# scenelists = ['bears', 'car_fig','extr_farm','extr_woods','glass','intr_farm','intr_woods','metal',] # methods = ['directvoxgo','ibrnet_finetune','ibrnet_pretrain','mipnerf','nerf','nex','plenoxel','reference'] # import os # for scene in scenelists: # for method in methods: # f = open('results.txt', 'a') # lines=[] # cmds = "fvvdp --test 'scene="+ scene+ ",method="+method+".mp4' --ref 'scene="+ scene+ ",method=reference.mp4' --gpu 0 --display eizo_CS274_2k --quiet" # print(cmds) # # os.system(cmds) # r = os.popen(cmds) # info = r.readlines() # value = info[0].strip('\r\n') # lines.append(scene) # lines.append(method) # lines.append(value) # f.writelines(' '.join(lines)) # f.write('\n') # f.close() import os import glob import csv scenelists = ['bears','calci_museum_dinosaur', 'calci_museum_elephant', 'calci_museum_giraffe', 'calci_museum_whale','calci_museum_geopards', 'lucca_statue_3', 'lucca_statue_2_nostab','lucca_statue_3_nostab','extr_farm','intr_farm','extr_woods','intr_woods','car_fig','glass','metal'] methods=['directvoxgo', 'ibrnet_finetune' , 'ibrnet_pretrain', 'mipnerf', 'nerf', 'nex', 'plenoxel','gnt_crossscene','gnt_singlescene','light_field','reference'] f=open('/home/hl589/benchmark/nerf_fvvdpvideo.csv','a') writer = csv.writer(f) lines = ['condition_id','Q','proc_time'] writer.writerow(lines) f.close() for scene in scenelists: for method in methods: f=open('/home/hl589/benchmark/nerf_fvvdpvideo.csv','a') writer = csv.writer(f) print('currently process',method,scene) lines=[] lines.append(scene+'-'+method) cmds = "fvvdp --test 'scene="+ scene+ ",method="+method+".mp4' --ref 'scene="+ scene+ ",method=reference.mp4' --gpu 0 --display eizo_CS274_2k --quiet" print(cmds) r = os.popen(cmds) info = r.readlines() value = info[0].strip('\r\n') lines.append(value) lines.append(0) writer.writerow(lines) f.close() # # scenelists=['fern','flower','fortress','horns','leaves','orchids','room','trex'] # scenelists = ['bears', 'car_fig','extr_farm','extr_woods','glass','intr_farm','intr_woods','metal'] # methods = ['directvoxgo','ibrnet_finetune','ibrnet_pretrain','mipnerf','nerf','nex','plenoxel','reference'] # import pyfvvdp # import glob # import os # import csv # from numpy import * # fv = pyfvvdp.fvvdp(display_name='eizo_CS274_2k', heatmap='threshold') # f=open('/home/hl589/benchmark/nerf_fvvdpimage.csv','a') # writer = csv.writer(f) # lines = ['condition_id','Q','proc_time'] # writer.writerow(lines) # f.close() # for scene in scenelists: # for method in methods: # print(scene, method) # # f = open('lab_fvvdp_image_results.txt', 'a') # f=open('/home/hl589/benchmark/nerf_fvvdpimage.csv','a') # writer = csv.writer(f) # ref_imgdir = os.path.join('/anfs/gfxdisp/hanxue_nerf_data/benchmark_results_sparse_set/reference',scene) # imgdir = os.path.join('/anfs/gfxdisp/hanxue_nerf_data/benchmark_results_sparse_set',method+'/'+scene) # ref_images = sorted(glob.glob(os.path.join(ref_imgdir,'*.png'))) # images = sorted(glob.glob(os.path.join(imgdir,'*.png'))) # assert len(ref_images)==len(images) # results = [] # for i in range(len(ref_images)): # I_ref = pyfvvdp.load_image_as_array(ref_images[i]) # I_test = pyfvvdp.load_image_as_array(images[i]) # Q_JOD_blur, stats_blur = fv.predict( I_test, I_ref, dim_order="HWC" ) # results.append(Q_JOD_blur.item()) # lines = [] # lines.append(scene+'-'+method) # lines.append(str(mean(results))) # lines.append(0) # writer.writerow(lines) # f.close() # # lines.append(scene) # # lines.append(method) # # lines.append(str(mean(results))) # # # print(lines) # # # print(' '.join(lines)) # # f.writelines(' '.join(lines)) # # f.write('\n') # # f.close() # for scene in scenelists: # f = open('llff_results.txt', 'a') # imgdir='/anfs/gfxdisp/hanxue_nerf_data/logs/plenoxel/'+scene+'/test_renders' # imgfiles = sorted(glob.glob(os.path.join(imgdir,'*.png'))) # ref_images = imgfiles[1::2] # images = imgfiles[::2] # dvg_imgdir='/anfs/gfxdisp/hanxue_nerf_data/logs/dvg_new/'+scene+'/render_test_fine_last' # dvg_imgfiles = sorted(glob.glob(os.path.join(dvg_imgdir,'*.png'))) # print(len(ref_images),len(images),ref_images,images) # assert len(ref_images)==len(images) # assert len(ref_images)==len(dvg_imgfiles) # plenoxel_results = [] # dvg_results = [] # reference_results = [] # for i in range(len(ref_images)): # I_ref = pyfvvdp.load_image_as_array(ref_images[i]) # I_test = pyfvvdp.load_image_as_array(images[i]) # Q_JOD_blur, stats_blur = fv.predict( I_test, I_ref, dim_order="HWC" ) # plenoxel_results.append(Q_JOD_blur.item()) # I_test = pyfvvdp.load_image_as_array(dvg_imgfiles[i]) # I_test = I_test[:,:1008,:] # print(I_test.shape, I_ref.shape) # Q_JOD_blur, stats_blur = fv.predict( I_test, I_ref, dim_order="HWC" ) # dvg_results.append(Q_JOD_blur.item()) # Q_JOD_blur, stats_blur = fv.predict( I_ref, I_ref, dim_order="HWC" ) # reference_results.append(Q_JOD_blur.item()) # lines = [] # lines.append(scene) # lines.append('directvoxgo') # lines.append(str(mean(dvg_results))) # f.writelines(' '.join(lines)) # f.write('\n') # lines = [] # lines.append(scene) # lines.append('plenoxel') # lines.append(str(mean(plenoxel_results))) # f.writelines(' '.join(lines)) # f.write('\n') # lines = [] # lines.append(scene) # lines.append('reference') # lines.append(str(mean(reference_results))) # f.writelines(' '.join(lines)) # f.write('\n') # f.close() # for scene in scenelists: # f = open('llff_results.txt', 'a') # imgdir='/rds/user/hl589/rds-nerf-benchmark-JDeuXlFW9KE/nerf_benchmark_ww/exp_log/ibr/llff_test/eval_llff/'+scene+'_315000' # ref_images = sorted(glob.glob(os.path.join(imgdir,'*gt_rgb.png'))) # ibrfinetune_images = sorted(glob.glob(os.path.join(imgdir,'*pred_fine.png'))) # imgdir='/rds/user/hl589/rds-nerf-benchmark-JDeuXlFW9KE/nerf_benchmark_ww/exp_log/ibr/llff_test/eval_no_finetune_llff/'+scene+'_255000' # ibrpretrain_images = sorted(glob.glob(os.path.join(imgdir,'*pred_fine.png'))) # imgdir='/rds/user/hl589/rds-nerf-benchmark-JDeuXlFW9KE/nerf_benchmark_ww/exp_log/nerf_exp/llff/'+scene+'/eval' # nerf_images = sorted(glob.glob(os.path.join(imgdir,'rgb_*.png'))) # imgdir='/rds/user/hl589/rds-nerf-benchmark-JDeuXlFW9KE/nerf_benchmark_ww/exp_log/nex_exp/'+scene+'/nex_exp'+scene+'/rendered_val' # nex_images = sorted(glob.glob(os.path.join(imgdir,'*_ours.png'))) # imgdir='/rds/user/hl589/rds-nerf-benchmark-JDeuXlFW9KE/nerf_benchmark_ww/exp_log/mip_nerf/'+scene+'/test_preds' # mipnerf_images = sorted(glob.glob(os.path.join(imgdir,'color_*.png'))) # print(len(ref_images),len(ibrfinetune_images),len(ibrpretrain_images),ref_images) # print(len(nerf_images),len(nex_images),len(mipnerf_images)) # assert len(ref_images)==len(ibrfinetune_images) # assert len(ref_images)==len(ibrpretrain_images) # assert