基于SD的特征追踪 ================ 这个示例展示一个在AOP上运行的特征点追踪算法 ------------------------------------------- 调用接口： - tianmoucv.proc.features.(HarrisCorner,sift,hog) - tianmoucv.proc.tracking.(feature_matching,mini_l2_cost_matching,align_images) .. code:: ipython3 %load_ext autoreload 必要的包 -------- .. code:: ipython3 %autoreload import sys,os import torch import numpy as np import matplotlib.pyplot as plt import torch import torch.nn.functional as F import matplotlib.pyplot as plt from tianmoucv.isp import lyncam_raw_comp,demosaicing_npy,SD2XY from tianmoucv.proc.features import HarrisCorner,sift,hog from tianmoucv.proc.tracking import feature_matching,mini_l2_cost_matching,align_images import cv2 from tianmoucv.data import TianmoucDataReader .. parsed-literal:: TianMouCV™ 0.3.5.5, via Y. Lin update exaple and fix dynamo optimization bug .. 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 .. code:: ipython3 %autoreload import time from tianmoucv.isp import lyncam_raw_comp,demosaicing_npy,SD2XY from tianmoucv.proc.features import HarrisCorner,sift,hog from tianmoucv.proc.tracking import feature_matching,mini_l2_cost_matching,align_images from tianmoucv.proc.reconstruct import TD_integration from IPython.display import clear_output import cv2 fl_aim = [] kp_aim = [] gap = 5 #目前的策略比较粗暴，间隔一个固定的帧就直接重新找特征点，防止积累误差 fix_update_frame = 500//gap tracking_count = 0 imlist = [] time_begin = time.time() history = dict([]) key= 'outdoor_bridge_3' startID = 80 endID = startID + 80 dataset = TianmoucDataReader(all_data,MAXLEN=500*1,matchkey=key,speedUpRate=1) with torch.no_grad(): for index in range(len(dataset)): if indexendID: break else: sample = dataset[index] F0 = sample['F0'] F1 = sample['F1'] tsdiff = sample['rawDiff'] threshed_tsdiff = tsdiff[:,...].permute(1,2,3,0) for t in range(0,threshed_tsdiff.shape[0],gap): tsdiff = torch.Tensor(sample['rawDiff']) hdr_show = tsdiff[:,t,...].permute(1,2,0).numpy().copy()/255 hdr_show = cv2.resize(hdr_show,(640,320)) SD = threshed_tsdiff[t,...,1:] TD = threshed_tsdiff[t,...,0] Ix1,Iy1= SD2XY(SD) Ix1[abs(Ix1)<5]=0 Iy1[abs(Iy1)<5]=0 Ix1/=255.0 Iy1/=255.0 featureList1 = [] kp1=[] #第1步：计算两张图对应Harris角点检测 startT = time.time() #TODO:改成自适应阈值，控制特征点数量和密度 idmap,R = HarrisCorner(Ix1,Iy1,k=0.01,th=0.01,nmsSize=15) ''' :param th: 控制阈值,范围是0-1,对梯度来说应该设小一点,float :param nmsSize: 最大值筛选的范围 :param k: 是一个经验参数,0-1,float ''' endT = time.time() for i in range(idmap.shape[0]): for j in range(idmap.shape[1]): if idmap[i,j]>0: kp1.append([i,j]) #第2步：计算两张图对应fp list的特征描述子 startT = time.time() kp1,featureList1 = sift(Ix1,Iy1,kp1) endT = time.time() kp1 = [ (p[0]*2,p[1]*2) for p in kp1 ] fl1 = torch.stack(featureList1,dim=0).view(len(kp1),-1) #第3步：更新待追踪特征点列表 if tracking_count % fix_update_frame == 0 or len(fl_aim)==0: print('update tracking target') kp_aim = kp1 fl_aim = fl1 history = dict([]) for i in range(len(kp_aim)): history[i] = [ kp1[i] ] else: if len(fl1)>0: matches = feature_matching(fl_aim,fl1, ratio=0.75) #只要匹配上，就更新待追踪点坐标和对应的特征描述子，以免场景变化过大影响追踪 for m in matches: src_pts = kp_aim[m[0].queryIdx] dst_pts = kp1[m[0].trainIdx] dist = (src_pts[0]-dst_pts[0])**2 + (src_pts[1]-dst_pts[1])**2 #750fps,过远的匹配认为是错的，这个值可以改 if dist < 400: history[m[0].queryIdx].append(kp1[m[0].trainIdx]) kp_aim[m[0].queryIdx] = kp1[m[0].trainIdx] fl_aim[m[0].queryIdx,:] = fl1[m[0].trainIdx,:] #绘制追踪结果 if index%1 ==0: F_show = F1.copy() for k in history: traj = history[k] y2, x2 = (None,None) for kp in traj: y1 , x1 = int(kp[0]),int(kp[1]) #cv2.circle(F_show,(x1,y1),2,(0,0.2,0.2)) if not x2 is None: cv2.line(F_show,(x1,y1),(x2,y2),(0,1,0)) cv2.line(hdr_show,(x1,y1),(x2,y2),(0,1,0)) y2 = y1 x2 = x1 cv2.circle(F_show,(x2,y2),2,(0,1,1)) cv2.circle(hdr_show,(x2,y2),2,(0,1,1)) imshow = np.concatenate([F_show,hdr_show],axis=0) imlist.append(imshow) else: print('no useable new feature') tracking_count += 1 if index%1 ==0: time_10 = time.time() clear_output() print('avg fps:',10/(time_10-time_begin)) time_begin = time.time() plt.figure(figsize=(12,6)) plt.subplot(1,2,1) plt.imshow(F_show) plt.subplot(1,2,2) plt.imshow(hdr_show) plt.show() .. parsed-literal:: Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). .. parsed-literal:: avg fps: 3.853698340503137 .. image:: output_5_2.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]*2) # 需要转为视频的图片的尺寸 out = cv2.VideoWriter(name,0x7634706d , fps, size) for frame in frame_list: frame2 = frame.copy() frame2[:320,...] = frame2[:320,...] * 255 frame2[320:,...] = frame2[320:,...] * 32 frame2 = frame2[:,:,[2,1,0]].astype(np.uint8) out.write(frame2) out.release() images_to_video(imlist,'./feature_tracking_'+key+'.mp4',Val_size=(640,320),Flip=False)