基于LK方法的实时光流计算 ======================== 这个示例展示一个在AOP上运行的LK方法 ----------------------------------- 调用接口： - tianmoucv.proc.opticalflow.LK_optical_flow .. code:: ipython3 %load_ext autoreload .. parsed-literal:: The autoreload extension is already loaded. To reload it, use: %reload_ext autoreload 引入必要的包 ------------ .. code:: ipython3 %autoreload import sys import numpy as np import os import cv2 import matplotlib.pyplot as plt import torch import torch.nn.functional as F import math from tianmoucv.data import TianmoucDataReader .. parsed-literal:: TianMouCV™ 0.3.5.4, via Y. Lin update new nn for reconstruction 数据构造 -------- .. code:: ipython3 train='/data/lyh/tianmoucData/tianmoucReconDataset/train/' dirlist = os.listdir(train) traindata = [train + e for e in dirlist] val='/data/lyh/tianmoucData/tianmoucReconDataset/test/' vallist = os.listdir(val) valdata = [val + e for e in vallist] key_list = [] print('---------------------------------------------------') for sampleset in traindata: print('---->',sampleset,'有：',len(os.listdir(sampleset)),'个样本') for e in os.listdir(sampleset): print(e,end=" ") key_list.append(e) print('---------------------------------------------------') for sampleset in valdata: print('---->',sampleset,'有：',len(os.listdir(sampleset)),'个样本') for e in os.listdir(sampleset): print(e,end=" ") key_list.append(e) all_data = valdata + traindata .. parsed-literal:: --------------------------------------------------- ----> /data/lyh/tianmoucData/tianmoucReconDataset/train/normal 有： 67 个样本 outdoor_cross_8 train_cross2 traffic5 indoor_office_2 train_indoor_dog4 outdoor_cross_5 indoor_office_6 train_running_man_5 indoor_office_1 train_exam_fan2 indoor_office_3 people1 train_exam_fan5 indoor_office_4 indoor_slefie_2 outdoor_cross_9 outdoor_bridge_1 outdoor_cross_4 outdoor_cross_1 outdoor_4huan traffic15 outdoor_cross_12 outdoor_bridge_2 traffic9 traffic2 traffic_nohdr_16 traffic11 train_exam_fan1 train_indoor_dog1 train_cross3 train_driving5 traffic7 traffic_nohdr_15 train_driving14 train_driving9 outdoor_cross_7 train_driving4 traffic10 train_running_man_6 train_exam_fan3 train_driving6 train_cross4 train_driving3 outdoor_cross_3 train_driving11 traffic14 outdoor_bz_1 outdoor_hutong_1 indoor_slefie_1 indoor_keyboard1 train_man_play_ball1 train_driving8 traffic3 train_driving7 outdoor_cross_11 train_exam_full4 train_running_man_7 people10 traffic6 train_driving13 traffic13 traffic_nohdr_17 train_driving10 train_exam_full2 train_indoor_dog2 traffic1 train_exam_full1 ----> /data/lyh/tianmoucData/tianmoucReconDataset/train/extreme 有： 51 个样本 flicker_12 underbridge_hdr_4 hdr_people9 train_exam_flicker3 underbridge_hdr_2 hdr_traffic35 hdr_people15 flicker_3 hdr_people2 train_tunnel3_hdr_ae hdr_traffic18 shake2 indoor_crazy_shake flicker_1 flicker_8 hdr_traffic20 underbridge_hdr_1 hdr_traffic30 train_exam_flicker2 hdr_traffic19 flicker_17 flicker_6 shake5 hdr_traffic23 train_exam_flicker1 train_hdr_human hdr_people5 hdr_people3 flicker_0 hdr_people11 train_tunnel6_hdr_ae flicker_4 flicker_9 flicker_11 flicker_15 hdr_people7 shake4 hdr_traffic26 train_tunnel4_hdr_ae hdr_traffic25 hdr_traffic29 train_tunnel1_hdr_blur shake1 train_driving2 hdr_traffic22 train_exam_fan_QRcode_1 hdr_people6 flicker_14 hdr_traffic34 hdr_people14 train_tunnel5_hdr_ae --------------------------------------------------- ----> /data/lyh/tianmoucData/tianmoucReconDataset/test/normal 有： 24 个样本 test_tunnel2 test_man_play_ball3 test_exam_fan4 test_driving24 test_driving3 