def test_hard_clf(fname, data, labels):
clf = loaddata.load_pkl(fname)
data = norm_data(data)
data = torch.tensor(data).float()
pred_y = clf.predict(data)
# Difference between values to be considered close enough to not be sure of class
eps = 0.00005
pred_labels = []
for ele in pred_y:
tensor_max = ele.max(0)
max_val = tensor_max[0]
temp_label = tensor_max[1]
for i in range(len(ele)):
if temp_label != i and abs(max_val - ele[i]) <= eps:
temp_label = torch.tensor(-2)
pred_labels.append(temp_label.item() + 1)
# If labels provided, get the accuracy of the model
pred_acc = -1.0
if labels is not None:
pred_acc = sum(pred_labels == labels) / len(labels)
return pred_labels, pred_acc
def prepare_data(self, data=None, labels=None):
if (data == None):
data = loaddata.load_pkl("train_data.pkl")
labels = np.load("finalLabelsTrain.npy")
data = np.array(
[np.array(data[i], dtype=bool) for i in range(len(data))])
data_labelled = [[data[i], labels[i]] for i in range(len(data))]
data_labelled_standardized = self.standardize_data(data_labelled)
# np.random.shuffle( std_labelled_training_data )
self.training_data_loader = torch.utils.data.DataLoader(
data_labelled_standardized[:int(
len(data_labelled_standardized) * 0.80)],
batch_size=self.batch_size,
shuffle=True,
num_workers=0)
self.test_data_loader = torch.utils.data.DataLoader(
data_labelled_standardized[
int(len(data_labelled_standardized) * 0.80):],
batch_size=self.batch_size,
shuffle=True,
num_workers=0)
return None
def test_easy_clf(fname, data, labels):
clf = loaddata.load_pkl(fname)
data = norm_data(data)
pred_y = clf.predict(data)
pred_acc = -1.0
if labels is not None:
pred_acc = sum(pred_y == labels) / len(labels)
return pred_y, pred_acc
def predict():
""" Load Test data. """
input_data = loaddata.load_pkl(sys.argv[2])
np.save('input_pickle_data.npy', input_data)
test_data = np.load('input_pickle_data.npy', allow_pickle=True)
"""Network type"""
if sys.argv[1] == 'AB':
networkType = 'AB'
else:
networkType = 'All'
def data_process(train_data):
resized_data = []
for item in train_data:
resized_data.append(skt.resize(np.float32(item), (100, 100)))
for i in range(len(resized_data)):
resized_data[i] = resized_data[i].flatten()
resized_data = np.asarray(resized_data)
return resized_data
""" Obtain all images of 'a' and 'b'. Create a dataset with those instances."""
def data_split(resized_data, train_labels):
set_X, set_Y = [], []
for i in (np.unique(train_labels)):
items = list(np.where(train_labels == i)[0])
set_X.append(resized_data[items])
set_Y.append(train_labels[items])
if networkType == "AB":
x = np.concatenate((np.asarray(set_X[0]), np.asarray(set_X[1])))
y = np.concatenate((np.asarray(set_Y[0]), np.asarray(set_Y[1])))
else:
x = np.concatenate((np.asarray(set_X[0]), np.asarray(set_X[1]),
np.asarray(set_X[2]), np.asarray(set_X[3]),
np.asarray(set_X[4]), np.asarray(set_X[5]),
np.asarray(set_X[6]), np.asarray(set_X[7])))
y = np.concatenate((np.asarray(set_Y[0]), np.asarray(set_Y[1]),
np.asarray(set_Y[2]), np.asarray(set_Y[3]),
np.asarray(set_Y[4]), np.asarray(set_Y[5]),
np.asarray(set_Y[6]), np.asarray(set_Y[7])))
return x, y
"""Resize the test data"""
test_resize_data = data_process(test_data)
""" Load trained network"""
networkFile = 'networkAB.pickle' if networkType == "AB" else "networkAll.pickle"
with open(networkFile, 'rb') as handle:
network = pickle.load(handle)
load_lr_model = pickle.load(open(networkFile, 'rb'))
