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sbb_pixelwise_segmentation
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sbb_pixelwise_segmentation/utils.py

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import os
import cv2
import numpy as np
import seaborn as sns
from scipy.ndimage.interpolation import map_coordinates
from scipy.ndimage.filters import gaussian_filter
import random
from tqdm import tqdm
import imutils
import math
def bluring(img_in,kind):
if kind=='guass':
img_blur = cv2.GaussianBlur(img_in,(5,5),0)
elif kind=="median":
img_blur = cv2.medianBlur(img_in,5)
elif kind=='blur':
img_blur=cv2.blur(img_in,(5,5))
return img_blur
def elastic_transform(image, alpha, sigma,seedj, random_state=None):
"""Elastic deformation of images as described in [Simard2003]_.
.. [Simard2003] Simard, Steinkraus and Platt, "Best Practices for
Convolutional Neural Networks applied to Visual Document Analysis", in
Proc. of the International Conference on Document Analysis and
Recognition, 2003.
"""
if random_state is None:
random_state = np.random.RandomState(seedj)
shape = image.shape
dx = gaussian_filter((random_state.rand(*shape) * 2 - 1), sigma, mode="constant", cval=0) * alpha
dy = gaussian_filter((random_state.rand(*shape) * 2 - 1), sigma, mode="constant", cval=0) * alpha
dz = np.zeros_like(dx)
x, y, z = np.meshgrid(np.arange(shape[1]), np.arange(shape[0]), np.arange(shape[2]))
indices = np.reshape(y+dy, (-1, 1)), np.reshape(x+dx, (-1, 1)), np.reshape(z, (-1, 1))
distored_image = map_coordinates(image, indices, order=1, mode='reflect')
return distored_image.reshape(image.shape)
def rotation_90(img):
img_rot=np.zeros((img.shape[1],img.shape[0],img.shape[2]))
img_rot[:,:,0]=img[:,:,0].T
img_rot[:,:,1]=img[:,:,1].T
img_rot[:,:,2]=img[:,:,2].T
return img_rot
def rotatedRectWithMaxArea(w, h, angle):
"""
Given a rectangle of size wxh that has been rotated by 'angle' (in
radians), computes the width and height of the largest possible
axis-aligned rectangle (maximal area) within the rotated rectangle.
"""
if w <= 0 or h <= 0:
return 0,0
width_is_longer = w >= h
side_long, side_short = (w,h) if width_is_longer else (h,w)
# since the solutions for angle, -angle and 180-angle are all the same,
# if suffices to look at the first quadrant and the absolute values of sin,cos:
sin_a, cos_a = abs(math.sin(angle)), abs(math.cos(angle))
if side_short <= 2.*sin_a*cos_a*side_long or abs(sin_a-cos_a) < 1e-10:
# half constrained case: two crop corners touch the longer side,
# the other two corners are on the mid-line parallel to the longer line
x = 0.5*side_short
wr,hr = (x/sin_a,x/cos_a) if width_is_longer else (x/cos_a,x/sin_a)
else:
# fully constrained case: crop touches all 4 sides
cos_2a = cos_a*cos_a - sin_a*sin_a
wr,hr = (w*cos_a - h*sin_a)/cos_2a, (h*cos_a - w*sin_a)/cos_2a
return wr,hr
def rotate_max_area(image,rotated, rotated_label,angle):
""" image: cv2 image matrix object
angle: in degree
"""
wr, hr = rotatedRectWithMaxArea(image.shape[1], image.shape[0],
math.radians(angle))
h, w, _ = rotated.shape
y1 = h//2 - int(hr/2)
y2 = y1 + int(hr)
x1 = w//2 - int(wr/2)
x2 = x1 + int(wr)
return rotated[y1:y2, x1:x2],rotated_label[y1:y2, x1:x2]
def rotation_not_90_func(img,label,thetha):
rotated=imutils.rotate(img,thetha)
rotated_label=imutils.rotate(label,thetha)
return rotate_max_area(img, rotated,rotated_label,thetha)
def color_images(seg, n_classes):
ann_u=range(n_classes)
if len(np.shape(seg))==3:
seg=seg[:,:,0]
seg_img=np.zeros((np.shape(seg)[0],np.shape(seg)[1],3)).astype(float)
colors=sns.color_palette("hls", n_classes)
for c in ann_u:
c=int(c)
segl=(seg==c)
