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)
def analyze_digit_SVM(img):
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 = 8, 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")
"""
import sys
import cv2
import numpy as np
from pylab import imread, imshow, imsave, figure, show, subplot, plot, scatter, title
import ocr
import multilayerPerceptron as mlp
print(__doc__)
ocr.delete_files("../pics/cropped/")
print ("Preprocessing Image, Cropping Digits Into 28 X 28 Image Matrices\n")
# save_digit_to_binary_img_as_mnist(imgName, saveToFile = True, imgSize = 100, boundingRectMinSize = 5)
cropped_img_for_show, cropped_digits = ocr.save_digit_to_binary_img_as_mnist("../pics/12.png",saveToFile = True)
print ("Image Preprocessing Done, %d Potential Digits Were Cropped Out\n" % len(cropped_digits))
print ("Building Multilayer Perceptron Network From Trained Model\n")
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
"""
import sys
import cv2
import numpy as np
from pylab import imread, imshow, imsave, figure, show, subplot, plot, scatter, title
import ocr
import multilayerPerceptron as mlp
print(__doc__)
ocr.delete_files("../pics/cropped/")
print("Preprocessing Image, Cropping Digits Into 28 X 28 Image Matrices\n")
# save_digit_to_binary_img_as_mnist(imgName, saveToFile = True, imgSize = 100, boundingRectMinSize = 5)
cropped_img_to_show, cropped_thresh_to_Show, cropped_digits = ocr.save_digit_to_binary_img_as_mnist(
"../pics/print.png", dim=28, imgSize=100, saveToFile=True)
print("Image Preprocessing Done, %d Potential Digits Were Cropped Out\n" %
len(cropped_digits))
print("Building Multilayer Perceptron Network From Trained Model\n")
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
# figure(3)
# title("handWriting")
# cropped_img = ocr.save_digit_to_binary_img_as_mnist('../pics/handWriting.jpg', 10)
# cv2.imwrite('../pics/cropped/cvPic2.png', cropped_img)
# subplot(111)
# imshow(cropped_img)
# show()
# figure(4)
# title("handWriting")
# cropped_img = ocr.save_digit_to_binary_img_as_mnist('../pics/lotsOfDigits.png', 5)
# cv2.imwrite('../pics/cropped/cvPic3.png', cropped_img)
# subplot(111)
# imshow(cropped_img)
# show()
figure(1)
title("handWriting")
cropped_img = ocr.save_digit_to_binary_img_as_mnist('../pics/12.png', 5)
cv2.imwrite('../pics/cropped/cvPic3.png', cropped_img)
subplot(111)
imshow(cropped_img)
show()
# figure(3)
# title("handWriting")
# cropped_img = ocr.save_digit_to_binary_img_as_mnist('../pics/handWriting.jpg', 10)
# cv2.imwrite('../pics/cropped/cvPic2.png', cropped_img)
# subplot(111)
# imshow(cropped_img)
# show()
# figure(4)
# title("handWriting")
# cropped_img = ocr.save_digit_to_binary_img_as_mnist('../pics/lotsOfDigits.png', 5)
# cv2.imwrite('../pics/cropped/cvPic3.png', cropped_img)
# subplot(111)
# imshow(cropped_img)
# show()
figure(1)
title("handWriting")
cropped_img = ocr.save_digit_to_binary_img_as_mnist("../pics/12.png", 5)
cv2.imwrite("../pics/cropped/cvPic3.png", cropped_img)
subplot(111)
imshow(cropped_img)
show()
"""
import sys
import cv2
import numpy as np
from pylab import imread, imshow, imsave, figure, show, subplot, plot, scatter, title
import ocr
import multilayerPerceptron as mlp
print(__doc__)
ocr.delete_files("../pics/cropped/")
print ("Preprocessing Image, Cropping Digits Into 28 X 28 Image Matrices\n")
# save_digit_to_binary_img_as_mnist(imgName, saveToFile = True, imgSize = 100, boundingRectMinSize = 5)
cropped_img_to_show, cropped_thresh_to_Show, cropped_digits = ocr.save_digit_to_binary_img_as_mnist("../pics/print.png", dim = 28, imgSize = 100, saveToFile = True)
print ("Image Preprocessing Done, %d Potential Digits Were Cropped Out\n" % len(cropped_digits))
print ("Building Multilayer Perceptron Network From Trained Model\n")
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
