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realTimeWordRecognition.py
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realTimeWordRecognition.py
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import numpy as np
from matplotlib import pyplot as plt
import cv2
from numpy.lib.utils import source
import math
import tensorflow as tf
from keras_preprocessing.image import img_to_array
from keras.models import load_model
import numpy as np
from keras.models import model_from_json
# from keras.models import load_model
# Calculate skew angle of an image
def getSkewAngle(cvImage) -> float:
# Prep image, copy, convert to gray scale, blur, and threshold
newImage = cvImage.copy()
gray = cv2.cvtColor(newImage, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (9, 9), 0)
thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]
# Apply dilate to merge text into meaningful lines/paragraphs.
# Use larger kernel on X axis to merge characters into single line, cancelling out any spaces.
# But use smaller kernel on Y axis to separate between different blocks of text
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (30, 5))
dilate = cv2.dilate(thresh, kernel, iterations=5)
# Find all contours
contours, hierarchy = cv2.findContours(dilate, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
contours = sorted(contours, key = cv2.contourArea, reverse = True)
# Find largest contour and surround in min area box
largestContour = contours[0]
minAreaRect = cv2.minAreaRect(largestContour)
# Determine the angle. Convert it to the value that was originally used to obtain skewed image
angle = minAreaRect[-1]
if angle < -45:
angle = 90 + angle
return -1.0 * angle
# Rotate the image around its center
def rotateImage(cvImage, angle: float):
newImage = cvImage.copy()
(h, w) = newImage.shape[:2]
center = (w // 2, h // 2)
M = cv2.getRotationMatrix2D(center, angle, 1.0)
newImage = cv2.warpAffine(newImage, M, (w, h), flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE)
return newImage
# Deskew image
def deskew(cvImage):
angle = getSkewAngle(cvImage)
if(math.fabs(angle)>60):
angle+=math.degrees(math.pi)/2
return rotateImage(cvImage, -1.0 * angle)
# Function to generate horizontal projection profile
def getHorizontalProjectionProfile(image):
# Convert black spots to ones
image[image == 0] = 1
# Convert white spots to zeros
image[image == 255] = 0
horizontal_projection = np.sum(image, axis = 1)
return horizontal_projection
def getVerticalProjectionProfile(image):
# Convert black spots to ones
image[image == 0] = 1
# Convert white spots to zeros
image[image == 255] = 0
vertical_projection = np.sum(image, axis = 0)
return vertical_projection
# Driver Function
def shadow_remove(img):
rgb_planes = cv2.split(img)
result_norm_planes = []
for plane in rgb_planes:
dilated_img = cv2.dilate(plane, np.ones((7,7), np.uint8))
bg_img = cv2.medianBlur(dilated_img, 21)
diff_img = 255 - cv2.absdiff(plane, bg_img)
norm_img = cv2.normalize(diff_img,None, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_8UC1)
result_norm_planes.append(norm_img)
shadowremov = cv2.merge(result_norm_planes)
return shadowremov
def prediction2(image):
# characters = '०,१,२,३,४,५,६,७,८,९,क,ख,ग,घ,ङ,च,छ,ज,झ,ञ,ट,ठ,ड,ढ,ण,त,थ,द,ध,न,प,फ,ब,भ,म,य,र,ल,व,श,ष,स,ह,क्ष,त्र,ज्ञ'
# characters = characters.split(',')
labels = [u'\u091E',u'\u091F',u'\u0920',u'\u0921',u'\u0922',u'\u0923',u'\u0924',u'\u0925',u'\u0926',u'\u0927',u'\u0915',u'\u0928',u'\u092A',u'\u092B',u'\u092c',u'\u092d',u'\u092e',u'\u092f',u'\u0930',u'\u0932',u'\u0935',u'\u0916',u'\u0936',u'\u0937',u'\u0938',u'\u0939','क्ष','त्र','ज्ञ',u'\u0917',u'\u0918',u'\u0919',u'\u091a',u'\u091b',u'\u091c',u'\u091d',u'\u0966',u'\u0967',u'\u0968',u'\u0969',u'\u096a',u'\u096b',u'\u096c',u'\u096d',u'\u096e',u'\u096f']
# plt.imshow(image,cmap='gray')
# plt.show()
image = cv2.resize(image,(32,32))
image=cv2.GaussianBlur(image,(3,3),0)
