def get_data_predict_overlap(imgPredict, patch_height, patch_width, stride_height, stride_width,num_lesion,total_data):
### test
img = imgPredict
test_imgs_original = []
test_imgs_original .append(img)
test_imgs_original=np.asarray(test_imgs_original)
test_imgs_original=np.transpose(test_imgs_original,(0,3,1,2))
test_imgs_adjust = my_PreProc(test_imgs_original)
#extend both images and masks so they can be divided exactly by the patches dimensions
#test_imgs = test_imgs[0:Imgs_to_test,:,:,:]
#test_masks = test_masks[0:Imgs_to_test,:,:,:]
test_imgs = paint_border_overlap(test_imgs_adjust, patch_height, patch_width, stride_height, stride_width)
#check masks are within 0-1
print("\ntest images shape:")
print (test_imgs.shape)
#extract the TEST patches from the full images
patches_imgs_test = extract_ordered_overlap(test_imgs,patch_height,patch_width,stride_height,stride_width)
print("\ntest PATCHES images shape:")
print(patches_imgs_test.shape)
print("test PATCHES images range (min-max): " +str(np.min(patches_imgs_test)) +' - '+str(np.max(patches_imgs_test)))
return patches_imgs_test, test_imgs.shape[2], test_imgs.shape[3],test_imgs_adjust
def get_data_testing_overlap(DRIVE_test_imgs_original, DRIVE_test_groudTruth, patch_height, patch_width, stride_height, stride_width,num_lesion,total_data):
### test
test_imgs_original = load_hdf5(DRIVE_test_imgs_original)
test_masks=np.zeros([total_data,num_lesion,test_imgs_original.shape[2],test_imgs_original.shape[3]])
test_masks_temp = load_hdf5(DRIVE_test_groudTruth)#masks always the same
test_masks=test_masks_temp
test_imgs = my_PreProc(test_imgs_original)
print (np.max(test_masks),np.min(test_masks))
test_masks = test_masks/255.
#extend both images and masks so they can be divided exactly by the patches dimensions
#test_imgs = test_imgs[0:Imgs_to_test,:,:,:]
#test_masks = test_masks[0:Imgs_to_test,:,:,:]
test_imgs = paint_border_overlap(test_imgs, patch_height, patch_width, stride_height, stride_width)
test_masks = paint_border_overlap(test_masks,patch_height, patch_width, stride_height, stride_width)
#check masks are within 0-1
assert(np.max(test_masks)==1 and np.min(test_masks)==0)
print ("\ntest images shape:")
print (test_imgs.shape)
print ("\ntest mask shape:")
print (test_masks.shape)
print ("test images range (min-max): " +str(np.min(test_imgs)) +' - '+str(np.max(test_imgs)))
print ("test masks are within 0-1\n")
#extract the TEST patches from the full images
patches_imgs_test = extract_ordered_overlap(test_imgs,patch_height,patch_width,stride_height,stride_width)
print("\ntest PATCHES images shape:")
print( patches_imgs_test.shape)
print ("test PATCHES images range (min-max): " +str(np.min(patches_imgs_test)) +' - '+str(np.max(patches_imgs_test)))
return patches_imgs_test, test_imgs.shape[2], test_imgs.shape[3], test_masks
def get_data_training(DRIVE_train_imgs_original,
DRIVE_train_groudTruth,
patch_height,
patch_width,
N_subimgs,
inside_FOV,
num_lesion,
total_data
):
train_imgs_original = load_hdf5(DRIVE_train_imgs_original)#[img_id:img_id+1]
train_masks=np.zeros([total_data,num_lesion,train_imgs_original.shape[2],train_imgs_original.shape[3]])
for i in range(num_lesion):
train_masks_temp = load_hdf5(DRIVE_train_groudTruth +str(i+1)+'.hdf5')#[img_id:img_id+1]#masks always the same
train_masks[:,i,:,:]=train_masks_temp[:,0,:,:]
print("mask:",train_masks_temp.shape)
print(train_masks[:,0,:,:].shape)
print(train_imgs_original[:,0,:,:].shape)
train_imgs = my_PreProc(train_imgs_original)
print(train_imgs[:,0,:,:].shape)
train_masks = train_masks/255.
