File size: 6,985 Bytes
6263bf9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 |
from __future__ import print_function, division
import os
import torch
import pandas as pd
from skimage import io, transform
import numpy as np
import matplotlib.pyplot as plt
from torch.utils.data import Dataset, DataLoader
from torchvision.transforms import InterpolationMode
from torchvision import transforms, utils
from torchvision.utils import save_image
import cv2
import random
# Ignore warnings
import warnings
import os
import json
from PIL import Image
warnings.filterwarnings("ignore")
def cropping_preprocess(image):
non_zero_pixels = np.where(image != 255)
y_min, x_min = np.min(non_zero_pixels[0]), np.min(non_zero_pixels[1])
y_max, x_max = np.max(non_zero_pixels[0]), np.max(non_zero_pixels[1])
top_left = (x_min, y_min)
top_right = (x_max, y_min)
bottom_left = (x_min, y_max)
bottom_right = (x_max, y_max)
height = bottom_right[1] - top_left[1] + 1
width = bottom_right[0] - top_left[0] + 1
cropped_img = image[top_left[1]:top_left[1] + height, top_left[0]:top_left[0] + width]
h,w = cropped_img.shape[:2]
if h>224 and w>224:
return cropped_img
else:
scale_factor_h = 224 / h
scale_factor_w = 224 / w
new_width = int(w * scale_factor_w)
new_height = int(h * scale_factor_h)
resized_image = cv2.resize(cropped_img, (new_width, new_height), interpolation=cv2.INTER_AREA)
# print(resized_image.shape)
return resized_image
class RandomRegionBlackOut(object):
def __init__(self, p=0.5, blackout_ratio=0.2):
self.p = p # Probability of applying the transform
self.blackout_ratio = blackout_ratio # Ratio of the image area to blackout
def __call__(self, img):
if random.random() < self.p:
channels, width, height = img.shape
mask_width = int(width * self.blackout_ratio)
mask_height = int(height * self.blackout_ratio)
start_x = random.randint(0, width - mask_width)
start_y = random.randint(0, height - mask_height)
img[:, start_x:start_x+mask_width, start_y:start_y+mask_height] = 0.0
return img
class RandomRegionBlurOut(object):
def __init__(self, p=0.5, blackout_ratio=0.2):
self.p = p # Probability of applying the transform
self.blackout_ratio = blackout_ratio # Ratio of the image area to blackout
def __call__(self, img):
if random.random() < self.p:
channels, width, height = img.shape
mask_width = int(width * self.blackout_ratio)
mask_height = int(height * self.blackout_ratio)
start_x = random.randint(0, width - mask_width)
start_y = random.randint(0, height - mask_height)
img[:, start_x:start_x+mask_width, start_y:start_y+mask_height] = transforms.GaussianBlur((3,3), sigma=(0.1, 2.0))(img[:, start_x:start_x+mask_width, start_y:start_y+mask_height])
return img
class hktest(Dataset):
def __init__(self, split = "train"):
self.split = split
self.transforms ={
"train": transforms.Compose([
transforms.ToTensor(),
transforms.Resize((224, 224)),
RandomRegionBlackOut(p=0.4, blackout_ratio=0.2),
RandomRegionBlurOut(p=0.4, blackout_ratio=0.2),
transforms.ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4, hue=0.2),
transforms.GaussianBlur(kernel_size=(5, 5), sigma=(0.1, 0.2)),
transforms.Grayscale(num_output_channels=3),
]),
"test": transforms.Compose([
transforms.ToTensor(),
transforms.Resize((224,224)),
transforms.Grayscale(num_output_channels=3),
])
}
contactless_paths = list()
contactbased_paths = list()
contactless_ids = list()
contactbased_ids = list()
x=0
with open("hkpolyu_test.json",'r') as js:
sample_dict = json.load(js)
for file in sample_dict:
contactless_paths.extend(sample_dict[file]['Contactless'])
contactbased_paths.extend(sample_dict[file]['Contactbased'])
contactless_ids.append(file)
contactbased_ids.append(file)
self.train_files = {
"contactless": contactless_paths,
"contactbased": contactbased_paths
}
self.transform = self.transforms[split]
self.allfiles = self.train_files
self.all_files_paths_contactless = contactless_paths
self.label_id_mapping = contactless_ids
self.all_labels = list()
self.label_id_to_contactbased = dict()
for filename in self.allfiles["contactless"]:
id = filename.split("/")[-3]
self.all_labels.append(self.label_id_mapping.index(id))
for filename in self.allfiles["contactbased"]:
id = filename.split("/")[-3]
id = self.label_id_mapping.index(id)
if (id in self.label_id_to_contactbased):
self.label_id_to_contactbased[id].append(filename)
else:
self.label_id_to_contactbased[id] = [filename]
print("Number of Contactbased Files: ", len(self.allfiles["contactbased"]))
print("Number of Contactless Files: ", len(self.allfiles["contactless"]))
print("Number of classes: ", len(self.label_id_mapping))
print("Total number of images ", split ," : ", len(self.all_labels))
def __len__(self):
return len(self.all_files_paths_contactless)
def __getitem__(self, idx):
if torch.is_tensor(idx):
idx = idx.tolist()
label = self.all_labels[idx]
contactless_filename = self.all_files_paths_contactless[idx]
if len(self.label_id_to_contactbased[label]) == 1:
contactbased_filename = self.label_id_to_contactbased[label][0]
else:
contactbased_filename = self.label_id_to_contactbased[label][idx % len(self.label_id_to_contactbased[label])]
contactless_sample = Image.open(contactless_filename)
contactless_sample = contactless_sample.convert("RGB")
contactbased_sample = cv2.imread(contactbased_filename)
contactbased_sample = cropping_preprocess(contactbased_sample)
self.transform = transforms.Compose([
transforms.ToTensor(),
transforms.Resize((224, 224),interpolation=InterpolationMode.BICUBIC),
transforms.Grayscale(num_output_channels=3),
])
contactless_sample = self.transform(contactless_sample)
contactbased_sample = self.transform(contactbased_sample)
return contactless_sample, contactbased_sample, self.all_labels[idx]
|