def main():
args = parse_args()
args.pred_list_dir = os.path.join(args.result_dir, 'List_Masks')
if args.del_overlap:
args.submission_path = os.path.join(args.result_dir,
'csv_files_del_overlap')
else:
args.submission_path = os.path.join(args.result_dir, 'csv_files')
if not os.path.exists(args.submission_path):
os.mkdir(args.submission_path)
args.pred_image_list = os.listdir(args.pred_list_dir)
test_video_list = os.listdir(args.test_video_list_dir)
jobs = []
for i in xrange(len(test_video_list)):
p = multiprocessing.Process(target=convertCsvWorker,
args=(test_video_list[i], args))
jobs.append(p)
p.start()
# waiting for all jobs to finish
for j in jobs:
j.join()
print("Finishing converting all the csv fils")
def main():
args = parse_args()
frames = []
dataset = WAD_CVPR2018(args.dataset_dir)
if args.del_overlap:
args.submission_path = os.path.join(args.result_dir, 'csv_files_del_overlap')
else:
args.submission_path = os.path.join(args.result_dir, 'csv_files')
print(args.submission_path)
names = ['ImageId', 'LabelId', 'Confidence', 'PixelCount', 'EncodedPixels']
for csv_file in os.listdir(args.submission_path):
df = pd.read_csv(os.path.join(args.submission_path, csv_file), names=names)
df_new = df[df.PixelCount != 0]
frames.append(df_new)
if len(df) != len(df_new):
print("We have zeros elements in the file: %s" % csv_file)
sub_csv = pd.concat(frames, axis=0)
sub_csv = sub_csv.dropna()
# sub_csv.Confidence = 1 # THIS LINE NEEDS TO BE DELETED!
#confs = [0.1, 0.2, 0.3, 0.4, 0.5]
confs = [0.6, 0.7, 0.8, 0.9]
for i in tqdm(range(len(confs))):
conf = confs[i]
sub_csv = sub_csv.reset_index(drop=True)
sub_csv_new = submission_sanity_check(sub_csv, args.test_img_dir)
sub_csv_new.Confidence = sub_csv_new.Confidence.astype(float)
sub_csv_new = sub_csv_new.loc[sub_csv_new['Confidence'] > conf]
sub_csv_new = sub_csv_new.reset_index(drop=True)
sub_csv_new = sub_csv_new.reindex(columns=['ImageId', 'LabelId', 'PixelCount', 'Confidence', 'EncodedPixels'])
sub_csv_new.LabelId = sub_csv_new.LabelId.astype(int)
sub_csv_new.PixelCount = sub_csv_new.PixelCount.astype(int)
sub_csv_new.Confidence = sub_csv_new.Confidence.round(6)
# print some statistics
count = []
for l in np.unique(sub_csv_new.LabelId):
count.append(np.sum(sub_csv_new['LabelId'] == l))
count_ratio = np.array([x/np.sum(count) for x in count])
for i, l in enumerate(np.unique(sub_csv_new.LabelId)):
print("%s --> %d --> %0.3f" %(dataset.id_map_to_cat[l], count[i], count_ratio[i]))
print('Total instance number for conf %.1f is %d' % (conf, len(sub_csv_new)))
if args.del_overlap:
csv_file_name = os.path.join(args.result_dir, 'combined_%.2f_del_overlap.csv' % conf)
else:
csv_file_name = os.path.join(args.result_dir, 'combined_%.2f.csv' % conf)
sub_csv_new.to_csv(csv_file_name, header=True, index=False)
print("Finish saving file t: %s" % csv_file_name)
def main():
args = parse_args()
args.pred_list_dir = os.path.join(args.result_dir, 'List_Masks')
if args.del_overlap:
args.submission_path = os.path.join(args.result_dir,
'csv_files_del_overlap')
else:
args.submission_path = os.path.join(args.result_dir, 'csv_files')
if not os.path.exists(args.submission_path):
os.mkdir(args.submission_path)
args.pred_image_list = os.listdir(args.pred_list_dir)
test_video_list = os.listdir(args.test_video_list_dir)
jobs = []
for i in xrange(len(test_video_list)):
video_path = test_video_list[i]
videoname = video_path.split('.')[0]
print('videoname:' + videoname)
groundTruthImgList = []
predictionImgList = []
groundTruthList = open(
os.path.join(args.test_video_list_dir, video_path), "r")
groundTruthListlines = groundTruthList.readlines()
for groundTruthListline in groundTruthListlines:
gtfilename = groundTruthListline.split('\n')[0].split('\t')[0]
groundTruthImgList.append(gtfilename)
# We also read groudtruth md5 mapping
mapping_file = open(
os.path.join(args.mapping_dir, 'md5_mapping_' + video_path), "r")
mappingLines = mapping_file.readlines()
mapping_dict = {}
for mapping in mappingLines:
mapping_dict[mapping.split('\t')[1].replace(
'\n', '')] = mapping.split('\t')[0]
for gt in groundTruthImgList:
predictionImgList.append(getPrediction(gt, args, mapping_dict))
for list_index, filename in enumerate(groundTruthImgList):
args.csv_file_image = os.path.join(
args.submission_path, videoname + '_FRAME_%d' % list_index +
'_' + filename.split('/')[-1][:-4] + '.csv')
if not os.path.exists(args.csv_file_image):
if args.del_overlap:
p = multiprocessing.Process(
target=convertImages_with_postprocessing_image,
args=(filename, list_index, predictionImgList, args,
mapping_dict))
else:
p = multiprocessing.Process(
target=convertImages_with_image,
args=(filename, list_index, predictionImgList, args,
mapping_dict))
jobs.append(p)
p.start()
if len(jobs) > args.num_threads:
jobs[-args.num_threads].join()
# waiting for all jobs to finish
for j in jobs:
j.join()
print("Finishing converting all the csv fils")