def detect_word_model(model_predict, test_img):
img = prepareImg(cv2.imread(test_img), 64)
res = wordSegmentation(img, kernelSize=25, sigma=11, theta=7, minArea=100)
if not os.path.exists('tmp'):
os.mkdir('tmp')
for (j, w) in enumerate(res):
(wordBox, wordImg) = w
cv2.imwrite('tmp/%d.png' % j, wordImg)
imgFiles = os.listdir('tmp')
imgFiles = sorted(imgFiles)
pred_line = []
for f in imgFiles:
pred_line.append(predict_image(model_predict, 'tmp/' + f, True))
shutil.rmtree('tmp')
pred_line = correction_list(pred_line)
return (' '.join(pred_line))
def predict(w_model_predict, l_model_predict, test_img):
res = []
text = []
img = prepareImg(cv2.imread(test_img), 64)
res = wordSegmentation(img, kernelSize=25, sigma=11, theta=7, minArea=100)
if not os.path.exists('tmp'):
os.mkdir('tmp')
for (j, w) in enumerate(res):
(wordBox, wordImg) = w
cv2.imwrite('tmp/%d.png' % j, wordImg)
imgFiles = os.listdir('tmp')
imgFiles = sorted(imgFiles)
for f in imgFiles:
text.append(predict_image(w_model_predict, 'tmp/' + f, is_word=True))
shutil.rmtree('tmp')
text = correction_list(text)
text1 = ' '.join(text)
text2 = predict_image(l_model_predict, test_img, is_word=False)
return text1, text2
def rudra(gd):
# Opens a image in RGB mode
im = Image.open(gd)
# Setting the points for cropped image
width, height = im.size
left = width/4
top = height/4
right = width-width/4
bottom = height-height/4
# Cropped image of above dimension
# (It will not change orginal image)
im1 = im.crop((left, top, right, bottom))
# Shows the image in image viewer
#im1.show()
im1.save('temp.jpg')
gd='temp.jpg'
#l_model, l_model_predict = line_model()
#with open('line_model_predict.json', 'w') as f:
# f.write(l_model_predict.to_json())
with open('Resource/line_model_predict.json', 'r') as f:
l_model_predict = model_from_json(f.read())
with open('Resource/word_model_predict.json', 'r') as f:
w_model_predict = model_from_json(f.read())
#plot_model(l_model_predict, to_file='line_model.png', show_shapes=True, show_layer_names=True)
w_model_predict.load_weights('Resource/iam_words--15--1.791.h5')
l_model_predict.load_weights('Resource/iam_lines--12--17.373.h5')
test_img = gd#############################
img = prepareImg(cv2.imread(test_img), 64)
img2 = img.copy()
res = wordSegmentation(img, kernelSize=25, sigma=11, theta=7, minArea=100)
if not os.path.exists('tmp'):
os.mkdir('tmp')
for (j, w) in enumerate(res):
(wordBox, wordImg) = w
(x, y, w, h) = wordBox
cv2.imwrite('tmp/%d.png'%j, wordImg)
cv2.rectangle(img2,(x,y),(x+w,y+h),(0,255,0),1) # draw bounding box in summary image
cv2.imwrite('Resource/summary.png', img2)
plt.imshow(img2)
imgFiles = os.listdir('tmp')
imgFiles = sorted(imgFiles)
pred_line = []
for f in imgFiles:
pred_line.append(predict_image(w_model_predict, 'tmp/'+f, True))
print('-----------PREDICT-------------')
text1 =' '.join(pred_line)
print('[Word model]: '+' ' + text1)
pred_line = correction_list(pred_line)
text2=' '.join(pred_line)
print('[Word model with spell]: '+ text2)
text3= predict_image(l_model_predict, test_img, False)
print('[Line model]: ' + text3)
return text3
#rudra('cropped.jpg')
def how(gd, output):
#l_model, l_model_predict = line_model()
#with open('line_model_predict.json', 'w') as f:
# f.write(l_model_predict.to_json())
with open('Resource/line_model_predict.json', 'r') as f:
l_model_predict = model_from_json(f.read())
with open('Resource/word_model_predict.json', 'r') as f:
w_model_predict = model_from_json(f.read())
#plot_model(l_model_predict, to_file='line_model.png', show_shapes=True, show_layer_names=True)
w_model_predict.load_weights('Resource/iam_words--15--1.791.h5')
l_model_predict.load_weights('Resource/iam_lines--12--17.373.h5')
test_img = gd #############################
img = prepareImg(cv2.imread(test_img), 64)
img2 = img.copy()
res = wordSegmentation(img, kernelSize=25, sigma=11, theta=7, minArea=100)
if not os.path.exists('tmp'):
os.mkdir('tmp')
for (j, w) in enumerate(res):
(wordBox, wordImg) = w
(x, y, w, h) = wordBox
cv2.imwrite('tmp/%d.png' % j, wordImg)
cv2.rectangle(img2, (x, y), (x + w, y + h), (0, 255, 0),
1) # draw bounding box in summary image
cv2.imwrite('Resource/summary.png', img2)
plt.imshow(img2)
imgFiles = os.listdir('tmp')
imgFiles = sorted(imgFiles)
pred_line = []
for f in imgFiles:
pred_line.append(predict_image(w_model_predict, 'tmp/' + f, True))
print('-----------PREDICT-------------')
text1 = ' '.join(pred_line)
print('[Word model]: ' + ' ' + text1)
pred_line = correction_list(pred_line)
text2 = ' '.join(pred_line)
print('[Word model with spell]: ' + text2)
text3 = predict_image(l_model_predict, test_img, False)
print('[Line model]: ' + text3)
cnt1 = split15(text1)
cnt2 = split15(text2)
cnt3 = split15(text3)
print(cnt1, cnt2, cnt3)
f1 = open("output/output3.txt", "a")
if cnt3 >= cnt1 and cnt3 >= cnt2:
f1.write(text3)
elif cnt2 >= cnt1 and cnt2 >= cnt3:
f1.write(text2)
else:
f1.write(text1)
f1.write("\n")
f1.close()
plt.show()
shutil.rmtree('tmp')
"""
img = prepareImg(cv2.imread(test_img), 64)
img2 = img.copy()
res = wordSegmentation(img, kernelSize=25, sigma=11, theta=7, minArea=100)
if not os.path.exists('tmp'):
os.mkdir('tmp')
for (j, w) in enumerate(res):
(wordBox, wordImg) = w
(x, y, w, h) = wordBox
cv2.imwrite('tmp/%d.png' % j, wordImg)
cv2.rectangle(img2, (x, y), (x + w, y + h), (0, 255, 0),
1) # draw bounding box in summary image
cv2.imwrite('Resource/summary.png', img2)
plt.imshow(img2)
imgFiles = os.listdir('tmp')
imgFiles = sorted(imgFiles)
pred_line = []
for f in imgFiles:
pred_line.append(predict_image(w_model_predict, 'tmp/' + f, True))
print('-----------PREDICT-------------')
print('[Word model]: ' + ' '.join(pred_line))
pred_line = correction_list(pred_line)
print('[Word model with spell]: ' + ' '.join(pred_line))
print('[Line model]: ' + predict_image(l_model_predict, test_img, False))
plt.show()
shutil.rmtree('tmp')