def analyze_digit_MLP(img):
""" Takes in an image matrix, crops out the digits and outputs it to file """
ocr.delete_files("../pics/cropped/")
print ("Preprocessing Image, Cropping Digits Into 28 X 28 Image Matrices\n")
cropped_img_to_show, cropped_thresh_to_Show, cropped_digits = ocr.save_digit_to_binary_img_as_mnist(img, dim = 28, saveToFile = True, imgSize = frame_new_dim)
print ("Image Preprocessing Done, %d Potential Digits Were Cropped Out\n" % len(cropped_digits))
print ("Predicting Results\n")
print ("Image Digit probability")
index = 0
for input_digit in cropped_digits:
path = "../pics/cropped/" + str(index) + ".png"
input_digit = imread(path)
digit, probability = mlp.predict(input_digit, mlp_classifier)
print ("%d.png %d %f" % (index, digit, probability))
index += 1
new_dim = (SCALE_FACTOR * img.shape[1]/2, SCALE_FACTOR * img.shape[0]/2)
cropped_img_to_show = cv2.resize(cropped_img_to_show, new_dim)
cropped_thresh_to_Show = cv2.resize(cropped_thresh_to_Show, new_dim)
cv2.imshow('handWriting Capture Cropped Image', cropped_img_to_show)
cv2.imshow('handWriting Capture Cropped Thresh', cropped_thresh_to_Show)
# input_img = imread('../pics/cropped/0.png')
# print mlp.predict(input_img, mlp_classifier)
print ("Predicting Results\n")
print ("Image Digit probability")
# Loading from matrix
# index = 0
# for input_digit in cropped_digits:
# digit, probability = mlp.predict(input_digit, mlp_classifier)
# print ("%d.png %d %f" % (index, digit, probability))
# index += 1
# Loading from Image
index = 0
for input_digit in cropped_digits:
path = "../pics/cropped/" + str(index) + ".png"
input_digit = imread(path)
digit, probability = mlp.predict(input_digit, mlp_classifier)
print ("%d.png %d %f" % (index, digit, probability))
index += 1
figure(1)
title("handWriting")
cv2.imwrite("../pics/cropped/cvPic3.png", cropped_img_for_show)
subplot(111)
imshow(cropped_img_for_show)
show()
import numpy as np
import theano
import theano.tensor as T
import lasagne
import matplotlib
import matplotlib.cm as cm
import matplotlib.pyplot as plt
from pylab import imread, imshow, imsave, figure, show, subplot, plot, scatter, title
import multilayerPerceptron as mlp
f_output = mlp.build_classifier('../trainedResult/model.npz')
input_img = imread('../pics/cropped/20.png')
print mlp.predict(input_img, f_output)
plt.subplot(211)
plt.imshow(input_img, cmap=cm.binary)
instance = input_img.reshape(-1, 1, 28, 28)
pred = f_output(instance)
N = pred.shape[1]
plt.subplot(212)
#result with probability
plt.bar(range(N), pred.ravel())
plt.show()
mlp_classifier = mlp.build_classifier('../trainedResult/model.npz')
# input_img = imread('../pics/cropped/0.png')
# print mlp.predict(input_img, mlp_classifier)
print("Predicting Results\n")
print("Image Digit probability")
# Loading from matrix
# index = 0
# for input_digit in cropped_digits:
# digit, probability = mlp.predict(input_digit, mlp_classifier)
# print ("%d.png %d %f" % (index, digit, probability))
# index += 1
# Loading from Image
index = 0
for input_digit in cropped_digits:
path = "../pics/cropped/" + str(index) + ".png"
input_digit = imread(path)
digit, probability = mlp.predict(input_digit, mlp_classifier)
print("%d.png %d %f" % (index, digit, probability))
index += 1
figure(1)
title("handWriting")
cv2.imwrite("../pics/cropped/cvPic3.png", cropped_img_to_show)
subplot(111)
imshow(cropped_img_to_show)
show()
