working w koncu!!!

This commit is contained in:
shaaqu 2020-06-02 22:06:51 +02:00
parent db76915759
commit 457be5fba8
42 changed files with 297 additions and 70366 deletions

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@ -1,59 +0,0 @@
import numpy as np
import torch
import torchvision
import matplotlib.pyplot as plt
from time import time
from torchvision import datasets, transforms
from torch import nn, optim
import cv2
transform = transforms.Compose([transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,)),
])
# load nn model
input_size = 784 # = 28*28
hidden_sizes = [128, 128, 64]
output_size = 10
model = nn.Sequential(nn.Linear(input_size, hidden_sizes[0]),
nn.ReLU(),
nn.Linear(hidden_sizes[0], hidden_sizes[1]),
nn.ReLU(),
nn.Linear(hidden_sizes[1], hidden_sizes[2]),
nn.ReLU(),
nn.Linear(hidden_sizes[2], output_size),
nn.LogSoftmax(dim=-1))
model.load_state_dict(torch.load('digit_reco_model2.pt'))
model.eval()
# model = torch.load('digit_reco_model2.pt')
if model is None:
print("Model is not loaded.")
else:
print("Model is loaded.")
# img from dataset
val_set = datasets.MNIST('PATH_TO_STORE_TESTSET', download=True, train=False, transform=transform)
val_loader = torch.utils.data.DataLoader(val_set, batch_size=64, shuffle=True)
images, labels = next(iter(val_loader))
print(type(images))
img = images[0].view(1, 784)
plt.imshow(images[0].numpy().squeeze(), cmap='gray_r')
plt.show()
# recognizing
with torch.no_grad():
logps = model(img)
print(logps)
ps = torch.exp(logps)
probab = list(ps.numpy()[0])
print("Predicted Digit =", probab.index(max(probab)))

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@ -1,111 +0,0 @@
import numpy as np
import torch
import torchvision
import matplotlib.pyplot as plt
from time import time
from torchvision import datasets, transforms
from torch import nn, optim
import cv2
# IMG transform
transform = transforms.Compose([transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,)),
])
# dataset download
train_set = datasets.MNIST('PATH_TO_STORE_TRAINSET', download=True, train=True, transform=transform)
val_set = datasets.MNIST('PATH_TO_STORE_TESTSET', download=True, train=False, transform=transform)
train_loader = torch.utils.data.DataLoader(train_set, batch_size=64, shuffle=True)
val_loader = torch.utils.data.DataLoader(val_set, batch_size=64, shuffle=True)
print(train_set[0])
# building nn model
input_size = 784 # = 28*28
hidden_sizes = [128, 128, 64]
output_size = 10
model = nn.Sequential(nn.Linear(input_size, hidden_sizes[0]),
nn.ReLU(),
nn.Linear(hidden_sizes[0], hidden_sizes[1]),
nn.ReLU(),
nn.Linear(hidden_sizes[1], hidden_sizes[2]),
nn.ReLU(),
nn.Linear(hidden_sizes[2], output_size),
nn.LogSoftmax(dim=-1))
# print(model)
criterion = nn.NLLLoss()
images, labels = next(iter(train_loader))
images = images.view(images.shape[0], -1)
logps = model(images) # log probabilities
loss = criterion(logps, labels) # calculate the NLL loss
# training
optimizer = optim.SGD(model.parameters(), lr=0.003, momentum=0.9)
time0 = time()
epochs = 1
for e in range(epochs):
running_loss = 0
for images, labels in train_loader:
# Flatten MNIST images into a 784 long vector
images = images.view(images.shape[0], -1)
# Training pass
optimizer.zero_grad()
output = model(images)
loss = criterion(output, labels)
# This is where the model learns by backpropagating
loss.backward()
# And optimizes its weights here
optimizer.step()
running_loss += loss.item()
else:
print("Epoch {} - Training loss: {}".format(e + 1, running_loss / len(train_loader)))
print("\nTraining Time (in minutes) =", (time() - time0) / 60)
# testing
images, labels = next(iter(val_loader))
img = images[0].view(1, 784)
print(type(img))
print(img.size())
with torch.no_grad():
logps = model(img)
ps = torch.exp(logps)
probab = list(ps.numpy()[0])
print("Predicted Digit =", probab.index(max(probab)))
# view_classify(img.view(1, 28, 28), ps)
# accuracy
correct_count, all_count = 0, 0
for images, labels in val_loader:
for i in range(len(labels)):
img = images[i].view(1, 784)
with torch.no_grad():
logps = model(img)
ps = torch.exp(logps)
probab = list(ps.numpy()[0])
pred_label = probab.index(max(probab))
true_label = labels.numpy()[i]
if true_label == pred_label:
correct_count += 1
all_count += 1
print("Number Of Images Tested =", all_count)
print("\nModel Accuracy =", (correct_count / all_count))
# saving model
# torch.save(model.state_dict(), './digit_reco_model.pt')
# torch.save(model.state_dict(), './digit_reco_model2.pt')