len(ref_images)==len(nerf_images) # assert len(ref_images)==len(nex_images) # assert len(ref_images)==len(mipnerf_images) # ibrfinetune_results = [] # ibrpretrain_results = [] # nerf_results = [] # nex_results = [] # mipnerf_results = [] # reference_results = [] # for i in range(len(ref_images)): # I_ref = pyfvvdp.load_image_as_array(ref_images[i]) # I_test = pyfvvdp.load_image_as_array(ibrfinetune_images[i]) # Q_JOD_blur, stats_blur = fv.predict( I_test, I_ref, dim_order="HWC" ) # ibrfinetune_results.append(Q_JOD_blur.item()) # I_test = pyfvvdp.load_image_as_array(ibrpretrain_images[i]) # Q_JOD_blur, stats_blur = fv.predict( I_test, I_ref, dim_order="HWC" ) # ibrpretrain_results.append(Q_JOD_blur.item()) # I_test = pyfvvdp.load_image_as_array(nerf_images[i]) # Q_JOD_blur, stats_blur = fv.predict( I_test, I_ref, dim_order="HWC" ) # nerf_results.append(Q_JOD_blur.item()) # I_test = pyfvvdp.load_image_as_array(nex_images[i]) # Q_JOD_blur, stats_blur = fv.predict( I_test, I_ref, dim_order="HWC" ) # nex_results.append(Q_JOD_blur.item()) # I_test = pyfvvdp.load_image_as_array(mipnerf_images[i]) # Q_JOD_blur, stats_blur = fv.predict( I_test, I_ref, dim_order="HWC" ) # mipnerf_results.append(Q_JOD_blur.item()) # Q_JOD_blur, stats_blur = fv.predict( I_ref, I_ref, dim_order="HWC" ) # reference_results.append(Q_JOD_blur.item()) # lines = [] # lines.append(scene) # lines.append('ibrnet_finetune') # lines.append(str(mean(ibrfinetune_results))) # f.writelines(' '.join(lines)) # f.write('\n') # lines = [] # lines.append(scene) # lines.append('ibrnet_pretrain') # lines.append(str(mean(ibrpretrain_results))) # f.writelines(' '.join(lines)) # f.write('\n') # lines = [] # lines.append(scene) # lines.append('nerf') # lines.append(str(mean(nerf_results))) # f.writelines(' '.join(lines)) # f.write('\n') # lines = [] # lines.append(scene) # lines.append('nex') # lines.append(str(mean(nex_results))) # f.writelines(' '.join(lines)) # f.write('\n') # lines = [] # lines.append(scene) # lines.append('mipnerf') # lines.append(str(mean(mipnerf_results))) # f.writelines(' '.join(lines)) # f.write('\n') # # lines = [] # # lines.append(scene) # # lines.append('reference') # # lines.append(str(mean(reference_results))) # # f.writelines(' '.join(lines)) # # f.write('\n') # f.close() # import os # import glob # import csv # scenelists=['bears', 'car_fig', 'extr_farm','extr_woods', 'fern', 'flower',\ # 'fortress', 'glass', 'horns', 'intr_farm', 'intr_woods', 'leaves', \ # 'metal', 'orchids','room','trex'] # methods=['directvoxgo', 'ibrnet_finetune' , 'ibrnet_pretrain', 'mipnerf', 'nerf', 'nex', 'plenoxel','reference'] # f=open('/home/hl589/benchmark/nerf_fvvdp.csv','a') # writer = csv.writer(f) # lines = ['condition_id','Q','proc_time'] # writer.writerow(lines) # f.close() # f = open('fvvdp_results.txt', 'r') # lines=f.readlines() # results=[line.strip('\n').split(',') for line in lines] # f.close() # for method in methods: # for scene in scenelists: # f=open('/home/hl589/benchmark/nerf_fvvdp.csv','a') # writer = csv.writer(f) # print('currently process',method,scene) # lines=[] # lines.append(scene+'-'+method) # for i in range(len(results)): # if results[i][0]==scene and results[i][1]==method: # lines.append(results[i][2]) # break # # lines.append(str(sum(tmp)/len(tmp))) # lines.append(0) # writer.writerow(lines) # f.close()