File size: 6,256 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 |
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 import transforms, utils
import cv2
import random
import json
# Ignore warnings
import warnings
warnings.filterwarnings("ignore")
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 RB_loader_cb(Dataset):
def __init__(self, split = "train"):
self.base_path = "<path to ridgebase dataset parent folder>"
fingerdict = {
"Index": 0,
"Middle":1,
"Ring": 2,
"Little": 3
}
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)
])
}
self.train_path = {
"contactless": self.base_path + "Task1/Train/Contactless",
"contactbased": self.base_path + "Task1/Train/Contactbased"
}
self.test_path = {
"contactless": self.base_path + "Task1/Test/Contactless",
"contactbased": self.base_path + "Task1/Test/Contactbased"
}
self.train_files = {
"contactless": [self.train_path["contactless"] + "/" + f for f in os.listdir(self.train_path["contactless"]) if f.endswith('.png')],
"contactbased": [os.path.join(dp, f) for dp, dn, filenames in os.walk(self.train_path["contactbased"]) for f in filenames if os.path.splitext(f)[1] == '.bmp']
}
self.test_files = {
"contactless": [self.test_path["contactless"] + "/" + f for f in os.listdir(self.test_path["contactless"]) if f.endswith('.png')],
"contactbased": [os.path.join(dp, f) for dp, dn, filenames in os.walk(self.test_path["contactbased"]) for f in filenames if os.path.splitext(f)[1] == '.bmp']
}
self.transform = self.transforms[split]
self.allfiles = self.train_files if split == "train" else self.test_files
self.label_id_mapping = set()
self.label_id_to_contactbased = {}
self.all_files_paths_contactless = []
self.all_files_paths_contactbased = list()
self.all_labels = []
with open("label_map_rb.json",'r') as js:
self.label_mapping_dict = json.load(js)
for filename in self.allfiles["contactless"]:
id = filename.split("/")[-1].split("_")[2] + filename.split("/")[-1].split("_")[4].lower() + filename.split("/")[-1].split("_")[-1].split(".")[0]
self.label_id_mapping.add(id)
self.label_id_mapping = list(self.label_id_mapping)
for filename in self.allfiles["contactbased"]:
id = filename.split("/")[-1].split("_")[1] + filename.split("/")[-1].split("_")[2].lower() + str(fingerdict[filename.split("/")[-1].split("_")[3].split(".")[0]])
self.all_labels.append(self.label_mapping_dict[id])
self.all_files_paths_contactbased.append(filename)
print("Number of Contactbased Files: ", len(self.allfiles["contactbased"]))
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_contactbased)
def __getitem__(self, idx):
if torch.is_tensor(idx):
idx = idx.tolist()
contactbased_filename = self.all_files_paths_contactbased[idx]
contactbased_sample = cv2.imread(contactbased_filename)
if self.transform:
contactbased_sample = self.transform(contactbased_sample)
key_list = list(self.label_mapping_dict.keys())
val_list = list(self.label_mapping_dict.values())
return contactbased_sample, self.all_labels[idx] |