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")
Digit Segmentation and Classification Using
MLP and OpenCV on GoogleStreetView Data Base
Created by: Chenxing Ouyang & Jiali Xie
"""
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)
Digit Segmentation and Classification Using
MLP and OpenCV on GoogleStreetView Data Base
Created by: Chenxing Ouyang & Jiali Xie
"""
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)