seg_img[:,:,0]+=segl*(colors[c][0])
seg_img[:,:,1]+=segl*(colors[c][1])
seg_img[:,:,2]+=segl*(colors[c][2])
return seg_img
def resize_image(seg_in,input_height,input_width):
return cv2.resize(seg_in,(input_width,input_height),interpolation=cv2.INTER_NEAREST)
def get_one_hot(seg,input_height,input_width,n_classes):
seg=seg[:,:,0]
seg_f=np.zeros((input_height, input_width,n_classes))
for j in range(n_classes):
seg_f[:,:,j]=(seg==j).astype(int)
return seg_f
def IoU(Yi,y_predi):
## mean Intersection over Union
## Mean IoU = TP/(FN + TP + FP)
IoUs = []
classes_true=np.unique(Yi)
for c in classes_true:
TP = np.sum( (Yi == c)&(y_predi==c) )
FP = np.sum( (Yi != c)&(y_predi==c) )
FN = np.sum( (Yi == c)&(y_predi != c))
IoU = TP/float(TP + FP + FN)
print("class {:02.0f}: #TP={:6.0f}, #FP={:6.0f}, #FN={:5.0f}, IoU={:4.3f}".format(c,TP,FP,FN,IoU))
IoUs.append(IoU)
mIoU = np.mean(IoUs)
print("_________________")
print("Mean IoU: {:4.3f}".format(mIoU))
return mIoU
def data_gen(img_folder, mask_folder, batch_size,input_height, input_width,n_classes):
c = 0
n = [f for f in os.listdir(img_folder) if not f.startswith('.')]# os.listdir(img_folder) #List of training images
random.shuffle(n)
while True:
img = np.zeros((batch_size, input_height, input_width, 3)).astype('float')
mask = np.zeros((batch_size, input_height, input_width, n_classes)).astype('float')
for i in range(c, c+batch_size): #initially from 0 to 16, c = 0.
#print(img_folder+'/'+n[i])
try:
filename=n[i].split('.')[0]
train_img = cv2.imread(img_folder+'/'+n[i])/255.
train_img = cv2.resize(train_img, (input_width, input_height),interpolation=cv2.INTER_NEAREST)# Read an image from folder and resize
img[i-c] = train_img #add to array - img[0], img[1], and so on.
train_mask = cv2.imread(mask_folder+'/'+filename+'.png')
#print(mask_folder+'/'+filename+'.png')
#print(train_mask.shape)
train_mask = get_one_hot( resize_image(train_mask,input_height,input_width),input_height,input_width,n_classes)
#train_mask = train_mask.reshape(224, 224, 1) # Add extra dimension for parity with train_img size [512 * 512 * 3]
mask[i-c] = train_mask
except:
img[i-c] = np.ones((input_height, input_width, 3)).astype('float')
mask[i-c] = np.zeros((input_height, input_width, n_classes)).astype('float')
c+=batch_size
if(c+batch_size>=len(os.listdir(img_folder))):
c=0
random.shuffle(n)
yield img, mask
def otsu_copy(img):
img_r=np.zeros(img.shape)
img1=img[:,:,0]
img2=img[:,:,1]
img3=img[:,:,2]
_, threshold1 = cv2.threshold(img1, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)
_, threshold2 = cv2.threshold(img2, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)
_, threshold3 = cv2.threshold(img3, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)
img_r[:,:,0]=threshold1
img_r[:,:,1]=threshold1
img_r[:,:,2]=threshold1
return img_r
def get_patches(dir_img_f,dir_seg_f,img,label,height,width,indexer):
if img.shape[0]<height or img.shape[1]<width:
img,label=do_padding(img,label,height,width)
img_h=img.shape[0]
img_w=img.shape[1]
nxf=img_w/float(width)
nyf=img_h/float(height)
if nxf>int(nxf):
nxf=int(nxf)+1
if nyf>int(nyf):
nyf=int(nyf)+1
nxf=int(nxf)
nyf=int(nyf)
for i in range(nxf):
for j in range(nyf):
index_x_d=i*width
index_x_u=(i+1)*width
index_y_d=j*height
index_y_u=(j+1)*height
if index_x_u>img_w:
index_x_u=img_w
index_x_d=img_w-width
if index_y_u>img_h:
index_y_u=img_h
index_y_d=img_h-height
img_patch=img[index_y_d:index_y_u,index_x_d:index_x_u,:]
label_patch=label[index_y_d:index_y_u,index_x_d:index_x_u,:]
cv2.imwrite(dir_img_f+'/img_'+str(indexer)+'.png', img_patch )
cv2.imwrite(dir_seg_f+'/img_'+str(indexer)+'.png' , label_patch )
indexer+=1
return indexer
