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helpers_img.py
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helpers_img.py
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import matplotlib.image as mpimg
import numpy as np
import matplotlib.pyplot as plt
import os,sys
from PIL import Image
from tqdm import tqdm
from scipy import ndimage
import torch.nn.functional as F
import torch as tc
from mask_to_submission import *
# Helper functions
def load_image(infilename):
"""
Use the scipy.misc to load the image.
"""
data = mpimg.imread(infilename)
return data
def img_float_to_uint8(img):
"""
Change the image elements type.
"""
rimg = img - np.min(img)
rimg = (rimg / np.max(rimg) * 255).round().astype(np.uint8)
return rimg
def concatenate_images(img, gt_img):
"""
Concatenate an image and its groundtruth.
"""
nChannels = len(gt_img.shape)
w = gt_img.shape[0]
h = gt_img.shape[1]
if nChannels == 3:
cimg = np.concatenate((img, gt_img), axis=1)
else:
gt_img_3c = np.zeros((w, h, 3), dtype=np.uint8)
gt_img8 = img_float_to_uint8(gt_img)
gt_img_3c[:,:,0] = gt_img8
gt_img_3c[:,:,1] = gt_img8
gt_img_3c[:,:,2] = gt_img8
img8 = img_float_to_uint8(img)
cimg = np.concatenate((img8, gt_img_3c), axis=1)
return cimg
def value_to_class(v):
"""
Assign a class 1/0 (road or background) to v.
"""
df = np.sum(v)
if df > foreground_threshold:
return 1
else:
return 0
def img_crop(im, w, h):
"""
Crops an image im in patches of size w,h.
"""
list_patches = []
imgwidth = im.shape[0]
imgheight = im.shape[1]
is_2d = len(im.shape) < 3
for i in range(0,imgheight,h):
for j in range(0,imgwidth,w):
if is_2d:
im_patch = im[j:j+w, i:i+h]
else:
im_patch = im[j:j+w, i:i+h, :]
list_patches.append(im_patch)
return list_patches
def img_crop_mod(im, w, h):
"""
Crops an image im in patches of size 3*w,3*h.
"""
list_patches = []
imgwidth = im.shape[0]
imgheight = im.shape[1]
is_2d = len(im.shape) < 3
for i in range(h,imgheight-h,h):
for j in range(w,imgwidth-w,w):
if is_2d:
im_patch = im[j-w:j+2*w, i-h:i+2*h]
else:
im_patch = im[j-w:j+2*w, i-h:i+2*h, :]
list_patches.append(im_patch)
return list_patches
def from_mask_to_vector(mask_imgs,threshold):
"""
Takes as input a list of mask and returns a vector where the ith component
is 1 if the majority of the ith mask list element is 1.
"""
mask_imgs=np.array(mask_imgs).reshape(-1,16,16)
vector=mask_imgs.sum(axis=(1,2))
vector=vector[:] > mask_imgs.shape[1]*mask_imgs.shape[2]*threshold
return vector
def transform_subIMG_to_Tensor(sub_img_list):
'''Transform a list of list of sub images into a Tensor,
where the first index point to different sub images'''
N0= len(sub_img_list)
N1= len(sub_img_list[0])
N=N0*N1
x,y,channels=sub_img_list[0][0].shape
tensor=tc.Tensor(N,channels,x,y)
for j,image in enumerate(sub_img_list):
for k,sub_image in enumerate(image):
tensor[j*N1+k,:,:,:]=tc.FloatTensor(np.array([sub_image[:,:,i] for i in range(sub_image.shape[2])]))
return tensor
def reduce_dataset(dataset,label):
"""
Balance the number of zeros and ones in an image.
"""
new_dataset=[]
new_label=[]
nb_zeros=0
ones_positions=np.where(label)[0]
for pos in ones_positions:
new_dataset.append(dataset.narrow(0,int(pos),1))
new_label.append(1)
while nb_zeros < len(ones_positions):
j= np.random.randint(0,dataset.size(0))
if label[j]==0:
#print(j)
new_dataset.append(dataset.narrow(0,j,1))
new_label.append(0)
nb_zeros+=1
new_dataset=tc.cat(new_dataset,dim=0)
new_label=np.array(new_label)
return new_dataset,new_label
def label_to_img(imgwidth, imgheight, w, h, labels):
"""
Transform list of labels to image.
"""
im = np.zeros([imgwidth, imgheight])
m = np.zeros([imgwidth, imgheight])
idx = 0
for i in range(0,imgheight,h):
for j in range(0,imgwidth,w):
im[j:j+w, i:i+h] = labels[idx]
idx = idx + 1
return im
def make_img_overlay(img, predicted_img):
"""
Combines image and prediction.
"""
w = img.shape[0]
h = img.shape[1]
color_mask = np.zeros((w, h, 3), dtype=np.uint8)
color_mask[:,:,0] = predicted_img*255
img8 = img_float_to_uint8(img)
background = Image.fromarray(img8, 'RGB').convert("RGBA")
overlay = Image.fromarray(color_mask, 'RGB').convert("RGBA")
new_img = Image.blend(background, overlay, 0.2)
return new_img
def compute_F1(Y,Z):
"""
Compute F1 metric when inputs are LISTs
Input: Y, true value assigned to the patch.
Z, list of prediction for patches
"""
TN = 0
FP = 0
FN = 0
TP = 0
matrix = []
for i in range(len(Y)):
if (round(Y[i])==0) & (Z[i]==0):
TN = TN + 1
elif (round(Y[i])==1) & (Z[i]==0):
FN = FN + 1
elif (round(Y[i])==1) & (Z[i]==1):
TP = TP + 1
else:
FP = FP + 1
F1_score = 0
try:
precision = TP/(TP+FP)
recall = TP/(TP+FN)
F1_score = 2*precision*recall / (precision+recall)
except:
print('Something goes wrong...')
return F1_score
def calcul_F1(mask, prediction):
"""
Compute F1 metric when inputs are MATRICES
Input: mask, true values
prediction, predicted values.
"""
TN = 0
FP = 0
FN = 0
TP = 0
for i in range(mask.shape[0]):
for j in range(mask.shape[1]):
if (round(mask[i,j])==0) & (prediction[i,j]==0):
TN = TN + 1
elif (round(mask[i,j])==1) & (prediction[i,j]==0):
FN = FN + 1
elif (round(mask[i,j])==1) & (prediction[i,j]==1):
TP = TP + 1
else:
FP = FP + 1
F1_score = 0
try:
precision = TP/(TP+FP)
recall = TP/(TP+FN)
F1_score = 2*precision*recall / (precision+recall)
except:
print('Something goes wrong...')
return F1_score