demo.py

import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.autograd import Variable
import cv2
import sys
import numpy as np
from skimage import segmentation
import torch.nn.init

from pathlib import Path


use_cuda = torch.cuda.is_available()


parser = argparse.ArgumentParser(description='PyTorch Unsupervised Segmentation')
parser.add_argument('--nChannel', metavar='N', default=100, type=int, 
                    help='number of channels')
parser.add_argument('--maxIter', metavar='T', default=1000, type=int, 
                    help='number of maximum iterations')
parser.add_argument('--minLabels', metavar='minL', default=3, type=int, 
                    help='minimum number of labels')
parser.add_argument('--lr', metavar='LR', default=0.1, type=float, 
                    help='learning rate')
parser.add_argument('--nConv', metavar='M', default=2, type=int, 
                    help='number of convolutional layers')
parser.add_argument('--num_superpixels', metavar='K', default=10000, type=int, 
                    help='number of superpixels')
parser.add_argument('--compactness', metavar='C', default=100, type=float, 
                    help='compactness of superpixels')
parser.add_argument('--visualize', metavar='1 or 0', default=0, type=int, 
                    help='visualization flag')
parser.add_argument('--input', metavar='FILENAME',
                    help='input image file name', required=True)
args = parser.parse_args()


# CNN model
class MyNet(nn.Module):
    def __init__(self,input_dim):
        super(MyNet, self).__init__()
        self.conv1 = nn.Conv2d(input_dim, args.nChannel, kernel_size=3, stride=1, padding=1 )
        self.bn1 = nn.BatchNorm2d(args.nChannel)
        self.conv2 = nn.ModuleList()
        self.bn2 = nn.ModuleList()
        for i in range(args.nConv-1):
            self.conv2.append( nn.Conv2d(args.nChannel, args.nChannel, kernel_size=3, stride=1, padding=1 ) )
            self.bn2.append( nn.BatchNorm2d(args.nChannel) )
        self.conv3 = nn.Conv2d(args.nChannel, args.nChannel, kernel_size=1, stride=1, padding=0 )
        self.bn3 = nn.BatchNorm2d(args.nChannel)

    def forward(self, x):
        x = self.conv1(x)
        x = F.relu( x )
        x = self.bn1(x)
        for i in range(args.nConv-1):
            x = self.conv2[i](x)
            x = F.relu( x )
            x = self.bn2[i](x)
        x = self.conv3(x)
        x = self.bn3(x)
        return x
    

# load image
im = cv2.imread(args.input)
im_rgb = cv2.imread(args.input)

#norm_img = np.zeros(im.shape)

#im = cv2.GaussianBlur(im, (5,5), 1, 1)

data = torch.from_numpy( np.array([im.transpose( (2, 0, 1) ).astype('float32')/255.]) )

if use_cuda:
    print("CUDA DATA")
    data = data.cuda()
data = Variable(data)

# slic
import time
start = time.time()

im = cv2.cvtColor(im, cv2.COLOR_BGR2LAB )

#labels = SlicAvx2(num_components=10000, compactness=args.compactness,  num_threads=4)

labels = segmentation.slic(im, compactness=args.compactness, n_segments=args.num_superpixels, min_size_factor=0.1)
print("TIME TAKEN SLIC: ", time.time() - start)

######################## Considering EDGE INFORMATION ######################
# Grey scale im
# Do Canny
# Get B&W Edge Map 
# Get Labels for that 
# Every Edge Component a Different Label

# bw = cv2.Canny(im, 10, 200) 
# from skimage.measure import label 
# bw_label = label(bw) 
# labels[bw == 255] = bw_label[bw == 255] + np.max(labels) 

##########################################################################

labels = labels.reshape(im.shape[0]*im.shape[1])
u_labels = np.unique(labels)
l_inds = []
for i in range(len(u_labels)):
    l_inds.append( np.where( labels == u_labels[ i ] )[ 0 ] )

# train
model = MyNet( data.size(1) )
if use_cuda:
    print("CUDA MODEL")
    model.cuda()
model.train() 
loss_fn = torch.nn.CrossEntropyLoss()
#optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=0.9)
optimizer = optim.Adam(model.parameters(), lr=args.lr)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience = 5, verbose = True)

label_colours = np.random.randint(255,size=(100,3))

for batch_idx in range(args.maxIter):
    # forwarding
    optimizer.zero_grad() 
    
    start_t = time.time()
    
    output = model( data )[ 0 ]
    output = output.permute( 1, 2, 0 ).contiguous().view( -1, args.nChannel )
    ignore, target = torch.max( output, 1 ) 
    im_target = target.data.cpu().numpy()
    nLabels = len(np.unique(im_target))
    if args.visualize: 
        im_target_rgb = np.array([label_colours[ c % 100 ] for c in im_target])
        im_target_rgb = im_target_rgb.reshape( im.shape ).astype( np.uint8 )
        #cv2_imshow( "output", im_target_rgb )
        #cv2.waitKey(10)
        cv2_imshow(im_target_rgb )
  
    # superpixel refinement
    # TODO: use Torch Variable instead of numpy for faster calculation
    for i in range(len(l_inds)):
        labels_per_sp = im_target[ l_inds[ i ] ]
        u_labels_per_sp = np.unique( labels_per_sp )
        hist = np.zeros( len(u_labels_per_sp) )
        for j in range(len(hist)):
            hist[ j ] = len( np.where( labels_per_sp == u_labels_per_sp[ j ] )[ 0 ] )
        im_target[ l_inds[ i ] ] = u_labels_per_sp[ np.argmax( hist ) ]
    target = torch.from_numpy( im_target )
    if use_cuda:
        target = target.cuda()
    target = Variable( target )
    loss = loss_fn(output, target)
    loss.backward()
    optimizer.step()
    scheduler.step(loss)

    #print (batch_idx, '/', args.maxIter, ':', nLabels, loss.data[0])
    print (batch_idx, '/', args.maxIter, ':', nLabels, loss.item(), time.time() - start_t)

    PATH = 'model.pth'
    torch.save(model.state_dict(), PATH)


    if nLabels <= args.minLabels:
        print ("nLabels", nLabels, "reached minLabels", args.minLabels, ".")
        break

from skimage.color import label2rgb
# save output image
if not args.visualize:
    output = model( data )[ 0 ]
    output = output.permute( 1, 2, 0 ).contiguous().view( -1, args.nChannel )
    ignore, target = torch.max( output, 1 )
    
    im_target = target.data.cpu().numpy()
    im_target = im_target.reshape( im.shape[:-1] )
    np.save('label.npy', im_target)

    target_label = np.array([label_colours[ c % 100 ] for c in im_target])
   
    im_target_rgb = label2rgb(im_target, image=im_rgb,  kind = 'avg', bg_label = -1).astype( np.uint8 )
    #im_target_label = label2rgb(im_target, bg_label = 0).astype( np.uint8 )

    combined = np.hstack((im_target_rgb, target_label))
    
    combined_orig_out = np.hstack((im_rgb, im_target_rgb))

    target_label = target_label.reshape( im.shape ).astype( np.uint8 )
    
cv2.imwrite("PostProcessing.jpg", im_target)
cv2.imwrite( "output.png", im_target_rgb )
cv2.imwrite("Label.png", target_label)

cv2.imwrite("Combined.png", combined)
cv2.imwrite("ORIG-Compressed.png", combined_orig_out)