def do_padding(img,label,height,width):
height_new=img.shape[0]
width_new=img.shape[1]
h_start=0
w_start=0
if img.shape[0]<height:
h_start=int( abs(height-img.shape[0])/2. )
height_new=height
if img.shape[1]<width:
w_start=int( abs(width-img.shape[1])/2. )
width_new=width
img_new=np.ones((height_new,width_new,img.shape[2])).astype(float)*255
label_new=np.zeros((height_new,width_new,label.shape[2])).astype(float)
img_new[h_start:h_start+img.shape[0],w_start:w_start+img.shape[1],:]=np.copy(img[:,:,:])
label_new[h_start:h_start+label.shape[0],w_start:w_start+label.shape[1],:]=np.copy(label[:,:,:])
return img_new,label_new
def get_patches_num_scale(dir_img_f,dir_seg_f,img,label,height,width,indexer,n_patches,scaler):
if img.shape[0]<height or img.shape[1]<width:
img,label=do_padding(img,label,height,width)
img_h=img.shape[0]
img_w=img.shape[1]
height_scale=int(height*scaler)
width_scale=int(width*scaler)
nxf=img_w/float(width_scale)
nyf=img_h/float(height_scale)
if nxf>int(nxf):
nxf=int(nxf)+1
if nyf>int(nyf):
nyf=int(nyf)+1
nxf=int(nxf)
nyf=int(nyf)
for i in range(nxf):
for j in range(nyf):
index_x_d=i*width_scale
index_x_u=(i+1)*width_scale
index_y_d=j*height_scale
index_y_u=(j+1)*height_scale
if index_x_u>img_w:
index_x_u=img_w
index_x_d=img_w-width_scale
if index_y_u>img_h:
index_y_u=img_h
index_y_d=img_h-height_scale
img_patch=img[index_y_d:index_y_u,index_x_d:index_x_u,:]
label_patch=label[index_y_d:index_y_u,index_x_d:index_x_u,:]
img_patch=resize_image(img_patch,height,width)
label_patch=resize_image(label_patch,height,width)
cv2.imwrite(dir_img_f+'/img_'+str(indexer)+'.png', img_patch )
cv2.imwrite(dir_seg_f+'/img_'+str(indexer)+'.png' , label_patch )
indexer+=1
return indexer
def get_patches_num_scale_new(dir_img_f,dir_seg_f,img,label,height,width,indexer,scaler):
img=resize_image(img,int(img.shape[0]*scaler),int(img.shape[1]*scaler))
label=resize_image(label,int(label.shape[0]*scaler),int(label.shape[1]*scaler))
if img.shape[0]<height or img.shape[1]<width:
img,label=do_padding(img,label,height,width)
img_h=img.shape[0]
img_w=img.shape[1]
height_scale=int(height*1)
width_scale=int(width*1)
nxf=img_w/float(width_scale)
nyf=img_h/float(height_scale)
if nxf>int(nxf):
nxf=int(nxf)+1
if nyf>int(nyf):
nyf=int(nyf)+1
nxf=int(nxf)
nyf=int(nyf)
for i in range(nxf):
for j in range(nyf):
index_x_d=i*width_scale
index_x_u=(i+1)*width_scale
index_y_d=j*height_scale
index_y_u=(j+1)*height_scale
if index_x_u>img_w:
index_x_u=img_w
index_x_d=img_w-width_scale
if index_y_u>img_h:
index_y_u=img_h
index_y_d=img_h-height_scale
img_patch=img[index_y_d:index_y_u,index_x_d:index_x_u,:]
label_patch=label[index_y_d:index_y_u,index_x_d:index_x_u,:]
#img_patch=resize_image(img_patch,height,width)
#label_patch=resize_image(label_patch,height,width)
cv2.imwrite(dir_img_f+'/img_'+str(indexer)+'.png', img_patch )
cv2.imwrite(dir_seg_f+'/img_'+str(indexer)+'.png' , label_patch )
indexer+=1
return indexer
def provide_patches(dir_img,dir_seg,dir_flow_train_imgs,
dir_flow_train_labels,
input_height,input_width,blur_k,blur_aug,
flip_aug,binarization,scaling,scales,flip_index,
scaling_bluring,scaling_binarization,rotation,
rotation_not_90,thetha,scaling_flip,
augmentation=False,patches=False):
imgs_cv_train=np.array(os.listdir(dir_img))
segs_cv_train=np.array(os.listdir(dir_seg))
indexer=0
for im, seg_i in tqdm(zip(imgs_cv_train,segs_cv_train)):
img_name=im.split('.')[0]
if not patches:
cv2.imwrite(dir_flow_train_imgs+'/img_'+str(indexer)+'.png', resize_image(cv2.imread(dir_img+'/'+im),input_height,input_width ) )
cv2.imwrite(dir_flow_train_labels+'/img_'+str(indexer)+'.png' , resize_image(cv2.imread(dir_seg+'/'+img_name+'.png'),input_height,input_width ) )
