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for img_path in img_paths:
print (img_path)
mat = io.loadmat(img_path.replace('.jpg','.mat').replace('images','ground-truth').replace('IMG_','GT_IMG_'))
img= plt.imread(img_path)
k = np.zeros((img.shape[0],img.shape[1]))
gt = mat["image_info"][0,0][0,0][0]
for i in range(0,len(gt)):
if int(gt[i][1])<img.shape[0] and int(gt[i][0])<img.shape[1]:
k[int(gt[i][1]),int(gt[i][0])]=1
k = gaussian_filter_density(k)
with h5py.File(img_path.replace('.jpg','.h5').replace('images','ground-truth'), 'w') as hf:
hf['density'] = k
# function to create density maps for images
def gaussian_filter_density(gt):
print (gt.shape)
density = np.zeros(gt.shape, dtype=np.float32)
gt_count = np.count_nonzero(gt)
if gt_count == 0:
return density
pts = np.array(list(zip(np.nonzero(gt)[1], np.nonzero(gt)[0])))
leafsize = 2048
# build kdtree
tree = scipy.spatial.KDTree(pts.copy(), leafsize=leafsize)
# query kdtree
distances, locations = tree.query(pts, k=4)
print ('generate density...')
for i, pt in enumerate(pts):
pt2d = np.zeros(gt.shape, dtype=np.float32)
pt2d[pt[1],pt[0]] = 1.
if gt_count > 1:
sigma = (distances[i][1]+distances[i][2]+distances[i][3])*0.1
else:
sigma = np.average(np.array(gt.shape))/2./2. #case: 1 point
density += scipy.ndimage.filters.gaussian_filter(pt2d, sigma, mode='constant')
print ('done.')
return density
part_A_train = os.path.join(root,'part_A/train_data','images')
part_A_test = os.path.join(root,'part_A/test_data','images')
part_B_train = os.path.join(root,'part_B/train_data','images')
part_B_test = os.path.join(root,'part_B/test_data','images')
path_sets = [part_A_train,part_A_test]
img_paths = []
for path in path_sets:
for img_path in glob.glob(os.path.join(path, '*.jpg')):
img_paths.append(img_path)
#defining the image path
img_paths = []
for path in path_sets:
for img_path in glob.glob(os.path.join(path, '*.jpg')):
img_paths.append(img_path)
# importing libraries
import h5py
import scipy.io as io
import PIL.Image as Image
import numpy as np
import os
import glob
from matplotlib import pyplot as plt
from scipy.ndimage.filters import gaussian_filter
import scipy
import json
from matplotlib import cm as CM
from image import *
from model import CSRNet
import torch
from tqdm import tqdm
%matplotlib inline
#importing libraries
import h5py
import scipy.io as io
import PIL.Image as Image
import numpy as np
import os
import glob
from matplotlib import pyplot as plt
from scipy.ndimage.filters import gaussian_filter
import scipy
import json
import torchvision.transforms.functional as F
from matplotlib import cm as CM
from image import *
from model import CSRNet
import torch
%matplotlib inline
#defining the location of dataset
root = '/home/pulkit/CSRNet/ShanghaiTech/CSRNet-pytorch/'
part_A_train = os.path.join(root,'part_A/train_data','images')
part_A_test = os.path.join(root,'part_A/test_data','images')
part_B_train = os.path.join(root,'part_B/train_data','images')
part_B_test = os.path.join(root,'part_B/test_data','images')
path_sets = [part_A_test]
mae = 0
for i in tqdm(range(len(img_paths))):
img = transform(Image.open(img_paths[i]).convert('RGB')).cuda()
gt_file = h5py.File(img_paths[i].replace('.jpg','.h5').replace('images','ground-truth'),'r')
groundtruth = np.asarray(gt_file['density'])
output = model(img.unsqueeze(0))
mae += abs(output.detach().cpu().sum().numpy()-np.sum(groundtruth))
print (mae/len(img_paths))
path_sets = [part_B_train,part_B_test]
img_paths = []
for path in path_sets:
for img_path in glob.glob(os.path.join(path, '*.jpg')):
img_paths.append(img_path)
# creating density map for part_b images
for img_path in img_paths:
print (img_path)
mat = io.loadmat(img_path.replace('.jpg','.mat').replace('images','ground-truth').replace('IMG_','GT_IMG_'))
img= plt.imread(img_path)
k = np.zeros((img.shape[0],img.shape[1]))
gt = mat["image_info"][0,0][0,0][0]
for i in range(0,len(gt)):
if int(gt[i][1])<img.shape[0] and int(gt[i][0])<img.shape[1]:
k[int(gt[i][1]),int(gt[i][0])]=1
k = gaussian_filter_density(k)
with h5py.File(img_path.replace('.jpg','.h5').replace('images','ground-truth'), 'w') as hf:
hf['density'] = k
from matplotlib import cm as c
img = transform(Image.open('part_A/test_data/images/IMG_100.jpg').convert('RGB')).cuda()
output = model(img.unsqueeze(0))
print("Predicted Count : ",int(output.detach().cpu().sum().numpy()))
temp = np.asarray(output.detach().cpu().reshape(output.detach().cpu().shape[2],output.detach().cpu().shape[3]))
plt.imshow(temp,cmap = c.jet)
plt.show()
temp = h5py.File('part_A/test_data/ground-truth/IMG_100.h5', 'r')
temp_1 = np.asarray(temp['density'])
plt.imshow(temp_1,cmap = c.jet)
print("Original Count : ",int(np.sum(temp_1)) + 1)
plt.show()
print("Original Image")
plt.imshow(plt.imread('part_A/test_data/images/IMG_100.jpg'))
plt.show()
#setting the root to the Shanghai dataset you have downloaded
# change the root path as per your location of dataset
root = '/home/pulkit/CSRNet-pytorch/'
gt_file = h5py.File(img_paths[0].replace('.jpg','.h5').replace('images','ground-truth'),'r')
groundtruth = np.asarray(gt_file['density'])
plt.imshow(groundtruth,cmap=CM.jet)
plt.imshow(Image.open(img_paths[0]))
np.sum(groundtruth)
from torchvision import datasets, transforms
transform=transforms.Compose([
transforms.ToTensor(),transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])
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