def get_qualitative_results(denoise_func):
sclite.clear()
for i in tqdm(range(1, len(test_ds))):
image, text = test_ds[i]
resized_image = paragraph_segmentation_transform(image, image_size=form_size)
paragraph_bb = paragraph_segmentation_net(resized_image.as_in_context(ctx))
paragraph_bb = paragraph_bb[0].asnumpy()
paragraph_bb = expand_bounding_box(paragraph_bb, expand_bb_scale_x=0.01,
expand_bb_scale_y=0.01)
paragraph_segmented_image = crop_handwriting_page(image, paragraph_bb, image_size=segmented_paragraph_size)
word_bb = predict_bounding_boxes(word_segmentation_net, paragraph_segmented_image, min_c, overlap_thres, topk, ctx)
line_bbs = sort_bbs_line_by_line(word_bb, y_overlap=0.4)
line_images = crop_line_images(paragraph_segmented_image, line_bbs)
predicted_text = []
for line_image in line_images:
line_image = exposure.adjust_gamma(line_image, 1)
line_image = handwriting_recognition_transform(line_image, line_image_size)
character_probabilities = handwriting_line_recognition_net(line_image.as_in_context(ctx))
decoded_text = denoise_func(character_probabilities)
predicted_text.append(decoded_text)
actual_text = text[0].replace(""", '"').replace("'","'").replace("&", "&")
actual_text = actual_text.split("\n")
if len(predicted_text) > len(actual_text):
predicted_text = predicted_text[:len(actual_text)]
sclite.add_text(predicted_text, actual_text)
cer, _ = sclite.get_cer()
print("Mean CER = {}".format(cer))
return cer
def predict():
if request.method == 'POST':
# read image file string data
if 'file' not in request.files:
return redirect(url_for('home'))
else:
filestr = request.files['file'].read()
# convert string data to numpy array
npimg = np.fromstring(filestr, np.uint8)
# convert numpy array to image
img = cv2.imdecode(npimg, cv2.IMREAD_GRAYSCALE)
ctx = ctx = mx.gpu(0) if mx.context.num_gpus() > 0 else mx.cpu()
# Models
paragraph_segmentation_net = SegmentationNetwork(ctx=ctx)
paragraph_segmentation_net.cnn.load_parameters(paragraph_segmentation_model, ctx)
word_segmentation_net = WordSegmentationNet(2, ctx=ctx)
word_segmentation_net.load_parameters(word_segmentation_model, ctx)
handwriting_line_recognition_net = HandwritingRecognitionNet(rnn_hidden_states=rnn_hidden_states,
rnn_layers=rnn_layers,
max_seq_len=max_seq_len,
ctx=ctx)
handwriting_line_recognition_net.load_parameters(recognition_model, ctx)
MAX_IMAGE_SIZE_FORM = (1120, 800)
img_arr = np.asarray(img)
resized_image = paragraph_segmentation_transform(img_arr, image_size=MAX_IMAGE_SIZE_FORM)
paragraph_bb = paragraph_segmentation_net(resized_image.as_in_context(ctx))
paragraph_segmented_image = crop_handwriting_page(img_arr, paragraph_bb[0].asnumpy(),
image_size=segmented_paragraph_size)
word_bb = predict_bounding_boxes(word_segmentation_net, paragraph_segmented_image, min_c, overlap_thres, topk,
ctx)
line_bbs = sort_bbs_line_by_line(word_bb)
line_images = crop_line_images(paragraph_segmented_image, line_bbs)
predicted_text = []
for line_image in line_images:
line_image = handwriting_recognition_transform(line_image, line_image_size)
character_probabilities = handwriting_line_recognition_net(line_image.as_in_context(ctx))
decoded_text = denoise_func(character_probabilities)
predicted_text.append(decoded_text)
text = ' '.join(predicted_text)
print(text)
translated = gc_translate(text)
sentiment = gc_sentiment(text)
print(translated)
print(sentiment)
text_dict = {"text": text,
"translated": translated['translatedText'],
"polarity": sentiment[0],
"magnitude": sentiment[1]}
a = text_dict
return render_template('result.html', prediction=a)
line_images_array = []
fig, axs = plt.subplots(int(len(paragraph_segmented_images) / 2),
2,