test_driving20 indoor_office_5 outdoor_cross_10 test_running_man_8 test_cross3 outdoor_cross_13 outdoor_4huan_2 test_exam_full3 test_driving4 traffic4 test_driving12 test_driving16 outdoor_cross_6 traffic8 test_driving8 traffic12 outdoor_bridge_3 test_running_man_4 indoor_keyboard2 ----> /data/lyh/tianmoucData/tianmoucReconDataset/test/extreme 有： 30 个样本 shake3 test_tunnel7_hdr_ae hdr_traffic36 test_exam_fan_QRcode_2 flicker_16 hdr_traffic21 hdr_traffic32 test_indoor_dog3 hdr_traffic24 train_exam_flicker5 hdr_people13 test_tunnel8_hdr_ae_double hdr_people8 flicker_13 hdr_traffic33 hdr_people4 test_exam_fan_QRcode_3 hdr_traffic31 indoor_selfie_shake_3 flicker_7 hdr_people16 flicker_10 flicker_2 hdr_people12 test_driving_night_light1 test_hdr_human2 underbridge_hdr_3 flicker_18 flicker_5 shake6 光流计算 ----------------------------------- .. code:: ipython3 %autoreload from tianmoucv.proc.opticalflow import interpolate_image,flow_to_image from tianmoucv.proc.opticalflow import LK_optical_flow from tianmoucv.isp import SD2XY import time from IPython.display import clear_output accumTime = 5 noiseThresh = 8 lambda_of_HS = 25 #bigger->smoother #(输入是0~255时lambda要>1,否则千万不能太大) W = 640 H = 320 gridX, gridY = np.meshgrid(np.arange(W), np.arange(H)) key_list = ['test_exam_fan4'] imlist = [] for key in key_list: pathList = all_data dataset = TianmoucDataReader(pathList,showList=True, matchkey = key, MAXLEN=-1, speedUpRate=1, print_info=False) for index in range(len(dataset)): if index <= 15: continue elif index > 20: break else: print('rpogress:',index,'/',len(dataset)) sample = dataset[index] F0 = sample['F0'] F1 = sample['F1'] tsdiff = sample['rawDiff'] F0show = F0.copy() show_img = F0show.copy() for b in range(25//accumTime): sd = 0 td = 0 TD = 0 #积累几帧diff for t in range(accumTime): threshed_tsdiff = tsdiff[:,b*accumTime+t,...].permute(1,2,0) threshed_tsdiff[abs(threshed_tsdiff) 225 flow_show[np.stack([mask]*3,axis=-1)]=0 show_img = interpolate_image(show_img,u,v) tdiff_show = np.stack([tdshow[0,0,...].cpu()*255]*3,axis=2).astype(np.uint8) sparsity = 8 scale = 10 for w in range(640//sparsity): for h in range(320//sparsity): x = int(w*sparsity) y = int(h*sparsity) u_ij = -u[0,y,x] v_ij = -v[0,y,x] color = flow_show[y,x,:] color = tuple([int(e+20) for e in color]) if (u_ij**2+v_ij**2)>5: cv2.arrowedLine(flow_show, (x,y), (int(x+u_ij*scale),int(y+v_ij*scale)), color,2, tipLength=0.15) tdiff_show_tensor = torch.Tensor(tdiff_show.copy()) flow_show_tensor = torch.Tensor(flow_show) mask = torch.stack([torch.mean(flow_show_tensor,dim=-1)>0]*3,dim=-1) tdiff_show_tensor[mask] = flow_show_tensor[mask]/255.0 tdiff_show_merge = tdiff_show_tensor.numpy() imshow = np.concatenate([flow_show/255.0,tdiff_show,tdiff_show_merge],axis=0) imlist.append(imshow) if b %10 ==0: clear_output() plt.figure(figsize=(9,5)) plt.axis('off') plt.subplot(2,2,1) plt.imshow(Ix,cmap='gray') plt.subplot(2,2,2) plt.imshow(TD,cmap='gray') plt.axis('off') plt.subplot(2,2,3) plt.imshow(F0show) plt.subplot(2,2,4) plt.imshow(flow_show) plt.show() .. image:: output_7_0.png .. code:: ipython3 def images_to_video(frame_list,name,Val_size=(512,256),Flip=False): fps = 30 size = (Val_size[0], Val_size[1]) # 需要转为视频的图片的尺寸 out = cv2.VideoWriter(name,0x7634706d , fps, size) for frame in frame_list: frame = (frame-np.min(frame))/(np.max(frame)-np.min(frame)) * 255 frame2 = frame.astype(np.uint8) out.write(frame2) out.release() images_to_video(imlist,'./LKOF_'+key+'.mp4',Val_size=(640,960),Flip=True)