"""Forward the test data into network"""
y_predicted = load_lr_model.predict(test_resize_data)
""" Saving predicted values in an output file"""
np.save(sys.argv[3], y_predicted)
return y_predicted
def load_data_All_Classes(data, labels):
data1 = load_pkl(data)
data1 = np.asarray(data1)
for i in range(len(data1)):
data1[i] = np.asarray(data1[i])
labels = np.load('finalLabelsTrain.npy')
for i in range(len(data1)):
data1[i] = skt.resize(data1[i], ((50, 50)), anti_aliasing=True)
return data1, labels
def test(data=None):
weights_file_path = "./pre_trained_cnn_weights.weights"
network = CNN(lr=0.001, batch_size=8, epochs=15, number_of_classes=9)
weights = torch.load(weights_file_path)
network.load_state_dict(weights)
if (data == None):
data = loaddata.load_pkl("train_data.pkl")
data = [[np.array(data[i], dtype=bool)] for i in range(len(data))]
data = network.standardize_data(data)
data = torch.stack([data[i][0] for i in range(len(data))])
return network.predict(data)
def load_data_AB(data, labels):
data_X = []
label_X = []
data1 = load_pkl(data)
data1 = np.asarray(data1)
for i in range(len(data1)):
data1[i] = np.asarray(data1[i])
labels = np.load('finalLabelsTrain.npy')
for i in range(len(data1)):
if (labels[i] == 1 or labels[i] == 2):
data_X.append(data1[i])
label_X.append(labels[i])
data_X = np.array(data_X)
label_X = np.array(label_X)
for i in range(len(data_X)):
data_X[i] = skt.resize(data_X[i], ((50, 50)), anti_aliasing=True)
return data_X, label_X
def load_data():
data = load_pkl('train_data(1).pkl')
data1 = np.asarray(data)
for i in range(len(data1)):
data1[i] = np.asarray(data1[i])
labels = np.load('finalLabelsTrain.npy')
for i in range(len(labels)):
labels[i] = labels[i] - 1
for i in range(len(data)):
data[i] = skt.resize(data[i], ((50, 50)), anti_aliasing=True)
data[i] = np.expand_dims(data[i], axis=0)
trans = transforms.ToPILImage()
trans1 = transforms.Grayscale(num_output_channels=1)
data_new.append(data[i])
data1 = np.array(data_new)
return data1, labels
import numpy as np
from pathlib import Path
import matplotlib.pyplot as plt
import pickle
from skimage import transform as skt
from PIL import *
from loaddata import load_pkl
from torch.utils.data import DataLoader, Dataset
from PIL import Image
from sklearn.model_selection import train_test_split
#with open('/home/aritrab97/GNV_OCR/data2/train_data.pkl', 'rb') as f:
# data = pickle.load(f)
data= load_pkl('train_data(1).pkl')
data1=np.asarray(data)
for i in range(len(data1)):
data1[i]=np.asarray(data1[i])
#pil_img = Image.fromarray(data[1000])
#pil_img.save('lena_square_save.png')
#print(data1[1000].shape)
#plt.imshow(data1[1000])
#plt.show()
train_path = "/home/aritrab97/GNV_OCR/data1/train/"
test_path = "/home/aritrab97/GNV_OCR/data1/test/"
MODEL_STORE_PATH="/home/aritrab97/GNV_OCR/data2/"
#transformations = transforms.Compose([transforms.ToPILImage(),transforms.Grayscale(num_output_channels=1),transforms.ToTensor()])
transformations = transforms.Compose([transforms.ToPILImage(),transforms.Grayscale(num_output_channels=1)])
import matplotlib.pyplot as plt
from scipy import ndimage
import skimage
from PIL import Image
from matplotlib import pyplot as plt
import sys
from scipy import ndimage
from sklearn.model_selection import train_test_split
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
# Loading in the train data
train_data = loaddata.load_pkl('train_data.pkl')
# Loading in the labels
train_labels = np.load('finalLabelsTrain.npy')
