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new tests

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shaaqu 2020-06-01 00:21:32 +02:00
parent 29486c27df
commit db76915759
14 changed files with 57 additions and 62 deletions
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48
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@ -20,10 +20,20 @@
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3
coder/digits_recognizer.py
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@ -75,6 +75,9 @@ print("\nTraining Time (in minutes) =", (time() - time0) / 60)
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)

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38
coder/rocognizer.py
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@ -1,17 +1,19 @@
import numpy as np
import argparse
import imutils
import cv2
import matplotlib.pyplot as plt
import torch
from matplotlib import cm
from PIL.Image import Image
from torch import nn
from PIL import Image
from skimage.feature import hog
from torchvision.transforms import transforms
def white_bg_square(img):
"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
code = []
path = "test1.jpg"
path = "test5.jpg"
transform = transforms.Compose([transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,)),
@ -45,27 +47,19 @@ model.eval()
for rect in rects:
# Crop image
crop_img = img[rect[1]:rect[1] + rect[3], rect[0]:rect[0] + rect[2]]
plt.imshow(crop_img)
plt.show()
crop_img = img[rect[1]:rect[1] + rect[3] + 10, rect[0]:rect[0] + rect[2] + 10, 0]
# Resize the image
roi = cv2.resize(crop_img, (28, 28), interpolation=cv2.INTER_AREA)
roi = cv2.resize(crop_img, (28, 28), interpolation=cv2.INTER_LINEAR)
roi = cv2.dilate(roi, (3, 3))
plt.imshow(roi)
plt.show()
im = Image.fromarray(roi)
im = transform(im)
print(im)
plt.imshow(im)
plt.show()
im = transform(roi)
im = im.view(1, 784)
with torch.no_grad():
logps = model(im)
logps = model(im.float())
ps = torch.exp(logps)
print(ps[0])
probab = list(ps.numpy()[0])
print("Predicted Digit =", probab.index(max(probab)))
cv2.imshow("Resulting Image with Rectangular ROIs", img)
cv2.imshow("Code", img)
cv2.waitKey()

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coder/test1.jpg
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coder/test3.png
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coder/test5.png
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coder/test9.png
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coder/testno.png
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coder/train_nn.py
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@ -1,30 +0,0 @@
import matplotlib.pyplot as plt
import numpy as np
from numpy import asarray
import pygame
from sklearn import datasets
from sklearn.neural_network import MLPClassifier
from sklearn.metrics import accuracy_score
from PIL import Image
# recznie napisane cyfry
digits = datasets.load_digits()
y = digits.target
x = digits.images.reshape((len(digits.images), -1))
x_train = x[:1000000]
y_train = y[:1000000]
x_test = x[1000:]
y_test = y[1000:]
mlp = MLPClassifier(hidden_layer_sizes=(15,), activation='logistic', alpha=1e-4,
solver='sgd', tol=1e-4, random_state=1,
learning_rate_init=.1, verbose=True)
mlp.fit(x_train, y_train)
predictions = mlp.predict(x_test)
print(accuracy_score(y_test, predictions))
print(x_test[1])