def script_visualizeScoreResults(test_output_file,gt_output_file,gt_data_output_file,out_file_html,rel_path,means,out_dir): data=np.load(gt_data_output_file); gt_label=np.load(gt_output_file); pred_label=np.load(test_output_file); print data.shape,gt_label.shape,pred_label.shape # print data.shape; im_paths=[];captions=[]; correct=0; for im_no in range(data.shape[0]): data_path=os.path.join(out_dir,str(im_no)+'_data.png'); # gt_path=os.path.join(out_dir,str(im_no)+'_gt.png'); # pred_path=os.path.join(out_dir,str(im_no)+'_pred.png'); # scipy.misc.imsave(data_path,reshapeMat(data[im_no],means)); visualize.saveMatAsImage(reshapeMat(data[im_no],means)/255,data_path); pred_label_curr=pred_label[im_no,0]; gt_label_curr=gt_label[im_no,0]; # visualize.saveMatAsImage(reshapeMat(gt[im_no],means),gt_path); # visualize.saveMatAsImage(reshapeMat(out[im_no],means),pred_path); im_paths.append([data_path.replace(rel_path[0],rel_path[1])]); if (pred_label_curr*gt_label_curr)>=0: correct=correct+1; captions.append(['Pred '+str(pred_label_curr)+' GT '+str(gt_label_curr)]); # if im_no==10: # break; print correct visualize.writeHTML(out_file_html,im_paths,captions,height=224,width=224);
def script_compareHashWithToyExperiment(params):
in_file = params.in_file;
num_hash_tables_all = params.num_hash_tables_all;
key_type = params.key_type;
out_file_indices = params.out_file_indices;
out_file_pres = params.out_file_pres;
out_file_html = params.out_file_html;
rel_path = params.rel_path;
[features_test,features_train,labels_test,labels_train,_,_,indices,_]=pickle.load(open(in_file,'rb'));
visualize.saveMatAsImage(indices,out_file_indices);
hammings=[];
for out_file_pre,num_hash_tables in zip(out_file_pres,num_hash_tables_all):
indices_hash = getIndicesHash(features_test,features_train,num_hash_tables,key_type);
visualize.saveMatAsImage(indices_hash,out_file_pre+'.png');
hamming=util.getHammingDistance(indices,indices_hash);
pickle.dump([indices_hash,indices,hamming],open(out_file_pre+'.p','wb'));
hammings.append(np.mean(hamming));
sizes = scipy.misc.imread(out_file_indices);
sizes = sizes.shape
im_files_html=[];
captions_html=[];
for idx,out_file_pre in enumerate(out_file_pres):
out_file_curr=out_file_pre+'.png'
key_str=str(key_type);
key_str=key_str.replace('<type ','').replace('>','');
caption_curr='NN Hash. Num Hash Tables: '+str(num_hash_tables_all[idx])+' '+'Hamming Distance: '+str(hammings[idx]);
im_files_html.append([out_file_indices.replace(rel_path[0],rel_path[1]),out_file_curr.replace(rel_path[0],rel_path[1])])
captions_html.append(['NN cosine',caption_curr]);
visualize.writeHTML(out_file_html,im_files_html,captions_html,sizes[0]/2,sizes[1]/2);
def script_saveHashAnalysisImages(params):
path_to_db = params.path_to_db
class_labels_map = params.class_labels_map
percents = params.percents
out_file_class_pre = params.out_file_class_pre
out_file_hash_simple = params.out_file_hash_simple
out_file_hash_byClass = params.out_file_hash_byClass
hashtable = params.hashtable
inc = params.inc
dtype = params.dtype
# in_file = params.in_file;
if not os.path.exists(out_file_class_pre + '.npz'):
mani = Tube_Manipulator(path_to_db)
mani.openSession()
ids = mani.selectMix((Tube.class_idx_pascal, TubeHash.hash_val),
(TubeHash.hash_table == hashtable, ))
mani.closeSession()
ids = np.array(ids, dtype=dtype)
np.savez(out_file_class_pre, ids)
ids = np.load(out_file_class_pre + '.npz')['arr_0']
# ids=np.load(in_file)['arr_0'];
counts_all, class_ids_breakdown = getClassIdsCount(ids[:, 0], ids[:, 1])
ranks = getDiscriminativeScore(counts_all)
sort_idx = np.argsort(ranks)
counts_all = [counts_all[idx] for idx in sort_idx]
class_ids_breakdown = [class_ids_breakdown[idx] for idx in sort_idx]
im_simple = getHashAnalysisIm(counts_all,
class_ids_breakdown,
inc=inc,
colorByClass=False)
im_byClass = getHashAnalysisIm(counts_all,
class_ids_breakdown,
inc=inc,
colorByClass=True)
visualize.saveMatAsImage(im_simple, out_file_hash_simple)
visualize.saveMatAsImage(im_byClass, out_file_hash_byClass)
counts_all_ravel = np.array([c for counts in counts_all for c in counts])
class_ids_breakdown_ravel = np.array(
[c for class_ids in class_ids_breakdown for c in class_ids])
class_id_pascal, class_idx_pascal = zip(*class_labels_map)
for class_id_idx, class_id in enumerate(class_idx_pascal):
frequency = counts_all_ravel[class_ids_breakdown_ravel == class_id]
out_file = out_file_class_pre + '_' + class_id_pascal[
class_id_idx] + '.png'
title = class_id_pascal[class_id_idx] + ' ' + str(class_id)
cum_freq, idx_perc = getCumulativeInfo(frequency, percents)
savePerClassCumulativeGraph(cum_freq / float(cum_freq[-1]), idx_perc,
percents, out_file, title)
def script_compareHashWithToyExperiment(params):
in_file = params.in_file
num_hash_tables_all = params.num_hash_tables_all
key_type = params.key_type
out_file_indices = params.out_file_indices
out_file_pres = params.out_file_pres
out_file_html = params.out_file_html
rel_path = params.rel_path
[
features_test, features_train, labels_test, labels_train, _, _,
indices, _
] = pickle.load(open(in_file, 'rb'))
visualize.saveMatAsImage(indices, out_file_indices)
hammings = []
for out_file_pre, num_hash_tables in zip(out_file_pres,
num_hash_tables_all):
indices_hash = getIndicesHash(features_test, features_train,
num_hash_tables, key_type)
visualize.saveMatAsImage(indices_hash, out_file_pre + '.png')
hamming = util.getHammingDistance(indices, indices_hash)
pickle.dump([indices_hash, indices, hamming],
open(out_file_pre + '.p', 'wb'))
hammings.append(np.mean(hamming))
sizes = scipy.misc.imread(out_file_indices)
sizes = sizes.shape
im_files_html = []
captions_html = []
for idx, out_file_pre in enumerate(out_file_pres):
out_file_curr = out_file_pre + '.png'
key_str = str(key_type)
key_str = key_str.replace('<type ', '').replace('>', '')
caption_curr = 'NN Hash. Num Hash Tables: ' + str(
num_hash_tables_all[idx]) + ' ' + 'Hamming Distance: ' + str(
hammings[idx])
im_files_html.append([
out_file_indices.replace(rel_path[0], rel_path[1]),
out_file_curr.replace(rel_path[0], rel_path[1])
])
captions_html.append(['NN cosine', caption_curr])
visualize.writeHTML(out_file_html, im_files_html, captions_html,
sizes[0] / 2, sizes[1] / 2)
def script_saveHashAnalysisImages(params):
path_to_db = params.path_to_db;
class_labels_map = params.class_labels_map;
percents = params.percents;
out_file_class_pre = params.out_file_class_pre;
out_file_hash_simple = params.out_file_hash_simple;
out_file_hash_byClass = params.out_file_hash_byClass;
hashtable = params.hashtable;
inc = params.inc;
dtype = params.dtype;
# in_file = params.in_file;
if not os.path.exists(out_file_class_pre+'.npz'):
mani=Tube_Manipulator(path_to_db);
mani.openSession();
ids=mani.selectMix((Tube.class_idx_pascal,TubeHash.hash_val),(TubeHash.hash_table==hashtable,));
mani.closeSession();
ids=np.array(ids,dtype=dtype);
np.savez(out_file_class_pre,ids);