train_imgs = train_imgs[:,:,7:429,:] #cut bottom and top so now it is 422*422
train_masks = train_masks[:,:,7:429,:] #cut bottom and top so now it is 422*422
data_consistency_check(train_imgs,train_masks)
#check masks are within 0-1
assert(np.min(train_masks)==0 and np.max(train_masks)==1)
print ("\ntrain images shape:")
print (train_imgs.shape)
print ("\ntrain masks shape:")
print (train_masks.shape)
print ("train images 0 range (min-max): " +str(np.min(train_imgs[:,0])) +' - '+str(np.max(train_imgs[:,0])))
print ("train images 1 range (min-max): " +str(np.min(train_imgs[:,1])) +' - '+str(np.max(train_imgs[:,1])))
print ("train images 2 range (min-max): " +str(np.min(train_imgs[:,2])) +' - '+str(np.max(train_imgs[:,2])))
print ("train masks are within 0-1\n")
#extract the TRAINING patches from the full images
patches_imgs_train, patches_masks_train = extract_random(train_imgs,train_masks,patch_height,patch_width,N_subimgs,inside_FOV)
data_consistency_check(patches_imgs_train, patches_masks_train)
print ("\ntrain PATCHES images/masks shape:")
print (patches_imgs_train.shape)
print (patches_masks_train.shape)
print ("train PATCHES images range (min-max): " +str(np.min(patches_imgs_train)) +' - '+str(np.max(patches_imgs_train)))
return patches_imgs_train, patches_masks_train#, patches_imgs_test, patches_masks_test
def get_data_testing(DRIVE_test_imgs_original, DRIVE_test_groudTruth, Imgs_to_test, patch_height, patch_width,num_lesion,total_data):
### test
test_imgs_original = load_hdf5(DRIVE_test_imgs_original)
test_masks=np.zeros([total_data,num_lesion,test_imgs_original.shape[2],test_imgs_original.shape[3]])
for i in range(num_lesion):
test_masks_temp = load_hdf5(DRIVE_test_groudTruth +str(i+1)+'.hdf5')#[img_id:img_id+1]#masks always the same
test_masks[:,i,:,:]=test_masks_temp[:,0,:,:]
test_imgs = my_PreProc(test_imgs_original)
test_masks = test_masks/255.
#extend both images and masks so they can be divided exactly by the patches dimensions
test_imgs = test_imgs[0:Imgs_to_test,:,:,:]
test_masks = test_masks[0:Imgs_to_test,:,:,:]
test_imgs = paint_border(test_imgs,patch_height,patch_width)
test_masks = paint_border(test_masks,patch_height,patch_width)
data_consistency_check(test_imgs, test_masks)
#check masks are within 0-1
assert(np.max(test_masks)==1 and np.min(test_masks)==0)
print ("\ntest images/masks shape:")
print (test_imgs.shape)
print ("test images range (min-max): " +str(np.min(test_imgs)) +' - '+str(np.max(test_imgs)))
print ("test masks are within 0-1\n")
#extract the TEST patches from the full images
patches_imgs_test = extract_ordered(test_imgs,patch_height,patch_width)
patches_masks_test = extract_ordered(test_masks,patch_height,patch_width)
data_consistency_check(patches_imgs_test, patches_masks_test)
print ("\ntest PATCHES images/masks shape:")
print (patches_imgs_test.shape)
print ("test PATCHES images range (min-max): " +str(np.min(patches_imgs_test)) +' - '+str(np.max(patches_imgs_test)))
return patches_imgs_test, patches_masks_test
preds.append(outputs)
predictions = np.concatenate(preds, axis=0)
print("Predictions finished")
#===== Convert the prediction arrays in corresponding images
pred_patches = pred_to_imgs(predictions, patch_height, patch_width, "original")
#========== Elaborate and visualize the predicted images ====================
pred_imgs = None
orig_imgs = None
gtruth_masks = None
if average_mode == True:
pred_imgs = recompone_overlap(pred_patches, new_height, new_width,
stride_height, stride_width) # predictions
orig_imgs = my_PreProc(
test_imgs_orig[0:pred_imgs.shape[0], :, :, :]) #originals
gtruth_masks = masks_test #ground truth masks
else:
pred_imgs = recompone(pred_patches, 13, 12) # predictions
orig_imgs = recompone(patches_imgs_test, 13, 12) # originals
gtruth_masks = recompone(patches_masks_test, 13, 12) #masks
# apply the DRIVE masks on the repdictions #set everything outside the FOV to zero!!
kill_border(pred_imgs, test_border_masks) #DRIVE MASK #only for visualization
## back to original dimensions
orig_imgs = orig_imgs[:, :, 0:full_img_height, 0:full_img_width]
pred_imgs = pred_imgs[:, :, 0:full_img_height, 0:full_img_width]
gtruth_masks = gtruth_masks[:, :, 0:full_img_height, 0:full_img_width]
print("Orig imgs shape: " + str(orig_imgs.shape))
print("pred imgs shape: " + str(pred_imgs.shape))