scale_th = np.array([500000,2500000,7000000,15000000])
for fn in datas[:,0]:
idx_colorgain = np.where(datas_colorgain[:,0]==fn)[0][0]
cg = datas_colorgain[idx_colorgain]
hw = cg[4]*cg[5]
ss = (scale_th<=hw).sum()
s_hw.append(ss)
if fn_meta is not None:
df = pd.read_csv(fn_meta)
datas_meta = df.values
datas_meta = np.hstack((datas_meta[:,:5], np.int32(s_hw)[:,None]))
dict_im_fq = count_imfq_samemeta(datas_meta,meta_format = 20)
flist = [osp.join(fd_in, fn+'.jpg') for fn in datas[:,0]]
for idx, fn in enumerate(tqdm(flist)):
#img = cv2.imread(fn)
#hh,ww,_ = img.shape
#gt = np.where(datas[idx][1:]==1)[0][0]
gt = datas[idx][-1]
if map_gt is not None:
if gt>= len(map_gt):
#colorgain_csv = './dat/all18_colorgain1.csv'
#out_csv = './dat/all18_info1.csv'
info_list = []
df = pd.read_csv(colorgain_csv)
datas_colorgain = df.values
for fn_gt, fn_meta, map_gt in zip(fn_gts, fn_metas, map_gts):
df = pd.read_csv(fn_gt)
datas = df.values
if fn_meta is not None:
df = pd.read_csv(fn_meta)
datas_meta = df.values
dict_im_fq = count_imfq_samemeta(fn_meta)
flist = [osp.join(fd_in, fn + '.jpg') for fn in datas[:, 0]]
for idx, fn in enumerate(tqdm(flist)):
img = cv2.imread(fn)
hh, ww, _ = img.shape
if fn_meta is not None:
idx_meta = np.where(datas_meta[:, 0] == Path(fn).stem)[0][0]
meta = datas_meta[idx_meta][[1, 2, 4]]
n_rep = dict_im_fq[Path(fn).stem]
else:
meta = [math.nan, math.nan, math.nan]
n_rep = 1.0