ids=np.load(out_file_class_pre+'.npz')['arr_0'];
# ids=np.load(in_file)['arr_0'];
counts_all,class_ids_breakdown = getClassIdsCount(ids[:,0],ids[:,1]);
ranks = getDiscriminativeScore(counts_all);
sort_idx=np.argsort(ranks);
counts_all=[counts_all[idx] for idx in sort_idx];
class_ids_breakdown=[class_ids_breakdown[idx] for idx in sort_idx];
im_simple = getHashAnalysisIm(counts_all,class_ids_breakdown,inc=inc,colorByClass=False);
im_byClass = getHashAnalysisIm(counts_all,class_ids_breakdown,inc=inc,colorByClass=True);
visualize.saveMatAsImage(im_simple,out_file_hash_simple)
visualize.saveMatAsImage(im_byClass,out_file_hash_byClass)
counts_all_ravel=np.array([c for counts in counts_all for c in counts]);
class_ids_breakdown_ravel=np.array([c for class_ids in class_ids_breakdown for c in class_ids]);
class_id_pascal,class_idx_pascal = zip(*class_labels_map);
for class_id_idx,class_id in enumerate(class_idx_pascal):
frequency = counts_all_ravel[class_ids_breakdown_ravel==class_id]
out_file=out_file_class_pre+'_'+class_id_pascal[class_id_idx]+'.png'
title=class_id_pascal[class_id_idx]+' '+str(class_id)
cum_freq,idx_perc=getCumulativeInfo(frequency,percents)
savePerClassCumulativeGraph(cum_freq/float(cum_freq[-1]),idx_perc,percents,out_file,title)
def script_visualizeSegResults(pred_file,gt_output_file,gt_data_output_file,out_file_html,rel_path,means,out_dir): data=np.load(gt_data_output_file); gt= np.load(gt_output_file); out = np.load(pred_file); print data.shape; im_paths=[];captions=[]; for im_no in range(data.shape[0]): print im_no; data_path=os.path.join(out_dir,str(im_no)+'_data.png'); gt_path=os.path.join(out_dir,str(im_no)+'_gt.png'); pred_path=os.path.join(out_dir,str(im_no)+'_pred.png'); # scipy.misc.imsave(data_path,reshapeMat(data[im_no],means)); visualize.saveMatAsImage(reshapeMat(data[im_no],means)/255,data_path); visualize.saveMatAsImage(reshapeMat(gt[im_no],means),gt_path); visualize.saveMatAsImage(reshapeMat(out[im_no],means),pred_path); im_paths.append([data_path.replace(rel_path[0],rel_path[1]),gt_path.replace(rel_path[0],rel_path[1]),pred_path.replace(rel_path[0],rel_path[1])]); captions.append(['im','mask_gt','mask_pred']); # if im_no==10: # break; visualize.writeHTML(out_file_html,im_paths,captions,height=224,width=224);
def main():
# # out_dir='/disk2/marchExperiments/deep_proposals/testing_3_28_2/images';
# # out_dir='/disk2/marchExperiments/deep_proposals/new_design/training_human/images';
# out_dir='/disk2/aprilExperiments/dual_flow/onlyHuman/images';
# out_dir_old='/disk2/aprilExperiments/dual_flow/onlyHuman/images_old';
# img_pre='img';
# ims=[file_curr for file_curr in os.listdir(out_dir) if file_curr.startswith(img_pre) and not file_curr.endswith('.npy')];
# img_pre='img';
# mask_pre='pred_mask';
# out_file_html=os.path.join(out_dir,'visualize.html');
# rel_path_old=['/disk2','../../../..']
# rel_path=['/disk2','../../../../..']
# img_paths=[];
# captions=[];
# for file_curr in ims:
# print file_curr
# file_curr_split=file_curr.split('_');
# file_curr_pre='_'.join(file_curr_split[:4]);
# # print file_curr_pre;
# # print file_curr_split
# # print file_curr;
# im_path_old=[os.path.join(out_dir_old,x) for x in os.listdir(out_dir_old) if x.startswith(file_curr_pre)][0];
# im_path = os.path.join(out_dir,file_curr);
# # print im_path_old;
# # print os.path.exists(im_path_old);
# # raw_input();
# mask_path=im_path.replace(img_pre,mask_pre);
# mask_path_old=im_path_old.replace(img_pre,mask_pre);
# im=scipy.misc.imread(mask_path);
# mask_path=mask_path+'_heat.png'
# visualize.saveMatAsImage(im[:,:,0],mask_path)
# im=scipy.misc.imread(mask_path_old);
# mask_path_old=mask_path_old+'_heat.png'
# visualize.saveMatAsImage(im[:,:,0],mask_path_old)
# img_path=im_path;
# img_paths.append([img_path.replace(rel_path[0],rel_path[1]),mask_path.replace(rel_path[0],rel_path[1]),im_path_old.replace(rel_path_old[0],rel_path_old[1]),mask_path_old.replace(rel_path_old[0],rel_path_old[1])]);
# captions.append([img_path[img_path.rindex('/')+1:]+' flow',mask_path[mask_path.rindex('/')+1:]+' flow',im_path_old[im_path_old.rindex('/')+1:]+' old',mask_path_old[mask_path_old.rindex('/')+1:]+' old']);
# visualize.writeHTML(out_file_html,img_paths,captions,height=224,width=224);
# return
out_dir='/disk2//aprilExperiments/headC_160/noFlow_gaussian_all/images_test';
out_file_html=os.path.join(out_dir,'visualize.html');
rel_path=['/disk2','../../../..']
img_paths=[];
captions=[];
img_pre='img';
mask_pre='pred_mask';
imgs_all=[file_curr for file_curr in os.listdir(out_dir) if file_curr.startswith(img_pre) and not file_curr.endswith('.npy')];
lists=[[],[],[],[]];
caption_lists=[[],[],[],[]]
for file_curr in imgs_all:
file_curr_split=file_curr.split('_');
print file_curr_split
if file_curr_split[3]=='pos':
if file_curr_split[4]=='correct.png':
lists[0].append(file_curr);
caption_lists[0].append('Positive Correct')
else:
lists[1].append(file_curr);
caption_lists[1].append('Positive Wrong')
else:
if file_curr_split[4]=='correct.png':
lists[2].append(file_curr);
caption_lists[2].append('Negative Correct')
else:
lists[3].append(file_curr);
caption_lists[3].append('Negative Wrong')
# print file_curr_split
# print file_curr;
# raw_input();
lists=[file_curr for file_curr_list in lists for file_curr in file_curr_list];
caption_lists=[file_curr for file_curr_list in caption_lists for file_curr in file_curr_list];
for idx,file_curr in enumerate(lists):
img_path=os.path.join(out_dir,file_curr);
mask_path=img_path.replace(img_pre,mask_pre);
# img_path_old=img_path.replace(out_dir,out_dir_old);
# mask_path_old=mask_path.replace(out_dir,out_dir_old);
im=scipy.misc.imread(mask_path);
mask_path=mask_path+'_heat.png'
# visualize.showMat(im[:,:,0]);
visualize.saveMatAsImage(im[:,:,0],mask_path)
# img_paths.append([img_path.replace(rel_path[0],rel_path[1]),mask_path.replace(rel_path[0],rel_path[1]),img_path_old.replace(rel_path[0],rel_path[1]),mask_path_old.replace(rel_path[0],rel_path[1])]);
# captions.append([caption_lists[idx]+' img',caption_lists[idx]+' mask',caption_lists[idx]+'OLD img',caption_lists[idx]+'OLD mask']);
img_paths.append([img_path.replace(rel_path[0],rel_path[1]),mask_path.replace(rel_path[0],rel_path[1])]);
captions.append([caption_lists[idx]+' img',caption_lists[idx]+' mask']);
visualize.writeHTML(out_file_html,img_paths,captions,height=224,width=224);
# img_row_curr=[];
# img_curr=os.path.join(out_dir,'img_'+str(i)+'.png');
# img_row_curr.append(img_curr.replace(rel_path[0],rel_path[1]));
# for j in range(1,3):
# img_curr=os.path.join(out_dir,'img_crop_'+str(j)+'_'+str(i)+'.png');
# img_row_curr.append(img_curr.replace(rel_path[0],rel_path[1]));
# img_paths.append(img_row_curr);
# captions.append(['','','']);
#
return
out_dir='/disk2/marchExperiments/deep_proposals/checkNegScaling';
out_file_html=os.path.join(out_dir,'visualize.html');
rel_path=['/disk2','../../../..']
