].split('/')[-1])) def Vis_FastOC_Atten(img_path, label_path, image, label, atten, shape, cmap=plt.cm.Blues, index=1, choice=1, subplot=False): """ This function prints and plots the attention weight matrix. Input: choice: 1 represents plotting the histogram of the weights' distribution 2 represents plotting the attention weights' map """ atten_np = atten.cpu().data.numpy() # c x hw (h, w) = shape if choice == 1: # read image/ label from the given paths image = cv2.imread(img_path[index], cv2.IMREAD_COLOR) #1024x2048x3 image = image[:, :, -1] image = cv2.resize(image, dsize=(h, w),interpolation=cv2.INTER_CUBIC) label = cv2.imread(label_path[index], cv2.IMREAD_GRAYSCALE) #1024x2048 label = id2trainId(label, id_to_trainid) label = down_sample_target(label, 8) else: # use the image crop directly. image = image.astype(np.float)[index] #3x1024x2048 image = np.transpose(image, (1,2,0)) mean = (102.9801, 115.9465, 122.7717) image += mean image = image.astype(np.uint8) image = cv2.resize(image, dsize=(w, h),interpolation=cv2.INTER_CUBIC) label = label.cpu().numpy().astype(np.uint8)[index] label = down_sample_target(label, 8) img_label = PILImage.fromarray(label) img_label.putpalette(palette) plt.tight_layout() plt.figure(figsize=(48, 24)) plt.axis('off') if subplot: plt.subplot(3,7,1) plt.imshow(image) plt.axis('off') plt.subplot(3,7,2) plt.imshow(img_label) plt.axis('off') for row in range(3): for col in range(7): if (row*7+col) == 0 or (row*7+col) == 1: continue if subplot: plt.subplot(3,7,row*7+col+1) cm = atten_np[row*7+col-2] cm = np.reshape(cm, (h, w)) plt.imshow(cm, cmap='Blues', interpolation='nearest') plt.axis('off') if not subplot: plt.show() outpath='./object_context_vis/fast_baseoc_map/' plt.savefig(outpath+'fast_baseoc_map_'+str(img_path[0][0:-3].split('/')[-1])+'_'+str(row*7+col-2)+'.png', bbox_inches='tight', pad_inches = 0) else: plt.gca().set_title("Attention Map %d" %(row*7+col-2)) if subplot: plt.show() outpath='./object_context_vis/fast_baseoc_map/' plt.savefig(outpath+'fast_baseoc_map_'+str(img_path[0][0:-3].split('/')[-1])+'png', bbox_inches='tight', pad_inches = 0) print("image id: {}".format(img_path[0][0:-3].split('/')[-1]))