def ComputeScores(list_rgb,
dic_gt,
dic_prob,
p1,
p2,
keep_memory=False,
path_save='./tmp'):
res_AJI = []
res_F1 = []
res_DSB = []
res_ps = []
res_TP = []
res_FN = []
res_FP = []
for path in list_rgb:
GT = imread(dic_gt[path])
GT = PostProcess(GT, 1, 0)
DImg = dic_prob[path]
DImg[DImg < 0] = 0
DImg = DImg.astype("uint8")
S = PostProcess(DImg, p1, p2)
res_AJI.append(AJI_fast(GT, S))
res_F1.append(ComputeF1(GT, S))
scores, p_s, TP, FN, FP = DataScienceBowlMetrics(GT, S)
res_DSB.append(scores)
res_ps.append(p_s)
res_TP.append(TP)
res_FN.append(FN)
res_FP.append(FP)
if keep_memory:
img_mean = np.mean(imread(path)[:, :, 0:3])
if img_mean < 125:
color_cont = False
else:
color_cont = True
OUT = join(path_save, basename(path).replace('.png', ''))
CheckOrCreate(OUT)
os.symlink(abspath(path), join(OUT, "rgb.png"))
os.symlink(abspath(dic_gt[path]), join(OUT, "bin.png"))
imsave(join(OUT, "colored_bin.png"), color_bin(label(GT)))
imsave(join(OUT, "colored_pred.png"), color_bin(S))
imsave(join(OUT, "output_DNN.png"), DImg)
imsave(join(OUT, "contours_gt.png"),
Overlay_with_pred(path, GT, color_cont).astype('uint8'))
imsave(join(OUT, "contours_pred.png"),
Overlay_with_pred(path, S, color_cont).astype('uint8'))
# pdb.set_trace()
if keep_memory:
return res_AJI, res_F1, res_DSB, res_ps, res_TP, res_FN, res_FP
else:
return np.mean(res_AJI), np.mean(res_F1), np.mean(res_DSB)
CheckOrCreate(options.output_sample)
for key in dic.keys():
OUT_ID = join(options.output_sample, basename(key).replace('.png', ''))
CheckOrCreate(OUT_ID)
dic_prob[key] = np.mean(np.concatenate(dic[key]), axis=0)[:, :, 1]
dic_final_pred[key] = PostProcess(dic_prob[key], P1, P2)
# dic_final_pred[key] = (dic_prob[key] > P2).astype('uint8')
dic_final_pred[key] = label(dic_final_pred[key])
dic_final_pred[key] = remove_small_objects(dic_final_pred[key], 32)
img_mean = np.mean(imread(key)[:, :, 0:3])
if img_mean < 125:
color_cont = False
else:
color_cont = True
#put rgb image
os.symlink(abspath(key), join(OUT_ID, "rgb.png"))
imsave(join(OUT_ID, "colored_pred.png"),
color_bin(dic_final_pred[key]))
imsave(join(OUT_ID, "output_DNN_mean.png"),
img_as_ubyte(dic_prob[key]))
for k, el in enumerate(dic[key]):
imsave(
join(OUT_ID, "output_DNN_{}.png").format(k),
img_as_ubyte(el[0, :, :, 1]))
imsave(
join(OUT_ID, "contours_pred.png"),
Overlay_with_pred(key, dic_final_pred[key],
color_cont).astype('uint8'))
WriteEvaluation(outcsv, dic_final_pred)
tf.reset_default_graph() # so that it can restore properly the next model
dic_final_pred = {}
dic_prob = {}
CheckOrCreate(options.output_sample)
for key in dic.keys():
OUT_ID = join(options.output_sample, basename(key).replace('.png', ''))
CheckOrCreate(OUT_ID)
dic_prob[key] = np.mean(np.concatenate(dic[key]), axis=0)[:,:,1]
dic_final_pred[key] = PostProcess(dic_prob[key], P1, P2)
# dic_final_pred[key] = (dic_prob[key] > P2).astype('uint8')
dic_final_pred[key] = label(dic_final_pred[key])
dic_final_pred[key] = remove_small_objects(dic_final_pred[key], 32)
img_mean = np.mean(imread(key)[:,:,0:3])
if img_mean < 125:
color_cont = False
else:
color_cont = True
#put rgb image
os.symlink(abspath(key), join(OUT_ID, "rgb.png"))
imsave(join(OUT_ID, "colored_pred.png"), color_bin(dic_final_pred[key]))
imsave(join(OUT_ID, "output_DNN_mean.png"), img_as_ubyte(dic_prob[key]))
for k, el in enumerate(dic[key]):
imsave(join(OUT_ID, "output_DNN_{}.png").format(k), img_as_ubyte(el[0,:,:,1]))
imsave(join(OUT_ID, "contours_pred.png"), Overlay_with_pred(key, dic_final_pred[key], color_cont).astype('uint8'))
WriteEvaluation(outcsv, dic_final_pred)