img_paths=[];
captions=[];
for i in range(1,11):
img_row_curr=[];
img_curr=os.path.join(out_dir,'img_'+str(i)+'.png');
img_row_curr.append(img_curr.replace(rel_path[0],rel_path[1]));
for j in range(1,3):
img_curr=os.path.join(out_dir,'img_crop_'+str(j)+'_'+str(i)+'.png');
img_row_curr.append(img_curr.replace(rel_path[0],rel_path[1]));
img_paths.append(img_row_curr);
captions.append(['','','']);
visualize.writeHTML(out_file_html,img_paths,captions);
return
text_input='/disk2/februaryExperiments/deep_proposals/positive_data.txt';
test_output_file='/disk2/februaryExperiments/deep_proposals/model_no_seg_test.npy';
gt_output_file='/disk2/februaryExperiments/deep_proposals/model_no_seg_gt.npy';
gt_data_output_file='/disk2/februaryExperiments/deep_proposals/model_no_seg_gt_data.npy';
# out_dir='/disk2/februaryExperiments/deep_proposals/model_no_score_test';
out_dir='/disk2/marchExperiments/deep_proposals/model_no_seg_test';
test_output_file='/disk2/marchExperiments/deep_proposals/debugging_nan/pred.npy';
gt_output_file='/disk2/marchExperiments/deep_proposals/debugging_nan/label.npy';
gt_data_output_file='/disk2/marchExperiments/deep_proposals/debugging_nan/im.npy';
out_dir='/disk2/marchExperiments/deep_proposals/model_seg_tan_test';
util.mkdir(out_dir);
out_file_html=os.path.join(out_dir,'visualize.html');
rel_path=['/disk2','../../../..'];
means=[122,117,104]
script_visualizeSegResults(test_output_file,gt_output_file,gt_data_output_file,out_file_html,rel_path,means,out_dir)
# script_visualizeSegResults(gt_data_output_file)
# script_visualizeScoreResults(test_output_file,gt_output_file,gt_data_output_file,out_file_html,rel_path,means,out_dir)
# lines=util.readLinesFromFile(text_input);
# lines=lines[:100];
# out=np.load(test_output_file);
# gt=np.load(gt_output_file);
return
out_files=[];
gt_files=[];
mask_files=[];
print out.shape,gt.shape
for im_no in range(out.shape[0]):
print im_no
im=out[im_no];
im=im.reshape((im.shape[1],im.shape[2]));
# print im.shape
out_file=os.path.join(out_dir,str(im_no+1)+'.png');
visualize.saveMatAsImage(im,out_file);
out_files.append(out_file);
im=gt[im_no];
im=im.reshape((im.shape[1],im.shape[2]));
# print im.shape
out_file=os.path.join(out_dir,str(im_no+1)+'_mask.png');
visualize.saveMatAsImage(im,out_file);
gt_files.append(out_file);
out_file_html=os.path.join(out_dir,'visualize.html');
rel_path=['/disk2','../../../..'];
im_paths=[];captions=[];
for idx,line in enumerate(lines):
im_path=line[:line.index(' ')];
# mask_path=line[line.index(' '):];
# mask=scipy.misc.imread(mask_path);
im_path=im_path.replace(rel_path[0],rel_path[1]);
im_paths.append([im_path,out_files[idx].replace(rel_path[0],rel_path[1]),
gt_files[idx].replace(rel_path[0],rel_path[1])]);
captions.append(['im','pred','gt']);
visualize.writeHTML(out_file_html,im_paths,captions,height=224,width=224);
def main():
# dir_flo='/disk2/aprilExperiments/flo_subset_transfer';
# dir_model_results='/disk2/aprilExperiments/flo_subset_predictions';
# dir_meta_im='/disk2/marchExperiments/youtube';
# util.mkdir(dir_model_results);
# flo_files=[file_curr for file_curr in os.listdir(dir_flo) if file_curr.endswith('.flo')];
# img_files_all=[];
# for file_curr in flo_files:
# video_name=file_curr[:file_curr.index('.')]
# img_file=os.path.join(dir_meta_im,video_name,'images_transfer',file_curr.replace('.flo','.jpg'));
# img_files_all.append(img_file+' 1');
# util.writeFile(os.path.join(dir_model_results,'test.txt'),img_files_all);
# # /disk2/aprilExperiments/flo_subset_predictions/test.txt /home/maheenrashid/Downloads/debugging_jacob/optical_flow_prediction/examples/opticalflow/final.caffemodel 1
# return
dir_flo='/disk2/aprilExperiments/flo_subset_transfer';
dir_meta_im='/disk2/marchExperiments/youtube';
out_dir_flo_im='/disk2/aprilExperiments/flo_im';
out_dir_tif_im='/disk2/aprilExperiments/tif_im';
util.mkdir(out_dir_flo_im);
util.mkdir(out_dir_tif_im);
out_file_html='/disk2/aprilExperiments/flo_im_visualize.html';
rel_path_img=['/disk2','../../../..'];
rel_path_tif=['/disk2','../../../..'];
rel_path_flo=['/disk2','../../..'];
flo_files=[os.path.join(dir_flo,file_curr) for file_curr in os.listdir(dir_flo) if file_curr.endswith('.flo')];
# flo_files=flo_files[:10];
img_paths_all=[];
captions_all=[]
for flo_file in flo_files:
flo_just_name=flo_file[flo_file.rindex('/')+1:flo_file.rindex('.')];
video_name=flo_just_name[:flo_just_name.index('.')];
flo_file_np=os.path.join(out_dir_flo_im,flo_just_name+'.npy');
if os.path.exists(flo_file_np):
continue;
print flo_file
try:
flo=util.readFlowFile(flo_file,flip=False)
except:
print 'ERROR';
continue;
np.save(flo_file_np,flo);
print flo.shape
out_flo_name_x=os.path.join(out_dir_flo_im,flo_just_name+'_x.png');
visualize.saveMatAsImage(flo[:,:,0],out_flo_name_x);
out_flo_name_y=os.path.join(out_dir_flo_im,flo_just_name+'_y.png');
visualize.saveMatAsImage(flo[:,:,1],out_flo_name_y);
jpg_name=os.path.join(dir_meta_im,video_name,'images_transfer',flo_just_name+'.jpg');
tif_name=os.path.join(dir_meta_im,video_name,'images_transfer',flo_just_name+'.tif');
tif=scipy.misc.imread(tif_name);
print tif.shape,np.min(tif),np.max(tif);
tif_just_name=flo_just_name;
out_tif_name_x=os.path.join(out_dir_tif_im,tif_just_name+'_x.png');
visualize.saveMatAsImage(tif[:,:,0],out_tif_name_x);
out_tif_name_y=os.path.join(out_dir_tif_im,tif_just_name+'_y.png');
visualize.saveMatAsImage(tif[:,:,1],out_tif_name_y);
img_paths_all.append([jpg_name.replace(rel_path_img[0],rel_path_img[1]),out_flo_name_x.replace(rel_path_flo[0],rel_path_flo[1]),out_flo_name_y.replace(rel_path_flo[0],rel_path_flo[1]),
out_tif_name_x.replace(rel_path_tif[0],rel_path_tif[1]),out_tif_name_y.replace(rel_path_tif[0],rel_path_tif[1])]);
captions_all.append([flo_just_name,'x_flo','y_flo','cluster_x','cluster_y']);
visualize.writeHTML(out_file_html,img_paths_all,captions_all,300,300);