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@ -1,85 +0,0 @@
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.metrics import accuracy_score
from sklearn.neural_network import MLPClassifier
import pandas as pd
import cv2
import keras
# 28x28
train_data = np.genfromtxt('dataset/train.csv', delimiter=',', skip_header=1, max_rows=20000, encoding='utf-8')
test_data = np.genfromtxt('dataset/test.csv', delimiter=',', skip_header=1, max_rows=20000, encoding='utf-8')
# train_data = pd.read_csv('dataset/train.csv')
# test_data = pd.read_csv('dataset/test.csv')
# training
# recznie napisane cyfry
digits = datasets.load_digits()
y = digits.target
x = digits.images.reshape((len(digits.images), -1))
# print(type(y[0]), type(x[0]))
# ogarnac zbior, zwiekszyc warstwy
# x_train = train_data.iloc[:, 1:].values.astype('float32')
# y_train = train_data.iloc[:, 0].values.astype('int32')
# x_test = test_data.values.astype('float32')
x_train = train_data[0:10000, 1:]
y_train = train_data[0:10000, 0]
x_test = train_data[10001:20000, 1:]
y_test = train_data[10001:20000, 0].astype('int')
print(type(y_test[0]), type(x_test[0]))
# x_train = x[:900]
# y_train = y[:900]
# x_test = x[900:]
# y_test = y[900:]
# 500, 500, 500, 500, 500
mlp = MLPClassifier(hidden_layer_sizes=(150, 100, 100, 100), activation='logistic', alpha=1e-4,
solver='sgd', tol=0.000000000001, random_state=1,
learning_rate_init=.1, verbose=True, max_iter=10000)
mlp.fit(x_train, y_train)
predictions = mlp.predict(x_test)
print("Accuracy: ", accuracy_score(y_test, predictions))
# image
img = cv2.cvtColor(cv2.imread('test5.jpg'), cv2.COLOR_BGR2GRAY)
img = cv2.blur(img, (9, 9)) # poprawia jakosc
img = cv2.resize(img, (28, 28), interpolation=cv2.INTER_AREA)
img = img.reshape((len(img), -1))
# print(type(img))
# print(img.shape)
# plt.imshow(img ,cmap='binary')
# plt.show()
data = []
rows, cols = img.shape
for i in range(rows):
for j in range(cols):
k = img[i, j]
if k > 225:
k = 0 # brak czarnego
else:
k = 255
data.append(k)
data = np.asarray(data, dtype=np.float64)
# print(data)
print(type(data))
predictions = mlp.predict([data])
print("Liczba to:", predictions[0].astype('int'))

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@ -0,0 +1,89 @@
import numpy as np
import torch
import torchvision
import matplotlib.pyplot as plt
from time import time
from torchvision import datasets, transforms
from torch import nn, optim
import torch.nn.functional as F
import cv2
from nn_model import Net
'''
Q:
what is batch?
'''
n_epochs = 3
batch_size_train = 64
batch_size_test = 1000
model = Net()
print("Model loaded.")
optimizer = optim.SGD(model.parameters(), lr=0.003, momentum=0.5)
criterion = nn.NLLLoss()
transform = transforms.Compose([transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,)),
])
train_set = datasets.MNIST('PATH_TO_STORE_TRAIN_SET', download=True, train=True, transform=transform)
test_set = datasets.MNIST('PATH_TO_STORE_TEST_SET', download=True, train=False, transform=transform)
train_loader = torch.utils.data.DataLoader(train_set, batch_size=batch_size_train, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_set, batch_size=batch_size_test, shuffle=True)
print("Data sets loaded.")
train_losses = []
train_counter = []
test_losses = []
test_counter = [i * len(train_loader.dataset) for i in range(n_epochs + 1)]
def train_model(epoch):
print("Training model.")
model.train()
for batch_idx, (data, target) in enumerate(train_loader):
optimizer.zero_grad()
output = model(data)
loss = criterion(output, target)
loss.backward()
optimizer.step()
if batch_idx % 10 == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, batch_idx * len(data), len(train_loader.dataset),
100. * batch_idx / len(train_loader), loss.item()))
train_losses.append(loss.item())
train_counter.append(
(batch_idx * 64) + ((epoch - 1) * len(train_loader.dataset)))
def test_model():
print("Testing model.")
model.eval()
test_loss = 0
correct = 0
with torch.no_grad():
for data, target in test_loader:
output = model(data)
test_loss += F.nll_loss(output, target, size_average=False).item()
pred = output.data.max(1, keepdim=True)[1]
correct += pred.eq(target.data.view_as(pred)).sum()
test_loss /= len(test_loader.dataset)
test_losses.append(test_loss)
print('\nTest set: Avg. loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
test_loss, correct, len(test_loader.dataset),
100. * correct / len(test_loader.dataset)))
def create_model():
test_model()
for epoch in range(1, n_epochs + 1):
train_model(epoch)
test_model()
torch.save(model.state_dict(), './model.pt')
create_model()

