def testSingleImg():
NetHelper.gpu()
#submission()
nh = NetHelper(deploy=cfgs.deploy_pt, model=cfgs.best_model_dir)
img = Data.imFromFile(os.path.join(cfgs.train_mask_path, "1_1_mask.tif"))
res = nh.bin_pred_map(img)
print(np.histogram(res))
def testSingleImg():
NetHelper.gpu()
#submission()
nh=NetHelper(deploy=cfgs.deploy_pt,model=cfgs.best_model_dir)
img=Data.imFromFile(os.path.join(cfgs.train_mask_path,"1_1_mask.tif"))
res=nh.bin_pred_map(img)
print(np.histogram(res))
def func(filename, nh):
_, idx, ext = Data.splitPath(filename)
if ext != ".tif":
return None
cfgs.cnt += 1
print(cfgs.cnt)
#idx=int(idx)
img = Data.imFromFile(filename)
ready = prep(img, cfgs.inShape[1], cfgs.inShape[0])
# print(np.histogram(ready))
# ready*=0.00392156862745
ready -= 128
ready *= 0.0078431372549
pred_bin, pred, img = classifier(ready, nh)
# pred_bin,pred,output, img=classifier(ready,nh)
result = run_length_enc(pred_bin)
if debug:
# print('org',np.histogram(ready))
# print('data', np.histogram(img))
hist = np.histogram(img)
print(pd.DataFrame(hist[0], index=hist[1][1:]).T)
hist = np.histogram(pred)
print(pd.DataFrame(hist[0], index=hist[1][1:]).T)
mask = plt.imread(os.path.join(cfgs.train_mask_path,
idx + "_mask.tif"))
plt.figure(1)
plt.subplot(221)
plt.title('mask')
plt.imshow(mask)
plt.subplot(222)
plt.title('prediction')
plt.imshow(pred_bin)
plt.subplot(223)
plt.title('img')
plt.imshow(img)
plt.subplot(224)
plt.title('heatmap ')
plt.imshow(pred)
plt.show()
# print(idx,result)
return (idx, result)
def func(filename, nh):
_,idx,ext=Data.splitPath(filename)
if ext!=".tif":
return None
cfgs.cnt+=1
print(cfgs.cnt)
#idx=int(idx)
img=Data.imFromFile(filename)
ready=prep(img,cfgs.inShape[1],cfgs.inShape[0])
# print(np.histogram(ready))
# ready*=0.00392156862745
ready-=128
ready*=0.0078431372549
pred_bin,pred, img=classifier(ready,nh)
# pred_bin,pred,output, img=classifier(ready,nh)
result=run_length_enc(pred_bin)
if debug:
# print('org',np.histogram(ready))
# print('data', np.histogram(img))
hist=np.histogram(img)
print(pd.DataFrame(hist[0],index=hist[1][1:]).T)
hist=np.histogram(pred)
print(pd.DataFrame(hist[0],index=hist[1][1:]).T)
mask=plt.imread(os.path.join(cfgs.train_mask_path,idx+"_mask.tif"))
plt.figure(1)
plt.subplot(221)
plt.title('mask')
plt.imshow(mask)
plt.subplot(222)
plt.title('prediction')
plt.imshow(pred_bin)
plt.subplot(223)
plt.title('img')
plt.imshow(img)
plt.subplot(224)
plt.title('heatmap ')
plt.imshow(pred)
plt.show()
# print(idx,result)
return (idx,result)