def main(): # out_dir='/disk2/aprilExperiments/headC_160/figuring_test/im_pred'; # seg_file='1_1_seg.png' # seg_curr=np.load(os.path.join(out_dir,seg_file))[0]; # print seg_curr[:,0]; # seg_curr=np.load(os.path.join(out_dir,'1_2_seg.png'))[0]; # print seg_curr[:,0]; # # min_value=np.min(seg_curr); # # print min_value # # bin_curr=np.sum(seg_curr==min_value,axis=0); # # print bin_curr.shape; # # print bin_curr # # idx_emp=np.where(bin_curr==seg_curr.shape[0]); # # print len(idx_emp); # # print idx_emp # # print np.max(idx_emp); # # bin_curr=np.sum(seg_curr==0,axis=1); # # print bin_curr.shape; # # idx_emp=np.where(bin_curr==seg_curr.shape[1]); # # print len(idx_emp); # # print np.max(idx_emp); # # print idx_emp # return im_path='/disk2/ms_coco/train2014/COCO_train2014_000000460565.jpg'; out_dir='/disk2/aprilExperiments/headC_160/figuring_test/im_pred_score'; im_size_org=(427,640); score_list=getScoresList(out_dir); # out_dir_new='/disk2/aprilExperiments/headC_160/figuring_test/im_pred_score'; score_all=np.load(os.path.join(out_dir,'all_score.npy'))[0][0]; print score_all.shape for i in range(score_all.shape[0]): for j in range(score_all.shape[1]): score_bef=score_list[(i+1,j+1)]; score_now=score_all[i,j]; print score_bef,score_now,i,j print np.abs(score_bef-score_now) assert np.abs(score_bef-score_now)<0.00001; visualize.saveMatAsImage(score_all,os.path.join(out_dir,'new_mat.png')) return scores=score_list.values(); idx_max=np.argmax(scores); idx_sort=np.argsort(scores)[::-1]; # print idx_sort # return # [::-1]; # print idx_sort[0],idx_max print scores idx_max=idx_sort[19]; print scores[idx_max] print idx_max,scores[idx_max]; # return keys=score_list.keys(); [r,c]=zip(*keys); max_r=max(r); max_c=max(c); max_idx=keys[idx_max]; pix_starts=[]; good_ones=[max_idx]; im=Image.open(im_path); draw = ImageDraw.Draw(im) for r_idx,c_idx in good_ones: print r_idx,c_idx; pix_start=getPixStart(r_idx-1,c_idx-1,im_size_org,max_r,max_c); pix_starts.append(pix_start); seg_curr=os.path.join(out_dir,str(r_idx)+'_'+str(c_idx)+'_seg.npy'); seg_curr=np.load(seg_curr)[0]; heatmap=getHeatMap(seg_curr); im=np.array(im); im=im*0.5; heatmap=heatmap*0.5; print pix_start # pix_start=[pix_start_curr-160 for pix_start_curr in pix_start] im_rel=im[pix_start[0]:pix_start[0]+160,pix_start[1]:pix_start[1]+160] im[pix_start[0]:pix_start[0]+160,pix_start[1]:pix_start[1]+160]=im_rel+heatmap[:min(heatmap.shape[0],im_rel.shape[0]),:min(heatmap.shape[1],im_rel.shape[1])]; im=Image.fromarray(np.uint8(im)); for pix_start in pix_starts: draw.rectangle([pix_start[1],pix_start[0],pix_start[1]+160,pix_start[0]+160]); im.save(os.path.join(out_dir,'check.png')); return for r_idx in range(0,max_rows,9): row_curr=[]; scores_row=[]; for c_idx in range(0,max_cols,9): seg_curr=np.load(os.path.join(out_dir,str(r_idx+1)+'_'+str(c_idx+1)+'_seg.png'))[0]; seg_curr[seg_curr<0]=0; score_curr=np.load(os.path.join(out_dir,str(r_idx+1)+'_'+str(c_idx+1)+'_score.png'))[0]; print seg_curr.shape,score_curr; row_curr.append(seg_curr); scores_row.append(score_curr); scores.append(scores_row); row_curr=np.hstack(tuple(row_curr)); if r_idx==0: img_yet=row_curr; else: img_yet=np.vstack((img_yet,row_curr)); print img_yet.shape visualize.saveMatAsImage(img_yet,os.path.join(out_dir,'full_img.png'))
def main():
in_dir='/disk2/aprilExperiments/testing_neg_torch';
# check_dir=os.path.join(in_dir,'check_crops_invalid');
# out_file_html='visualize_crops.html';
visualize.writeHTMLForFolder(in_dir,'.png',200,200);
return
in_dir='/disk2/aprilExperiments/testing_neg_fixed_test';
img_path=os.path.join(in_dir,'1.png');
crop_path_pos=img_path.replace('.png','_crop_pos.npy');
crop_path_neg=img_path.replace('.png','_crop_neg.npy');
bbox_path=img_path.replace('.png','_bbox.npy');
im=scipy.misc.imread(img_path);
bbox=np.load(bbox_path);
crop_box_pos=np.load(crop_path_pos);
crop_box_neg=np.load(crop_path_neg);
# crop_box_pos=np.array(crop_path_pos,dty);
# crop_box_neg=np.array(crop_path_neg,dty);
out_file_neg_box=img_path.replace('.png','_box_neg.png');
out_file_pos_box=img_path.replace('.png','_box_pos.png');
im=Image.open(img_path);
# draw=Image.ImageDraw(im);
# draw = ImageDraw.Draw(im)
# for crop_box in crop_box_neg:
# # draw.rectangle([(bbox[box_no,0],bbox[box_no,1]),(bbox[box_no,0]+bbox[box_no,2],bbox[box_no,1]+bbox[box_no,3])],outline=(255,255,255))
# draw.rectangle([(crop_box[0],crop_box[1]),(crop_box[2],crop_box[3])],outline=(0,0,255))
# box_no=0;
# draw.rectangle([(bbox[box_no,0],bbox[box_no,1]),(bbox[box_no,0]+bbox[box_no,2],bbox[box_no,1]+bbox[box_no,3])],outline=(255,255,255))
# del draw
# print out_file_neg_box
# im.save(out_file_neg_box, "PNG");
to_plot=[];
print crop_box_pos.shape
# min_x_req=bbox[0,0]-224;
# min_y_req=bbox[0,1]-224;
# max_x_req=bbox[0,0]+bbox[0,2]+224;
# max_y_req=bbox[0,1]+bbox[0,3]+224;
center_x=bbox[0,0]+bbox[0,2]/2;
center_y=bbox[0,1]+bbox[0,3]/2;
min_x_req=center_x-224;
min_y_req=center_y-224;
max_x_req=center_x+224;
max_y_req=center_y+224;
for idx_crop_box_curr,crop_box_curr in enumerate(crop_box_pos):
# if idx_crop_box_curr%100==0:
# print idx_crop_box_curr;
[min_x,min_y,max_x,max_y]=list(crop_box_curr);
if min_x>=min_x_req and min_y>=min_y_req and max_x<=max_x_req and max_y<=max_y_req:
to_plot.append(idx_crop_box_curr);
print len(to_plot)
# draw = ImageDraw.Draw(im);
crop_check_dir=os.path.join(in_dir,'check_crops_invalid');
util.mkdir(crop_check_dir);
print crop_check_dir;
# return
im_np=np.array(im);
for idx_idx_box,idx_box in enumerate(range(len(crop_box_neg))):
if idx_idx_box%1000==0:
print idx_idx_box;
crop_box=crop_box_neg[idx_box];
im_curr=im_np[crop_box[1]:crop_box[3],crop_box[0]:crop_box[2],:];
out_file=os.path.join(crop_check_dir,str(idx_idx_box)+'.png');
scipy.misc.imsave(out_file,im_curr);
# draw.rectangle([(crop_box[0],crop_box[1]),(crop_box[2],crop_box[3])],outline=(0,0,255))
# box_no=0;
# draw.rectangle([(bbox[box_no,0],bbox[box_no,1]),(bbox[box_no,0]+bbox[box_no,2],bbox[box_no,1]+bbox[box_no,3])],outline=(255,255,255))
# del draw
# print out_file_pos_box
# im.save(out_file_pos_box, "PNG");
return
im=scipy.misc.imread(img_path);
bbox=np.load(bbox_path);
