recognizing but training must be improved

This commit is contained in:
shaaqu 2020-05-20 08:24:33 +02:00
parent ddb652119b
commit 4b8c560be9
8 changed files with 129 additions and 64 deletions

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@ -19,11 +19,14 @@
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@ -1,31 +1,63 @@
import numpy as np import numpy as np
from PIL import Image from PIL import Image
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.metrics import accuracy_score
from sklearn.neural_network import MLPClassifier
import cv2 import cv2
def image(): # training
img = cv2.cvtColor(cv2.imread('test.jpg'), cv2.COLOR_BGR2GRAY) # recznie napisane cyfry
img = cv2.GaussianBlur(img, (15, 15), 0) # poprawia jakosc digits = datasets.load_digits()
img = cv2.resize(img, (8, 8), interpolation=cv2.INTER_AREA)
print(type(img)) y = digits.target
print(img.shape) x = digits.images.reshape((len(digits.images), -1))
print(img)
plt.imshow(img, cmap='binary')
plt.show()
data = [] x_train = x[:1000000]
y_train = y[:1000000]
x_test = x[1000:]
y_test = y[1000:]
rows, cols = img.shape mlp = MLPClassifier(hidden_layer_sizes=(15,), activation='logistic', alpha=1e-4,
for i in range(rows): 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))
# image
img = cv2.cvtColor(cv2.imread('test3.png'), cv2.COLOR_BGR2GRAY)
img = cv2.GaussianBlur(img, (5, 5), 0) # poprawia jakosc
img = cv2.resize(img, (8, 8), interpolation=cv2.INTER_AREA)
print(type(img))
print(img.shape)
print(img)
plt.imshow(img ,cmap='binary')
plt.show()
data = []
rows, cols = img.shape
for i in range(rows):
for j in range(cols): for j in range(cols):
k = img[i, j] k = img[i, j]
if k > 200: if k > 100:
k = 0 # brak czarnego k = 0 # brak czarnego
else: else:
k = 1 k = 1
data.append(k) data.append(k)
print(data) data = np.asarray(data, dtype=np.float32)
print(data)
predictions = mlp.predict([data])
print("Liczba to:", predictions[0])

13
coder/rocognizer.py Normal file
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@ -0,0 +1,13 @@
import matplotlib.pyplot as plt
import numpy as np
from numpy import asarray
from sklearn import datasets
from sklearn.neural_network import MLPClassifier
from sklearn.metrics import accuracy_score
from PIL import Image
import pygame
import functions
import sys
import time

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@ -1,29 +0,0 @@
import matplotlib.pyplot as plt
import numpy as np
from numpy import asarray
from sklearn import datasets
from sklearn.neural_network import MLPClassifier
from sklearn.metrics import accuracy_score
from PIL import Image
def train():
# 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))

30
coder/train_nn.py Normal file
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@ -0,0 +1,30 @@
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])