figsize=(15,
9 * int(len(paragraph_segmented_images) / 2)))
for i, paragraph_segmented_image in enumerate(paragraph_segmented_images):
s_y, s_x = int(i / 2), int(i % 2)
axs[s_y, s_x].imshow(paragraph_segmented_image, cmap='Greys_r')
axs[s_y, s_x].axis('off')
axs[s_y, s_x].set_title("{}".format(i))
predicted_bbs = predicted_words_bbs_array[i]
line_bbs = sort_bbs_line_by_line(predicted_bbs, y_overlap=0.4)
line_images = crop_line_images(paragraph_segmented_image, line_bbs)
line_images_array.append(line_images)
for line_bb in line_bbs:
(x, y, w, h) = line_bb
image_h, image_w = paragraph_segmented_image.shape[-2:]
(x, y, w, h) = (x * image_w, y * image_h, w * image_w, h * image_h)
rect = patches.Rectangle((x, y), w, h, fill=False, color="r")
axs[s_y, s_x].add_patch(rect)
handwriting_line_recognition_net = HandwritingRecognitionNet(
rnn_hidden_states=512, rnn_layers=2, ctx=ctx, max_seq_len=160)
handwriting_line_recognition_net.load_parameters(
"models/handwriting_line8.params", ctx=ctx)
handwriting_line_recognition_net.hybridize()
def generate_op(img_n, img_dir, folder_path):
image_name = img_n.split('.')[0]
img_path = os.path.join(img_dir, img_n)
image = _pre_process_image(img_path, 'form')
form_size = (1120, 800)
predicted_bbs = []
resized_image = paragraph_segmentation_transform(image, form_size)
bb_predicted = paragraph_segmentation_net(resized_image.as_in_context(ctx))
bb_predicted = bb_predicted[0].asnumpy()
bb_predicted = expand_bounding_box(bb_predicted,
expand_bb_scale_x=0.03,
expand_bb_scale_y=0.03)
predicted_bbs.append(bb_predicted)
(x, y, w, h) = bb_predicted
image_h, image_w = image.shape[-2:]
(x, y, w, h) = (x * image_w, y * image_h, w * image_w, h * image_h)
segmented_paragraph_size = (700, 700)
paragraph_segmented_images = []
bb = predicted_bbs[0]
image = crop_handwriting_page(image,
bb,
image_size=segmented_paragraph_size)
paragraph_segmented_images.append(image)
min_c = 0.1
overlap_thres = 0.1
topk = 600
predicted_words_bbs_array = []
for i, paragraph_segmented_image in enumerate(paragraph_segmented_images):
predicted_bb = predict_bounding_boxes(word_segmentation_net,
paragraph_segmented_image, min_c,
overlap_thres, topk, ctx)
predicted_words_bbs_array.append(predicted_bb)
for j in range(predicted_bb.shape[0]):
(x, y, w, h) = predicted_bb[j]
image_h, image_w = paragraph_segmented_image.shape[-2:]
(x, y, w, h) = (x * image_w, y * image_h, w * image_w, h * image_h)
line_images_array = []
for i, paragraph_segmented_image in enumerate(paragraph_segmented_images):
predicted_bbs = predicted_words_bbs_array[i]
line_bbs = sort_bbs_line_by_line(predicted_bbs, y_overlap=0.4)
line_images = crop_line_images(paragraph_segmented_image, line_bbs)
line_images_array.append(line_images)
for line_bb in line_bbs:
(x, y, w, h) = line_bb
image_h, image_w = paragraph_segmented_image.shape[-2:]
(x, y, w, h) = (x * image_w, y * image_h, w * image_w, h * image_h)
line_image_size = (60, 800)
character_probs = []
for line_images in line_images_array:
form_character_prob = []
for i, line_image in enumerate(line_images):
line_image = handwriting_recognition_transform(
line_image, line_image_size)
line_character_prob = handwriting_line_recognition_net(
line_image.as_in_context(ctx))
form_character_prob.append(line_character_prob)
character_probs.append(form_character_prob)
FEATURE_LEN = 150
save_path = os.path.join(folder_path, image_name + '.txt')
file = open(save_path, 'w')
for i, form_character_probs in enumerate(character_probs):
for j, line_character_probs in enumerate(form_character_probs):
decoded_line_bs = get_beam_search(line_character_probs)
print(decoded_line_bs)
file.write(decoded_line_bs + ' ')
file.close()