crop_box_pos=np.load(crop_path_pos);
crop_box_neg=np.load(crop_path_neg);
print bbox.shape
ys=bbox[:,1];
print ys.shape
print np.argmax(ys);
print bbox[np.argmax(ys)]
# return
crop_box_pos=np.array(crop_box_pos,dtype='int');
crop_box_neg=np.array(crop_box_neg,dtype='int');
print crop_box_pos.shape
print crop_box_neg.shape
new_im=np.zeros((im.shape[0],im.shape[1]));
for crop_box_curr in crop_box_neg:
# print crop_box_curr
new_im[crop_box_curr[1]:crop_box_curr[3],crop_box_curr[0]:crop_box_curr[2]]=new_im[crop_box_curr[1]:crop_box_curr[3],crop_box_curr[0]:crop_box_curr[2]]+1;
im_new=new_im;
visualize.saveMatAsImage(im_new,os.path.join(in_dir,'heat_map_neg.png'));
new_im=np.zeros((im.shape[0],im.shape[1]));
for crop_box_curr in crop_box_pos:
# print crop_box_curr
new_im[crop_box_curr[1]:crop_box_curr[3],crop_box_curr[0]:crop_box_curr[2]]=new_im[crop_box_curr[1]:crop_box_curr[3],crop_box_curr[0]:crop_box_curr[2]]+1;
im_new=new_im;
print im_new.shape
visualize.saveMatAsImage(im_new,os.path.join(in_dir,'heat_map_pos.png'));
return
script_makeNegImages();
return
in_dir='/disk2/aprilExperiments/testing_neg';
img_path=os.path.join(in_dir,'71.png');
crop_path=img_path.replace('.png','_crop.npy');
bbox_path=img_path.replace('.png','_bbox.npy');
max_dim=128;
tolerance=32;
bbox=np.load(bbox_path);
crop_box=np.load(crop_path);
im = Image.open(img_path);
box_to_plot,scale,new_tolerance=getCorrespondingTolerance(bbox[0],max_dim,tolerance);
# print (center_box,center_crop)
center_box=[bbox[0,0]+bbox[0,2]/2.0,bbox[0,1]+bbox[0,3]/2.0];
center_crop=[crop_box[0]+(crop_box[2]-crop_box[0])/2.0,crop_box[1]+(crop_box[3]-crop_box[1])/2.0];
print center_box,center_crop
dist_centers=np.sqrt(np.sum(np.power(np.array(center_box)-np.array(center_crop),2)));
print (new_tolerance,dist_centers)
if dist_centers<new_tolerance:
scaleTest(bbox[0],crop_box,[0.5,2]);
def main():
dir_meta='/disk2/aprilExperiments/deep_proposals/flow_all_humans/';
dir_other_images='/disk2/aprilExperiments/deep_proposals/flow_neg/flo_subset_for_pos_cropped';
dir_pos=os.path.join(dir_meta,'results');
out_file_match=os.path.join(dir_meta,'match.txt');
dir_curr='/disk2/aprilExperiments/deep_proposals/flow_neg_subset_debug/results';
dir_small_im=dir_curr+'_images'
dir_full_crops='/disk2/aprilExperiments/deep_proposals/flow_neg/flo_subset_for_pos_cropped'
list_files=[os.path.join(dir_curr,file_curr) for file_curr in os.listdir(dir_curr) if file_curr.endswith('.h5')]
img_files=[];
h5_names=[];
for list_file in list_files:
img_file=util.readLinesFromFile(list_file.replace('.h5','.txt'))[0].strip();
h5_names.append(list_file[list_file.rindex('/')+1:list_file.rindex('.')]);
img_file=img_file[img_file.rindex('/')+1:];
img_files.append(img_file);
# h5_names_sorted=[int(name) for name in h5_names];
# h5_names_sorted.sort();
out_file_html=os.path.join(dir_full_crops,'comparison.html');
imgs_full_crops=[file_curr for file_curr in os.listdir(dir_full_crops) if file_curr.endswith('.png')];
img_paths=[];
captions=[];
rel_path=['/disk2','../../../..'];
for img_file in imgs_full_crops:
img_file_full=os.path.join(dir_full_crops,img_file);
print img_files[0],img_file
# h5_name_rel=int(h5_names[img_files.index(img_file)])
img_file_small=os.path.join(dir_small_im,h5_names[img_files.index(img_file)]+'.jpg');
img_paths.append([img_file_full.replace(rel_path[0],rel_path[1]),img_file_small.replace(rel_path[0],rel_path[1])]);
captions.append(['Crop full flow','Flow on Cropped']);
util.writeHTML(out_file_html,img_paths,captions,240,240);
# # util.mkdir(out_dir_matches);
# # writeh5ImgFile(dir_pos,out_file_match);
# img_files_to_find=[file_curr for file_curr in os.listdir(dir_other_images) if file_curr.endswith('.png')];
# lines=util.readLinesFromFile(out_file_match);
# just_imgs=[line[line.rindex('/')+1:] for line in lines];
# idx_matches=[];
# # idx_matches=[just_imgs.index(file_curr) for file_curr in img_files_to_find];
# # h5_files=[lines[idx_match][:lines[idx_match].index(' ')] for idx_match in idx_matches];
# # print h5_files[0],len(h5_files);
# # for file_curr in h5_files:
# for img_file_to_find in img_files_to_find:
# if img_file_to_find in just_imgs:
# idx_match=just_imgs.index(img_file_to_find)
# else:
# continue;
# file_curr=lines[idx_match][:lines[idx_match].index(' ')]
# out_file_curr=os.path.join(out_dir_matches,file_curr[file_curr.rindex('/')+1:]);
# shutil.copyfile(file_curr,out_file_curr);
# file_curr=file_curr.replace('.h5','.txt');
# out_file_curr=out_file_curr.replace('.h5','.txt');
# shutil.copyfile(file_curr,out_file_curr);
# # for file_curr in img_files_to_find:
# # idx_match=lines.index(file_curr);
# # idx_matches.append(idx_match)
# return
# dir_curr='/disk2/aprilExperiments/deep_proposals/flow_neg/flo_subset_for_pos_cropped';
# visualize.writeHTMLForFolder(dir_curr,'.jpg');
return
dir_meta='/disk2/aprilExperiments/deep_proposals/flow_neg/';
dir_neg=os.path.join(dir_meta,'results');
out_file_rec=os.path.join(dir_meta,'ims_to_analyze.npz')
out_file_match=os.path.join(dir_meta,'match.txt');
lines=util.readLinesFromFile('/disk2/aprilExperiments/deep_proposals/positives_person.txt');
clusters_file='/home/maheenrashid/Downloads/debugging_jacob/optical_flow_prediction_test/examples/opticalflow/clusters.mat';
arrs=np.load(out_file_rec)
negs=arrs['negs'];
h5_files=[line[:line.index(' ')] for line in negs];
dir_flo_im=os.path.join(dir_meta,'flo_subset_for_pos');
util.mkdir(dir_flo_im);
replace_paths=['',''];
srf.script_saveFloAsNpPred(clusters_file,h5_files,dir_flo_im,replace_paths)
return
print lines[0];
imgs=[line[:line.index(' ')] for line in lines];
num_to_keep=100;
random.shuffle(imgs);
imgs=imgs[:num_to_keep];
imgs_neg=[img[img.rindex('/')+1:img.rindex('_')] for img in imgs];
print imgs_neg[0]
lines_neg=util.readLinesFromFile(out_file_match);
print 'lines_neg',len(lines_neg);
print lines_neg[0];
rel_imgs=[];
for idx_lines,line in enumerate(lines_neg):
# if idx_lines%100==0:
# print idx_lines;
rel_img_part=line[line.rindex(' ')+1:];