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@ -0,0 +1,22 @@
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
self.conv2_drop = nn.Dropout2d()
self.fc1 = nn.Linear(320, 50)
self.fc2 = nn.Linear(50, 10)
def forward(self, x):
x = F.relu(F.max_pool2d(self.conv1(x), 2))
x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
x = x.view(-1, 320)
x = F.relu(self.fc1(x))
x = F.dropout(x, training=self.training)
x = self.fc2(x)
return F.log_softmax(x)

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@ -4,16 +4,15 @@ import torch
from PIL.Image import Image from PIL.Image import Image
from torch import nn from torch import nn
from torchvision.transforms import transforms from torchvision.transforms import transforms
from torch.autograd import Variable
import numpy as np
def white_bg_square(img): from nn_model import Net
"return a white-background-color image having the img in exact center"
size = (max(img.size),)*2
layer = Image.new('RGB', size, (255, 255, 255))
layer.paste(img, tuple(map(lambda x:(x[0]-x[1])/2, zip(size, img.size))))
return layer
def recognizer(a_path):
code = [] code = []
path = "test5.jpg" path = a_path
transform = transforms.Compose([transforms.ToTensor(), transform = transforms.Compose([transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,)), transforms.Normalize((0.5,), (0.5,)),
@ -34,32 +33,31 @@ rects = [cv2.boundingRect(ctr) for ctr in ctrs]
input_size = 784 # = 28*28 input_size = 784 # = 28*28
hidden_sizes = [128, 128, 64] hidden_sizes = [128, 128, 64]
output_size = 10 output_size = 10
model = nn.Sequential(nn.Linear(input_size, hidden_sizes[0]), model = Net()
nn.ReLU(), model.load_state_dict(torch.load('model.pt'))
nn.Linear(hidden_sizes[0], hidden_sizes[1]),
nn.ReLU(),
nn.Linear(hidden_sizes[1], hidden_sizes[2]),
nn.ReLU(),
nn.Linear(hidden_sizes[2], output_size),
nn.LogSoftmax(dim=-1))
model.load_state_dict(torch.load('digit_reco_model2.pt'))
model.eval() model.eval()
for rect in rects: for rect in rects:
# Crop image # Crop image
crop_img = img[rect[1]:rect[1] + rect[3] + 10, rect[0]:rect[0] + rect[2] + 10, 0] crop_img = img[rect[1]:rect[1] + rect[3] + 10, rect[0]:rect[0] + rect[2] + 10, 0]
# Resize the image # Resize the image
roi = cv2.resize(crop_img, (28, 28), interpolation=cv2.INTER_LINEAR) roi = cv2.resize(crop_img, (28, 28), interpolation=cv2.INTER_CUBIC)
roi = cv2.dilate(roi, (3, 3)) # roi = cv2.dilate(roi, (3, 3))
plt.imshow(roi) # plt.imshow(roi)
plt.show() # plt.show()
im = transform(roi) im = transform(roi)
im = im.view(1, 784) im = im.view(1, 1, 28, 28)
with torch.no_grad(): with torch.no_grad():
logps = model(im.float()) logps = model(im)
ps = torch.exp(logps) ps = torch.exp(logps)
probab = list(ps.numpy()[0]) probab = list(ps.numpy()[0])
print("Predicted Digit =", probab.index(max(probab))) code.append(probab.index(max(probab)))
cv2.imshow("Code", img) print(code)
cv2.waitKey() # cv2.imshow("Code", img)
# cv2.waitKey()
return code
recognizer("55555.jpg")
# print(recognizer("55555.jpg"))

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