# When attempting to only classify a and b, looking only at reduced set.
ab_train_data = train_data[np.logical_or((train_labels == 1),
(train_labels == 2))]
ab_train_labels = train_labels[np.logical_or((train_labels == 1),
(train_labels == 2))]
# It appears that in the original data, the letter switches after every count of 9
# List of a,b data points that need to be rotated - this is not used
rot_list = []
def view_data(file_path, view=False):
'''
Opens and (optionally) views the data in the .npy, .pkl data objects
'''
label_dict = {
1: 'a',
2: 'b',
3: 'c',
4: 'd',
5: 'h',
6: 'i',
7: 'j',
8: 'k'
}
# Acquire all possible data.npy and label.npy files from file paths given
data_files = []
for dir in file_path:
dir_listing = os.listdir(dir)
file_pair = []
# Find the data.npys files
for f in dir_listing:
match = re.match(r'(.*)data(.*)', f)
if match != None:
print("Got {} from directory {}".format(f, dir))
if f.endswith('.pkl') or f.endswith('.npy'):
file_pair.append(dir + '/' + f)
# Find the labels.npy files
for f in dir_listing:
match = re.match(r'(.*)[lL]abel(.*).npy', f)
if match != None:
print("Got {} from directory {}".format(f, dir))
file_pair.append(dir + '/' + f)
data_files.append(file_pair)
if view:
print(data_files)
data_files = np.array(data_files)
# No files found
if np.min(data_files.shape) == 0:
print(
"No '.npy' files found in the given directories; quitting data visualization"
)
return
data = []
labels = []
for pair in data_files:
d = pair[0]
if d.endswith('.pkl'):
data.extend(loaddata.load_pkl(d))
else:
data.extend(np.load(d, allow_pickle=True))
if len(pair) > 1:
lbl = pair[1]
labels.extend(np.load(lbl, allow_pickle=True))
# If we want to see the binarized data and their respective labels...
if view:
data_pair = zip(data, labels)
# Visualize all the data
for d, lbl in data_pair:
for i in range(len(d)):
print("Label:", label_dict[lbl[i]])
fig = plt.figure()
plt.imshow(d[i], cmap="Greys")
plt.pause(0.75)
plt.close(fig)
return data, labels
def test(data=None):
weights_file_path = "./pre_trained_cnn_weights.weights"
network = CNN(lr=0.001, batch_size=8, epochs=15, number_of_classes=9)
weights = torch.load(weights_file_path)
network.load_state_dict(weights)
if (data == None):
data = loaddata.load_pkl("train_data.pkl")
data = [[np.array(data[i], dtype=bool)] for i in range(len(data))]
data = network.standardize_data(data)
data = torch.stack([data[i][0] for i in range(len(data))])
return network.predict(data)
# driver for convinience,
# will read data and labels as arguments from sys.argv
# where data_filename = sys.argv[1]
import sys
# Given that this is an unrequired, for-convienience driver, no guarantees are extended for invalid arguments
if __name__ == "__main__":
data = None
if (len(sys.argv) > 1):
data = loaddata.load_pkl(sys.argv[1])
test(data)
temp_label = torch.tensor(-2)
pred_labels.append(temp_label.item() + 1)
# If labels provided, get the accuracy of the model
pred_acc = -1.0
if labels is not None:
pred_acc = sum(pred_labels == labels) / len(labels)
return pred_labels, pred_acc
if __name__ == '__main__':
args = make_parser()
# Load the test data set
test_data = loaddata.load_pkl(args.data_file[0])
# Extract Features
print("Grabbing test data set features...")
test_data = feat_extraction.pad_data(test_data)
test_feat = np.array(feat_extraction.feature_ext(test_data, debug=False))
test_sums = np.array(feat_extraction.extract_sums(test_data))
test_feat = np.hstack((test_feat, test_sums))
print("Done with feature extraction.")
# Save extracted features into .npy files for future loading
print("Size of test data set:", test_feat.shape)
test_labels = None
# if args.labels:
# test_labels = np.load('test_data/test_feat/test_labels.npy')