rel_img_part=rel_img_part[rel_img_part.rindex('/')+1:rel_img_part.rindex('.')];
rel_imgs.append(rel_img_part);
print len(rel_imgs);
print rel_imgs[0];
idx_rel=[];
paths_rel=[];
for idx_curr,img_curr in enumerate(imgs_neg):
print idx_curr
idx=rel_imgs.index(img_curr);
idx_rel.append(idx);
paths_rel.append(lines_neg[idx]);
imgs=np.array(imgs);
paths_rel=np.array(paths_rel);
np.savez(out_file_rec,pos=imgs,negs=paths_rel);
# lines=[];
# h5_files=[os.path.join(dir_neg,file_curr) for file_curr in os.listdir(dir_neg) if file_curr.endswith('.h5')];
# print len(h5_files)
# for idx_file_curr,file_curr in enumerate(h5_files):
# if idx_file_curr%100==0:
# print idx_file_curr
# img_file=util.readLinesFromFile(file_curr.replace('.h5','.txt'))[0].strip();
# # print file_curr,img_file
# lines.append(file_curr+' '+img_file);
# util.writeFile(out_file_match,lines);
return
dir_meta='/disk2/aprilExperiments/flo_debug/results'
h5_file=os.path.join(dir_meta,'0.h5');
clusters_file='/home/maheenrashid/Downloads/debugging_jacob/optical_flow_prediction_test/examples/opticalflow/clusters.mat';
img_file = util.readLinesFromFile(h5_file.replace('.h5','.txt'))[0].strip();
with h5py.File(clusters_file,'r') as hf:
# print hf.keys();
C=np.array(hf.get('C'));
C=C.T
flow_mat = getFlowMat(h5_file,C);
im=scipy.misc.imread(img_file);
flow_mat=cv2.resize(flow_mat,(im.shape[1],im.shape[0]));
# print flow_mat.shape,im.shape
out_file_mat=h5_file.replace('.h5','.mat');
scipy.io.savemat(out_file_mat,{'N':flow_mat});
out_file_im=os.path.join(dir_meta,'from_mat.png');
string="img_file='"+img_file+"';flo_mat='"+out_file_mat+"';out_file='"+out_file_im+"';"
print string
return
out_dir='/disk2/aprilExperiments/flo_all_predictions/results'
dir_flo_im='/disk2/aprilExperiments/flo_all_predictions/flo_npy';
util.mkdir(dir_flo_im);
clusters_file='/home/maheenrashid/Downloads/debugging_jacob/optical_flow_prediction_test/examples/opticalflow/clusters.mat';
vision3_path='/disk2/marchExperiments';
hpc_path='/group/leegrp/maheen_data';
h5_files=[os.path.join(out_dir,file_curr) for file_curr in os.listdir(out_dir) if file_curr.endswith('.h5')];
img_files=[];
for h5_file in h5_files[:100]:
img_file=util.readLinesFromFile(h5_file.replace('.h5','.txt'))[0].strip();
img_files.append(img_file);
script_saveFloAsNpPred(clusters_file,h5_files,img_files,dir_flo_im)
return
dir_pred='/disk2/aprilExperiments/flo_all_predictions'
dir_im_meta='/disk2/marchExperiments/youtube'
file_name='youtube_list_flo_paths.txt';
test_file=os.path.join(dir_pred,'test.txt');
flo_files=util.readLinesFromFile(os.path.join(dir_pred,file_name));
print len(flo_files);
random.shuffle(flo_files);
im_paths=[];
for flo_file in flo_files:
flo_name=flo_file[flo_file.rindex('/')+1:];
video_name=flo_name[:flo_name.index('.')];
im_path=os.path.join(dir_im_meta,video_name,'images_transfer',flo_name[:flo_name.rindex('.')]+'.jpg');
im_paths.append(im_path+' 1');
# assert os.path.exists(im_path);
print len(im_paths);
print im_paths[0];
util.writeFile(test_file,im_paths);
print test_file;
return
dir_flo_pred='/disk2/aprilExperiments/flo_subset_predictions/pred_flo_im';
dir_flo_gt='/disk2/aprilExperiments/flo_im';
dir_im_meta='/disk2/marchExperiments/youtube'
out_dir_debug='/disk2/aprilExperiments/flo_debug';
flo_names=[file_curr for file_curr in os.listdir(dir_flo_pred) if file_curr.endswith('.npy')];
errors=[];
for flo_name in flo_names[:100]:
video_name=flo_name[:flo_name.index('.')];
im_path=os.path.join(dir_im_meta,video_name,'images_transfer',flo_name[:flo_name.rindex('.')]+'.jpg');
gt_flo_file=os.path.join(dir_flo_gt,flo_name)
# print gt_flo_file
gt_flo=np.load(gt_flo_file);
# print im_path.replace('/disk2','vision3.cs.ucdavis.edu:1000');
im=scipy.misc.imread(im_path);
gt_flo_cv2=bringToImageFrame(gt_flo,im.shape);
gt_flo_sp=bringToImageFrameSP(gt_flo,im.shape);
tol=1.0
error=np.sum(abs(gt_flo_sp-gt_flo_cv2)<tol)/float(gt_flo_sp.size);
errors.append(error);
print min(errors),max(errors),np.mean(errors);
return
out_file=os.path.join(out_dir_debug,flo_name);
out_file_mat=os.path.join(out_dir_debug,flo_name[:flo_name.rindex('.')]+'.mat');
scipy.io.savemat(out_file_mat,{'img':gt_flo});
print out_file
np.save(out_file,gt_flo);
out_file_gt_flo=os.path.join(out_dir_debug,'flo_gt_no_rescale.png');
print out_file_gt_flo
return
for flo_name in flo_names[:1]:
video_name=flo_name[:flo_name.index('.')];
im_path=os.path.join(dir_im_meta,video_name,'images_transfer',flo_name[:flo_name.rindex('.')]+'.jpg');
im=scipy.misc.imread(im_path);
# print im.shape
gt_flo=np.load(os.path.join(dir_flo_gt,flo_name));
pred_flo=np.load(os.path.join(dir_flo_pred,flo_name));
# print gt_flo.shape
# print pred_flo.shape
# pred_flo=bringToImageFrame(pred_flo,im.shape);
mag_pred_flo_bef=np.power(np.power(pred_flo[:,:,0],2)+np.power(pred_flo[:,:,1],2),0.5)
pred_flo=cv2.resize(pred_flo,(im.shape[1],im.shape[0]));
mag_pred_flo_aft=np.power(np.power(pred_flo[:,:,0],2)+np.power(pred_flo[:,:,1],2),0.5)
mag_gt_flo_bef=np.power(np.power(gt_flo[:,:,0],2)+np.power(gt_flo[:,:,1],2),0.5)
gt_flo=bringToImageFrame(gt_flo,im.shape);
mag_gt_flo_aft=np.power(np.power(gt_flo[:,:,0],2)+np.power(gt_flo[:,:,1],2),0.5)
print pred_flo.shape,gt_flo.shape
# pred_flo = cv2.resize(pred_flo, (im.shape[1],im.shape[0]))
# gt_flo = cv2.resize(gt_flo, (im.shape[1],im.shape[0]));
# pred_flo=makeUnitMag(pred_flo);
# gt_flo=makeUnitMag(gt_flo);
pred_values=[np.sort(pred_flo[:,:,0].ravel()),np.sort(pred_flo[:,:,1].ravel())]
gt_values=[np.sort(gt_flo[:,:,0].ravel()),np.sort(gt_flo[:,:,1].ravel())]
# print np.max(np.power(np.power(pred_values[0],2)+np.power(pred_values[1],2),0.5))
# print np.max(np.power(np.power(gt_values[0],2)+np.power(gt_values[1],2),0.5))
# print np.min(pred_values[0]),np.min(gt_values[0]);
# print np.min(pred_values[1]),np.min(gt_values[1]);
# print gt_values[0].shape,gt_values[1].shape
util.mkdir(out_dir_debug);
fig=plt.figure();
ax1 = fig.add_subplot(221)
ax1.plot(np.sort(mag_pred_flo_bef.ravel()));
ax2 = fig.add_subplot(222)