indexer+=1
if augmentation:
if flip_aug:
for f_i in flip_index:
cv2.imwrite(dir_flow_train_imgs+'/img_'+str(indexer)+'.png',
resize_image(cv2.flip(cv2.imread(dir_img+'/'+im),f_i),input_height,input_width) )
cv2.imwrite(dir_flow_train_labels+'/img_'+str(indexer)+'.png' ,
resize_image(cv2.flip(cv2.imread(dir_seg+'/'+img_name+'.png'),f_i),input_height,input_width) )
indexer+=1
if blur_aug:
for blur_i in blur_k:
cv2.imwrite(dir_flow_train_imgs+'/img_'+str(indexer)+'.png',
(resize_image(bluring(cv2.imread(dir_img+'/'+im),blur_i),input_height,input_width) ) )
cv2.imwrite(dir_flow_train_labels+'/img_'+str(indexer)+'.png' ,
resize_image(cv2.imread(dir_seg+'/'+img_name+'.png'),input_height,input_width) )
indexer+=1
if binarization:
cv2.imwrite(dir_flow_train_imgs+'/img_'+str(indexer)+'.png',
resize_image(otsu_copy( cv2.imread(dir_img+'/'+im)),input_height,input_width ))
cv2.imwrite(dir_flow_train_labels+'/img_'+str(indexer)+'.png',
resize_image( cv2.imread(dir_seg+'/'+img_name+'.png'),input_height,input_width ))
indexer+=1
if patches:
indexer=get_patches(dir_flow_train_imgs,dir_flow_train_labels,
cv2.imread(dir_img+'/'+im),cv2.imread(dir_seg+'/'+img_name+'.png'),
input_height,input_width,indexer=indexer)
if augmentation:
if rotation:
indexer=get_patches(dir_flow_train_imgs,dir_flow_train_labels,
rotation_90( cv2.imread(dir_img+'/'+im) ),
rotation_90( cv2.imread(dir_seg+'/'+img_name+'.png') ),
input_height,input_width,indexer=indexer)
if rotation_not_90:
for thetha_i in thetha:
img_max_rotated,label_max_rotated=rotation_not_90_func(cv2.imread(dir_img+'/'+im),cv2.imread(dir_seg+'/'+img_name+'.png'),thetha_i)
indexer=get_patches(dir_flow_train_imgs,dir_flow_train_labels,
img_max_rotated,
label_max_rotated,
input_height,input_width,indexer=indexer)
if flip_aug:
for f_i in flip_index:
indexer=get_patches(dir_flow_train_imgs,dir_flow_train_labels,
cv2.flip( cv2.imread(dir_img+'/'+im) , f_i),
cv2.flip( cv2.imread(dir_seg+'/'+img_name+'.png') ,f_i),
input_height,input_width,indexer=indexer)
if blur_aug:
for blur_i in blur_k:
indexer=get_patches(dir_flow_train_imgs,dir_flow_train_labels,
bluring( cv2.imread(dir_img+'/'+im) , blur_i),
cv2.imread(dir_seg+'/'+img_name+'.png'),
input_height,input_width,indexer=indexer)
if scaling:
for sc_ind in scales:
indexer=get_patches_num_scale_new(dir_flow_train_imgs,dir_flow_train_labels,
cv2.imread(dir_img+'/'+im) ,
cv2.imread(dir_seg+'/'+img_name+'.png'),
input_height,input_width,indexer=indexer,scaler=sc_ind)
if binarization:
indexer=get_patches(dir_flow_train_imgs,dir_flow_train_labels,
otsu_copy( cv2.imread(dir_img+'/'+im)),
cv2.imread(dir_seg+'/'+img_name+'.png'),
input_height,input_width,indexer=indexer)
if scaling_bluring:
for sc_ind in scales:
for blur_i in blur_k:
indexer=get_patches_num_scale_new(dir_flow_train_imgs,dir_flow_train_labels,
bluring( cv2.imread(dir_img+'/'+im) , blur_i) ,
cv2.imread(dir_seg+'/'+img_name+'.png') ,
input_height,input_width,indexer=indexer,scaler=sc_ind)
if scaling_binarization:
for sc_ind in scales:
indexer=get_patches_num_scale_new(dir_flow_train_imgs,dir_flow_train_labels,
otsu_copy( cv2.imread(dir_img+'/'+im)) ,
cv2.imread(dir_seg+'/'+img_name+'.png'),
input_height,input_width,indexer=indexer,scaler=sc_ind)
if scaling_flip:
for sc_ind in scales:
for f_i in flip_index:
indexer=get_patches_num_scale_new(dir_flow_train_imgs,dir_flow_train_labels,
cv2.flip( cv2.imread(dir_img+'/'+im) , f_i) ,
cv2.flip(cv2.imread(dir_seg+'/'+img_name+'.png') ,f_i) ,
input_height,input_width,indexer=indexer,scaler=sc_ind)
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