Create shanghaitech.py

Author: Willy0919

src/lib/dataset/shanghaitech.py

@@ -0,0 +1,271 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# Written by Willy
+from __future__ import print_function, division
+from skimage import io, color
+from skimage import transform as sk_transform
+from torch.utils.data import Dataset, DataLoader
+from torchvision import transforms
+import os
+import torch
+import matplotlib.pyplot as plt
+import scipy.io as scio
+import warnings
+import numpy as np
+warnings.filterwarnings("ignore")
+import random
+from src.lib.utils.image_opt import genDensity,showGt,getPerspective,getLevel,getAttentionDensity,showMultiscale,findSigma
+
+class IsColor(object):
+    def __init__(self,color=True):
+        self.color = color
+    def __call__(self,sample):
+        image = sample['image']
+        if len(image.shape)==2:
+            if self.color:
+                image = color.gray2rgb(image)
+        else:
+            if not self.color:
+                image = color.rgb2gray(image)
+                _image = np.zeros((image.shape[0],image.shape[1],3),np.uint8)
+                _image[:, :, 0] = image*255
+                _image[:, :, 1] = image*255
+                _image[:, :, 2] = image*255
+                image = _image
+        sample['image'] = image
+        return sample
+
+
+class RandomFlip(object):
+    def __call__(self, sample):
+        if random.random() < 0.5:
+            image = sample['image']
+            sample['image'] = sample['image'][:,::-1]
+            if len(sample['dots']) != 0:
+                sample['dots'][:,0] = image.shape[1] -1 - sample['dots'][:,0]
+        return sample
+
+class PreferredSize(object):
+    def __init__(self,size = 0, use_multiscale=False):
+        self.size = size
+        self.use_multiscale=use_multiscale
+
+    def __call__(self,sample):
+        if self.size>0:
+            image = sample['image']
+
+            h,w,c = image.shape
+            ratio = 1
+            if h > w:
+                new_h, new_w = self.size, int(self.size * w / h+0.5)
+                ratio = self.size/h
+            else:
+                new_h, new_w = int(self.size * h / w+0.5), self.size
+                ratio = self.size/w
+
+            if self.use_multiscale:
+                multi_img = sample['scale_images']
+                
+                multi_img_new = []
+                for img_s in multi_img:
+                    h_s,w_s,c_s = img_s.shape
+                    if h_s > w_s:
+                        new_h_s, new_w_s = self.size, int(self.size * w_s / h_s + 0.5)
+                        ratio_s = self.size / h_s
+                    else:
+                        new_h_s, new_w_s = int(self.size * h_s / w_s + 0.5), self.size
+                        ratio_s = self.size / w_s
+
+                    resized_img_s = sk_transform.resize(img_s,(new_h_s,new_w_s),preserve_range=True)
+                    out_img_s = np.zeros((self.size,self.size,c_s),dtype=np.float32)
+                    out_img_s[...] = 127.5
+                    out_img_s[:new_h_s, :new_w_s, :] = resized_img_s
+                    multi_img_new.append(out_img_s)
+
+                sample['scale_images'] = np.array(multi_img_new)
+            sample['dots'] = sample['dots']*ratio
+
+            resized_image = sk_transform.resize(image,(new_h,new_w),preserve_range = True)
+            out_image = np.zeros((self.size,self.size,c),dtype=np.float32)
+            out_image[...] = 127.5
+            out_image[:new_h,:new_w,:] = resized_image
+            sample['image'] = out_image
+        return sample
+
+
+class NineCrop(object):
+    def __call__(self,sample):
+        image = sample['image']
+        dots = sample['dots']
+        sigmas = sample['sigma']
+        h,w = image.shape[:2]
+        i = random.randint(0,2)
+        j = random.randint(0,2)
+        left = int(w/4*i)
+        top = int(h/4*j)
+        width = int(w/2)
+        height = int(h/2)
+
+        image = image[top: top + height,
+                left: left + width]
+        if len(dots) != 0:
+            idx = np.where(
+                (dots[:, 0] >= left) & (dots[:, 1] >= top) & (dots[:, 0] < left+width) & (dots[:, 1] < top+height))
+            dots = dots[idx]
+            dots[:,0] -= left
+            dots[:,1] -= top
+            idx = idx[0].tolist()
+            
+            if len(idx)==0:
+                sigmas = torch.FloatTensor([])
+            else:
+                sigmas = sigmas.index_select(0,torch.LongTensor(idx))
+            
+        sample['image'] = image
+        sample['dots'] = dots
+        sample['sigma'] = sigmas
+        return sample
+
+class Multiscale(object):
+    def __init__(self,cropscale=[]):
+        self.cropscale = cropscale
+
+
+    def __call__(self,sample):
+        image = sample['image']
+
+        h,w = image.shape[:2]
+        cx = int(w/2)
+        cy = int(h/2)
+        scale_img = []
+        for i in self.cropscale:
+            scale_img.append(image[cy - int(h*i/2): cy + int(h*i/2),