ret,image=cv2.threshold(image,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
image[0:2,:]=255
plt.imshow(image,cmap='gray')
plt.show()
image = img_to_array(image)
image = np.expand_dims(image, axis=0)
image = np.expand_dims(image, axis=3)
model = tf.keras.models.load_model("charModel.h5")
lists = model.predict(image/255.0)[0]
# print(np.argmax(lists))
# print("The letter is ",labels[np.argmax(lists)])
return labels[np.argmax(lists)],lists[(np.argmax(lists))]*100,np.argmax(lists)
def prediction(img):
loaded_model=load_model('cnn2.hdf5')
characters = '०,१,२,३,४,५,६,७,८,९,क,ख,ग,घ,ङ,च,छ,ज,झ,ञ,ट,ठ,ड,ढ,ण,त,थ,द,ध,न,प,फ,ब,भ,म,य,र,ल,व,श,ष,स,ह,क्ष,त्र,ज्ञ'
characters = characters.split(',')
image= cv2.resize(img,(32,32))
image=cv2.GaussianBlur(image,(3,3),0)
ret,image=cv2.threshold(image,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
image[0:3,:]=255
image = img_to_array(image)
image = np.expand_dims(image, axis=0)
image = np.expand_dims(image, axis=3)
output = loaded_model.predict(image/255.0)
output = output.reshape(46)
predicted = np.argmax(output)
devanagari_label = characters[predicted]
success = output[predicted] * 100
# print(predicted)
return devanagari_label, success,predicted
def predict(image):
image = shadow_remove(image)
image=deskew(image)
image = shadow_remove(image)
image=cv2.resize(image,(600,200))
image = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
ret,image=cv2.threshold(image,100,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
kernel=np.ones((1,1),np.uint8)
image=cv2.dilate(image,kernel,iterations=1)
image1=image.copy()
horizontal_projection = getHorizontalProjectionProfile(image1)
a=0
b=len(horizontal_projection)
for i in range(a,b-1):
if(horizontal_projection[i]==0 and horizontal_projection[i+1]!=0):
a=i
break;
for i in range(0,b-1):
if(horizontal_projection[b-i-1]==0 and horizontal_projection[b-i-2]!=0):
b=b-i-1
break
image=image[a:b,:]
image1=image.copy()
slice=int((b-a)*0.25)
image1=image1[slice:,:]
image2=image1.copy()
vertical_projection= getVerticalProjectionProfile(image1)
c=0
d=len(vertical_projection)
for i in range(c,d-1):
if(vertical_projection[i]==0 and vertical_projection[i+1]!=0):
c=i
break;
for i in range(0,d-1):
if(vertical_projection[d-i-1]==0 and vertical_projection[d-i-2]!=0):
d=d-i-1
break
image=image[:,c:d]
cv2.imshow("img",image)
cv2.waitKey(0)
vertical_projection= getVerticalProjectionProfile(image2[:,c:d])
found0=[]
zero_start=0
zero_end=0
for i in range(0,len(vertical_projection)):
if(vertical_projection[i]==0 and zero_start==0):
zero_start=i
if(vertical_projection[i]!=0 and zero_start!=0):
zero_end=i
found0.append(int(zero_start+(zero_end-zero_start)/2))
zero_start=0
found0.append(len(vertical_projection))
found0.insert(0,0)
print(found0)
image3=image.copy()
image_segmented=[]
for i in range(len(found0)-1):
image_segmented.append(image[:,found0[i]:found0[i+1]])
cv2.line(image3,(found0[i],0),(found0[i],20),(0,0,0),1)
cv2.imshow("o",image3)
cv2.waitKey(0)
string=""
return_list=[]
print(image_segmented)
for i in image_segmented:
im=cv2.bitwise_not(i)
# kernel=np.ones((1,1),np.uint8)
# image=cv2.dilate(image,kernel,iterations=1)
im2=im.copy()
var1,var2,num=prediction2(im)
var3,var4,num2=prediction(im2)
print(var1,var3)
if num in[10,29]:
string+=' '+str(var1)
return_list.append(var1)
elif num2 in [15,44]:
string+=' '+str(var3)
return_list.append(var3)
elif var2>var4:
string+=' '+str(var1)
return_list.append(var1)
else :
string+=' '+str(var3)
return_list.append(var3)
cv2.destroyAllWindows()
return return_list
def test():
'''
We will be using a similar template to test your code
'''
image_paths = ['./images/1.jpeg']
for i,image_path in enumerate(image_paths):
image = cv2.imread(image_path) # This input format wont change
answer = predict(image) # a list is expected
print(' '.join(answer))# will be the output string
if __name__ == '__main__':
test()