recognizing but training must be improved
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shaaqu
parent
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commit
4b8c560be9
@ -19,11 +19,14 @@
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@ -81,7 +84,7 @@
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@ -1,31 +1,63 @@
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import numpy as np
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from PIL import Image
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import matplotlib.pyplot as plt
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from sklearn import datasets
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from sklearn.metrics import accuracy_score
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from sklearn.neural_network import MLPClassifier
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import cv2
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def image():
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img = cv2.cvtColor(cv2.imread('test.jpg'), cv2.COLOR_BGR2GRAY)
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img = cv2.GaussianBlur(img, (15, 15), 0) # poprawia jakosc
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img = cv2.resize(img, (8, 8), interpolation=cv2.INTER_AREA)
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# training
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# recznie napisane cyfry
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digits = datasets.load_digits()
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print(type(img))
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print(img.shape)
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print(img)
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plt.imshow(img, cmap='binary')
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plt.show()
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y = digits.target
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x = digits.images.reshape((len(digits.images), -1))
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data = []
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x_train = x[:1000000]
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y_train = y[:1000000]
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x_test = x[1000:]
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y_test = y[1000:]
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rows, cols = img.shape
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for i in range(rows):
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for j in range(cols):
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k = img[i, j]
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if k > 200:
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k = 0 # brak czarnego
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else:
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k = 1
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mlp = MLPClassifier(hidden_layer_sizes=(15,), activation='logistic', alpha=1e-4,
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solver='sgd', tol=1e-4, random_state=1,
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learning_rate_init=.1, verbose=True)
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data.append(k)
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mlp.fit(x_train, y_train)
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predictions = mlp.predict(x_test)
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print(accuracy_score(y_test, predictions))
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# image
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img = cv2.cvtColor(cv2.imread('test3.png'), cv2.COLOR_BGR2GRAY)
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img = cv2.GaussianBlur(img, (5, 5), 0) # poprawia jakosc
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img = cv2.resize(img, (8, 8), interpolation=cv2.INTER_AREA)
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print(type(img))
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print(img.shape)
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print(img)
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plt.imshow(img ,cmap='binary')
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plt.show()
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data = []
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rows, cols = img.shape
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for i in range(rows):
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for j in range(cols):
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k = img[i, j]
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if k > 100:
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k = 0 # brak czarnego
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else:
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k = 1
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data.append(k)
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data = np.asarray(data, dtype=np.float32)
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print(data)
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predictions = mlp.predict([data])
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print("Liczba to:", predictions[0])
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print(data)
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13
coder/rocognizer.py
Normal file
13
coder/rocognizer.py
Normal file
@ -0,0 +1,13 @@
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import matplotlib.pyplot as plt
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import numpy as np
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from numpy import asarray
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from sklearn import datasets
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from sklearn.neural_network import MLPClassifier
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from sklearn.metrics import accuracy_score
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from PIL import Image
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import pygame
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import functions
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import sys
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import time
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BIN
coder/test1.jpg
Normal file
BIN
coder/test1.jpg
Normal file
Binary file not shown.
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After Width: | Height: | Size: 17 KiB |
BIN
coder/test3.png
Normal file
BIN
coder/test3.png
Normal file
Binary file not shown.
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After Width: | Height: | Size: 6.9 KiB |
BIN
coder/test9.png
Normal file
BIN
coder/test9.png
Normal file
Binary file not shown.
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After Width: | Height: | Size: 8.7 KiB |
@ -1,29 +0,0 @@
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import matplotlib.pyplot as plt
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import numpy as np
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from numpy import asarray
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from sklearn import datasets
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from sklearn.neural_network import MLPClassifier
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from sklearn.metrics import accuracy_score
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from PIL import Image
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def train():
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# recznie napisane cyfry
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digits = datasets.load_digits()
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y = digits.target
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x = digits.images.reshape((len(digits.images), -1))
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x_train = x[:1000000]
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y_train = y[:1000000]
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x_test = x[1000:]
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y_test = y[1000:]
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mlp = MLPClassifier(hidden_layer_sizes=(15,), activation='logistic', alpha=1e-4,
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solver='sgd', tol=1e-4, random_state=1,
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learning_rate_init=.1, verbose=True)
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mlp.fit(x_train, y_train)
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predictions = mlp.predict(x_test)
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print(accuracy_score(y_test, predictions))
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30
coder/train_nn.py
Normal file
30
coder/train_nn.py
Normal file
@ -0,0 +1,30 @@
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import matplotlib.pyplot as plt
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import numpy as np
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from numpy import asarray
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import pygame
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from sklearn import datasets
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from sklearn.neural_network import MLPClassifier
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from sklearn.metrics import accuracy_score
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from PIL import Image
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# recznie napisane cyfry
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digits = datasets.load_digits()
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y = digits.target
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x = digits.images.reshape((len(digits.images), -1))
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x_train = x[:1000000]
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y_train = y[:1000000]
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x_test = x[1000:]
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y_test = y[1000:]
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mlp = MLPClassifier(hidden_layer_sizes=(15,), activation='logistic', alpha=1e-4,
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solver='sgd', tol=1e-4, random_state=1,
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learning_rate_init=.1, verbose=True)
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mlp.fit(x_train, y_train)
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predictions = mlp.predict(x_test)
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print(accuracy_score(y_test, predictions))
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print(x_test[1])
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