+                cx - int(w*i/2): cx + int(w*i/2)])
+
+        sample['scale_images'] = np.array(scale_img)
+
+        return sample
+
+class ToTensor(object):
+    def __init__(self,rescale=1.0,margin_size = 1001,max_dot = 4000,use_att=False,use_multiscale=False):
+        self.rescale = rescale
+        self.margin_size = margin_size
+        self.max_dot = max_dot
+        self.use_att = use_att
+        self.use_multiscale = use_multiscale
+
+
+    def __call__(self,sample):
+        image = sample['image']
+        
+        dots = sample['dots']
+        sigmas = sample['sigma']
+        #sigmas = torch.FloatTensor(len(dots)).fill_(15.0)
+        if self.use_att:
+            densityMap = getAttentionDensity(image,3, dots, sigmas, self.margin_size,self.rescale)
+        else:
+            densityMap = genDensity(image, dots, sigmas, self.margin_size,self.rescale)
+        
+        if np.sum(densityMap)!= 0:
+            densityMap = densityMap * (len(dots) / np.sum(densityMap))
+        #densityMap = torch.FloatTensor(densityMap)
+       
+        image = image.transpose((2, 0, 1))
+        if self.use_multiscale:
+            multi_img = sample['scale_images']
+         
+            multi_img = multi_img.transpose((0,3,1,2))
+
+            sample['scale_images'] = multi_img.astype(np.float32)
+
+
+        outdots = np.zeros((self.max_dot,2))
+        #outdots[:dots.shape[0],:] = dots
+        count = len(dots)
+        if count:
+            outdots[:dots.shape[0],:] = dots
+        sample['image'] = image.astype(np.float32)
+        sample['densityMap'] = densityMap
+        sample['dots'] = outdots
+        sample['count'] = count
+        sample.pop('sigma')
+        return sample
+
+
+class Normalize(object):
+    def __init__(self,use_multiscale=False):
+        self.use_multiscale=use_multiscale
+
+    def __call__(self,sample):
+        image = sample['image']
+        image = (image - 127.5)/127.5
+        sample['image'] = image
+        if self.use_multiscale:
+            multi_img = sample['scale_images']
+            multi_img = (multi_img - 127.5)/127.5
+            
+            sample['scale_images'] = multi_img
+        return sample
+
+class HeadCountDataset(Dataset):
+
+    def __init__(self,max_iter,phase, data_file, transform=None,use_pers=False,use_attention=True):
+        self.data_file = data_file
+        f = open(self.data_file,'r')
+        self.data_idx = [i.strip() for i in f.readlines()]
+        f.close()
+        if phase == 'train':
+            self.data_idx = self.data_idx * int(np.ceil(float(max_iter) / len(self.data_idx)))
+        print('iteration length:', len(self.data_idx))
+        self.transform = transform
+        self.use_pmap = use_pers
+
+        self.root_path = os.path.abspath(os.path.dirname(__file__)+os.path.sep+"../../../")
+        self.use_att = use_attention
+
+    def __len__(self):
+        return len(self.data_idx)
+
+    def __getitem__(self, idx):
+        line = self.data_idx[idx]
+        img_path, dot_path = line.split(' ')
+        image = io.imread(os.path.join(self.root_path, img_path)).astype(np.float32)
+        notation = scio.loadmat(os.path.join(self.root_path, dot_path), struct_as_record=False, squeeze_me=True)
+        info = notation['image_info']
+        dots = info.location
+        idx = np.where((dots[:,0]>=0)&(dots[:,1]>=0)&(dots[:,0]<image.shape[1])&(dots[:,1]<image.shape[0]))
+        dots = dots[idx]
+        #sigma = findSigma(dots,3,0.3)
+        # sigma = torch.FloatTensor(len(dots)).fill_(15)
+        if self.use_pmap:
+            pmap_path = os.path.join(self.root_path, img_path.replace('images', 'pmap').replace('.jpg', '.mat'))
+            pmap_mat = scio.loadmat(pmap_path)
+            pmap = pmap_mat['pmap']
+            sigma = getPerspective(dots, pmap)
+
+        else:
+            sigma = findSigma(dots,3,0.3)
+        if self.use_att:
+
+            atv = getLevel(3, 0.1, np.array([3,9, 27]),dots,5)
+
+            sample = {'image': image, 'dots': dots, 'sigma': atv, 'image_path': img_path}
+        else:
+
+            sample = {'image': image, 'dots': dots,'sigma':sigma, 'image_path': img_path}
+        if self.transform:
+            sample = self.transform(sample)
+        return sample
+
+
+if __name__ == '__main__':
+
+    headcount_dataset = HeadCountDataset(250000,'train','data/ShanghaiTech/part_B_final/train_data.txt',use_pers=False,use_attention=False,
+                                         transform=transforms.Compose(
+                                             [IsColor(True),NineCrop(), RandomFlip(),Multiscale(cropscale=[0.75, 0.5]),PreferredSize(512,use_multiscale=True), ToTensor(use_att=False,use_multiscale=True), Normalize(use_multiscale=True)]))
+    dataloader = DataLoader(headcount_dataset, batch_size=1, shuffle=False, num_workers=1)
+
+    for i_batch, sample_batched in enumerate(dataloader):
+        print('success')
+        showMultiscale(sample_batched)
+