ax2.plot(np.sort(mag_pred_flo_aft.ravel()))
ax3 = fig.add_subplot(223)
ax3.plot(np.sort(mag_gt_flo_bef.ravel()))
ax4 = fig.add_subplot(224)
ax4.plot(np.sort(mag_gt_flo_aft.ravel()))
plt.tight_layout()
plt.savefig(os.path.join(out_dir_debug,'ranges.png'));
return
dir_flo_org='/disk2/aprilExperiments/flo_im';
dir_flo_pred='/disk2/aprilExperiments/flo_subset_predictions/pred_flo_im';
dir_im_meta='/disk2/marchExperiments/youtube'
im_names=[file_curr[:file_curr.rindex('_')] for file_curr in os.listdir(dir_flo_org) if file_curr.endswith('_x.png')];
out_file_html='/disk2/aprilExperiments/flo_subset_predictions/visualizeFlosComparison.html';
img_paths_all=[];
captions_all=[];
for im_name in im_names:
video_name=im_name[:im_name.index('.')];
jpg_file=os.path.join(dir_im_meta,video_name,'images_transfer',im_name+'.jpg');
x_flo_org=os.path.join(dir_flo_org,im_name+'_x.png');
y_flo_org=os.path.join(dir_flo_org,im_name+'_y.png');
x_flo_pred=os.path.join(dir_flo_pred,im_name+'_x.png');
y_flo_pred=os.path.join(dir_flo_pred,im_name+'_y.png');
row=[makeRelPath(jpg_file),makeRelPath(x_flo_org),makeRelPath(y_flo_org),makeRelPath(x_flo_pred),makeRelPath(y_flo_pred)];
captions=['im','org_x','org_y','pred_x','pred_y'];
img_paths_all.append(row);
captions_all.append(captions)
visualize.writeHTML(out_file_html,img_paths_all,captions_all,200,200);
return
results_dir='/disk2/aprilExperiments/flo_subset_predictions/results';
dir_flo_im='/disk2/aprilExperiments/flo_subset_predictions/pred_flo_im';
util.mkdir(dir_flo_im);
clusters_file='/home/maheenrashid/Downloads/debugging_jacob/optical_flow_prediction_test/examples/opticalflow/clusters.mat';
with h5py.File(clusters_file,'r') as hf:
print hf.keys();
C=np.array(hf.get('C'));
C=C.T
# img_files=[];
h5_files=[os.path.join(results_dir,file_curr) for file_curr in os.listdir(results_dir) if file_curr.endswith('.h5')];
for idx_h5_file,h5_file in enumerate(h5_files):
print idx_h5_file
img_file=util.readLinesFromFile(h5_file.replace('.h5','.txt'))[0];
img_name=img_file[img_file.rindex('/')+1:img_file.rindex('.')];
# img_files.append(img_file);
flo=getFlowMat(h5_file,C);
out_file_flo=os.path.join(dir_flo_im,img_name+'.npy');
np.save(out_file_flo,flo);
out_file_flo_x=os.path.join(dir_flo_im,img_name+'_x.png');
out_file_flo_y=os.path.join(dir_flo_im,img_name+'_y.png');
visualize.saveMatAsImage(flo[:,:,0],out_file_flo_x);
visualize.saveMatAsImage(flo[:,:,1],out_file_flo_y);
visualize.writeHTMLForFolder(dir_flo_im,ext='_x.png',height=300,width=300)
return
# pos_file_org='/disk2/aprilExperiments/deep_proposals/positives_person.txt';
# flow_dir='/disk2/aprilExperiments/deep_proposals/flow_all_humans/results_flow';
# out_dir='/disk2/aprilExperiments/dual_flow/onlyHuman_all_xavier';
# pos_file_new=os.path.join(out_dir,'positives.txt');
pos_file_org='/disk2/marchExperiments/deep_proposals/negatives.txt';
flow_dir='/disk2/aprilExperiments/deep_proposals/flow_neg/results_flow';
out_dir='/disk2/aprilExperiments/dual_flow/onlyHuman_all_xavier';
pos_file_new=os.path.join(out_dir,'negatives.txt');
pos_data=util.readLinesFromFile(pos_file_org);
pos_data_new=[];
for idx_pos_data_curr,pos_data_curr in enumerate(pos_data):
if idx_pos_data_curr%100==0:
print idx_pos_data_curr
img_name=pos_data_curr[:pos_data_curr.index(' ')];
img_name=img_name[img_name.rindex('/')+1:];
flow_name=img_name[:img_name.rindex('.')]+'_flow.png';
flow_path=os.path.join(flow_dir,flow_name);
if os.path.exists(flow_path):
pos_data_new_curr=pos_data_curr+' '+flow_path;
pos_data_new.append(pos_data_new_curr);
print len(pos_data_new)
util.writeFile(pos_file_new,pos_data_new);
return
dir_flow='/disk2/aprilExperiments/deep_proposals/flow_all_humans/results';
out_dir_flow_im='/disk2/aprilExperiments/deep_proposals/flow_all_humans/results_flow';
util.mkdir(out_dir_flow_im);
clusters_file='/home/maheenrashid/Downloads/debugging_jacob/optical_flow_prediction_test/examples/opticalflow/clusters.mat';
with h5py.File(clusters_file,'r') as hf:
print hf.keys();
C=np.array(hf.get('C'));
C=C.T
h5_files=[os.path.join(dir_flow,file_curr) for file_curr in os.listdir(dir_flow) if file_curr.endswith('.h5')];
img_files=[];
for idx,h5_file in enumerate(h5_files):
if idx%100==0:
print idx;
img_file=util.readLinesFromFile(h5_file.replace('.h5','.txt'))[0].strip();
img_files.append(img_file);
args=[];
count_missing=0;
for idx,(h5_file,img_file) in enumerate(zip(h5_files,img_files)):
if not os.path.exists(img_file):
count_missing=count_missing+1;
continue;
# print idx,h5_file,img_file
out_file_flow=img_file[:img_file.rindex('.')]+'_flow.png';
out_file_flow=out_file_flow[out_file_flow.rindex('/')+1:]
out_file_flow=os.path.join(out_dir_flow_im,out_file_flow);
arg_curr=(h5_file,img_file,out_file_flow,C,idx);
args.append(arg_curr);
print len(args);
print count_missing;
print len(img_files);
# for arg in args[:10]:
# arg=list(arg);
# print arg[:-2];
print 'starting Pool';
p=multiprocessing.Pool(8);
p.map(saveFlowImage,args);
# out_file_flow=h5_file.replace('.h5','.png');
# saveFlowImage(h5_file,img_file,out_file_flow,C);
# print h5_files.index(h5_file),out_file_flow
return
# /disk2/marchExperiments/youtube/dog_11_1/images_transfer/dog_11_1.avi_000326.tif
out_dir_results='/disk2/aprilExperiments/testing_flow/debug/results'
out_dir='/disk2/aprilExperiments/testing_flow/debug';
img_path='/disk2/marchExperiments/youtube/dog_11_1/images_transfer/dog_11_1.avi_000326.jpg'
tif_path='/disk2/marchExperiments/youtube/dog_11_1/images_transfer/dog_11_1.avi_000326.tif';
pred_path=os.path.join(out_dir_results,'0.h5');
clusters_file='/home/maheenrashid/Downloads/debugging_jacob/optical_flow_prediction_test/examples/opticalflow/clusters.mat';
C=getClusters(clusters_file);
img=scipy.misc.imread(img_path)
tif=scipy.misc.imread(tif_path);
tif=tif[:,:,:2];
for idx in range(tif.shape[2]):
print np.min(tif[:,:,idx]),np.max(tif[:,:,idx]);
flo=getFlowGT(tif,C);
with h5py.File(pred_path,'r') as hf:
data = hf.get('Outputs')
data = np.array(data)
flo_pred=assignToFlowSoft(data,C)
flo_pred=cv2.resize(flo_pred, (flo.shape[1],flo.shape[0]))
print np.min(flo),np.max(flo);
print np.min(flo_pred),np.max(flo_pred);
print flo_pred.shape
print flo.shape
print img.shape
print tif.shape
out_img=os.path.join(out_dir,'img.png');
tif_x=os.path.join(out_dir,'tif_x.png');
tif_y=os.path.join(out_dir,'tif_y.png');
flo_x=os.path.join(out_dir,'flo_x.png');
flo_y=os.path.join(out_dir,'flo_y.png');
flo_pred_x=os.path.join(out_dir,'flo_pred_x.png');
flo_pred_y=os.path.join(out_dir,'flo_pred_y.png');
plt.figure;plt.imshow(img);plt.savefig(out_img);plt.close();
plt.figure;plt.imshow(tif[:,:,0]);plt.savefig(tif_x);plt.close();
plt.figure;plt.imshow(tif[:,:,1]);plt.savefig(tif_y);plt.close();
plt.figure;plt.imshow(flo[:,:,0]);plt.savefig(flo_x);plt.close();
plt.figure;plt.imshow(flo[:,:,1]);plt.savefig(flo_y);plt.close();
plt.figure;plt.imshow(flo_pred[:,:,0]);plt.savefig(flo_pred_x);plt.close();
plt.figure;plt.imshow(flo_pred[:,:,1]);plt.savefig(flo_pred_y);plt.close();
return
dir_prop='/disk2/aprilExperiments/deep_proposals/addingFlow/';
util.mkdir(dir_prop);
out_file_pos=os.path.join(dir_prop,'positives.txt');
out_file_neg=os.path.join(dir_prop,'negatives.txt');
pos_file_org='/disk2/aprilExperiments/deep_proposals/positives_person.txt';
neg_file_org='/disk2/marchExperiments/deep_proposals/negatives.txt';
dir_flow_pos='/disk2/aprilExperiments/deep_proposals/flow/results';
dir_flow_neg='/disk2/aprilExperiments/deep_proposals/flow/results_neg';
num_to_keep=100;
flow_files,im_files=getFlowImFiles(dir_flow_pos);
pos_data=util.readLinesFromFile(pos_file_org);
pos_data=pos_data[:100];
writeNewFileWithFlow(pos_data,flow_files,im_files,out_file_pos)
flow_files,im_files=getFlowImFiles(dir_flow_neg);
pos_data=util.readLinesFromFile(neg_file_org);
pos_data=pos_data[:100];
writeNewFileWithFlow(pos_data,flow_files,im_files,out_file_neg)
return
dir_flow='/disk2/aprilExperiments/deep_proposals/flow/results_neg';
clusters_file='/home/maheenrashid/Downloads/debugging_jacob/optical_flow_prediction_test/examples/opticalflow/clusters.mat';
with h5py.File(clusters_file,'r') as hf:
print hf.keys();
C=np.array(hf.get('C'));
C=C.T
h5_files=[os.path.join(dir_flow,file_curr) for file_curr in os.listdir(dir_flow) if file_curr.endswith('.h5')];
img_files=[];
for h5_file in h5_files:
img_file=util.readLinesFromFile(h5_file.replace('.h5','.txt'))[0].strip();
img_files.append(img_file);
for h5_file,img_file in zip(h5_files,img_files):
out_file_flow=h5_file.replace('.h5','.png');
saveFlowImage(h5_file,img_file,out_file_flow,C);
print h5_files.index(h5_file),out_file_flow
return
clusters_file='/home/maheenrashid/Downloads/debugging_jacob/optical_flow_prediction_test/examples/opticalflow/clusters.mat';
with h5py.File(clusters_file,'r') as hf:
print hf.keys();
C=np.array(hf.get('C'));
C=C.T
print C.shape;
# return
theImage='/disk2/februaryExperiments/deep_proposals/positives/COCO_train2014_000000024215_61400.png';
theImage =scipy.misc.imread(theImage);
file_name='/disk2/aprilExperiments/deep_proposals/flow/results/44.h5';
with h5py.File(file_name,'r') as hf:
print('List of arrays in this file: \n', hf.keys())
data = hf.get('Outputs')
np_data = np.array(data)
print('Shape of the array dataset_1: \n', np_data.shape)
flow=assignToFlowSoft(np_data.ravel(),C);
# print np.min(flow),np.max(flow);
# flow_new_x=scipy.misc.imresize(flow[:,:,0],(theImage.shape[0],theImage.shape[1]))
# flow_new_y=scipy.misc.imresize(flow[:,:,1],(theImage.shape[0],theImage.shape[1]))
# flow=np.dstack((flow_new_x,flow_new_y));
# print np.min(flow),np.max(flow);
flow = cv2.resize(flow, (240,240))
print flow.shape
flow_old=flow;
# f=h5py.File(file_name,'r')
# output=f.get('/Outputs')
# f.close();
# output=np.array(output);
# return
# h5disp(file_name, '/Outputs');
# theTemp = h5read(file_name, '/Outputs');
# theOutput = [];
# for j = 1:size(theTemp,2)
# for k = 1:size(theTemp,1)
# for l = 1:size(theTemp,3)
# theOutput = [theOutput theTemp(k,j,l)];
# end
# end
# end
# N = assignToFlowSoft(theOutput(:), C);
# N = imresize(N, [size(theImage,1) size(theImage,2)]);
# return
path_to_mat='/disk2/temp/checking_h5.mat';
mat_data=scipy.io.loadmat(path_to_mat)
flow_new=mat_data['N'];
# print flow_new.shape
# print flow[:3,:3,0];
# print flow_new[:3,:3,0];
# ans=np.isclose(flow_new,flow);
# print ans
# print ans
# scipy.misc.imsave('/disk2/temp/a.png',255*np.dstack((np.dstack((ans[:,:,0],ans[:,:,0])),ans[:,:,0])))
# scipy.misc.imsave('/disk2/temp/b.png',255*np.dstack((np.dstack((ans[:,:,1],ans[:,:,1])),ans[:,:,1])))
# return
out_flow_range_old='/disk2/temp/checking_h5_flow_old.png';
out_flow_range_new='/disk2/temp/checking_h5_flow_new.png';
out_flow_range_img='/disk2/temp/checking_h5_flow_img.png';
plt.ion();
plt.figure();
plt.plot(np.sort(np.ravel(flow_old)));
plt.savefig(out_flow_range_old);
plt.figure();
plt.plot(np.sort(np.ravel(flow_new)));
plt.savefig(out_flow_range_new);
# return
out_flow_x='/disk2/temp/checking_h5_x.png';
out_flow_y='/disk2/temp/checking_h5_y.png';
out_mag='/disk2/temp/checking_h5_mag.png';
mag=np.power(np.power(flow[:,:,0],2)+np.power(flow[:,:,1],2),0.5)
flow=np.dstack((flow,mag));
print flow.shape
min_v=np.min(flow);
flow=flow+abs(min_v);
max_v=np.max(flow);
flow=flow/max_v
flow=flow*255;
flow=scipy.misc.imread(out_mag);
plt.figure();
plt.plot(np.sort(np.ravel(flow[:,:,2])));
plt.savefig(out_flow_range_img);
return
# flow=flow/np.dstack((mag,mag));
# flow=flow
# flow=flow+abs(np.min(flow))
# flow=255*flow;
# print np.min(flow[:,:,0]),np.max(flow[:,:,0]);
# print np.min(flow[:,:,1]),np.max(flow[:,:,1]);
for idx in range(flow.shape[2]):
mag=flow[:,:,idx];
print np.min(mag),np.max(mag);
im_x=np.dstack((np.dstack((flow[:,:,0],flow[:,:,0])),flow[:,:,0]));
im_y=np.dstack((np.dstack((flow[:,:,1],flow[:,:,1])),flow[:,:,1]));
# mag=np.dstack((np.dstack((mag,mag)),mag));
# mag=np.dstack((np.dstack((flow[:,:,0],flow[:,:,1])),mag))
scipy.misc.imsave(out_flow_x,im_x);
scipy.misc.imsave(out_flow_y,im_y);
scipy.misc.imsave(out_mag,flow);
