Create app.py

app.py

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+
+
+
+import tensorflow as tf
+from matplotlib import pyplot as plt
+from skimage.transform import rescale, resize
+import pickle as pkl
+import numpy as np
+import os
+import cv2
+from PIL import Image,ImageFont, ImageDraw
+import CALTextModel
+
+
+
+#### training setup parameters ####
+
+
+lambda_val=1e-4
+gamma_val=1
+
+
+
+
+
+################################### Utility functions###################################
+
+
+
+def load_dict_picklefile(dictFile):
+    fp=open(dictFile,'rb')
+    lexicon=pkl.load(fp)
+    fp.close()
+    return lexicon,lexicon[' ']
+
+def preprocess_img(img):
+    if len(img.shape)>2:
+      img= cv2.cvtColor(img.astype('float32'), cv2.COLOR_BGR2GRAY)
+    height=img.shape[0]
+    width=img.shape[1]
+
+    if(width<300):
+      result = np.ones([img.shape[0], img.shape[1]*2])*255
+      result[0:img.shape[0],img.shape[1]:img.shape[1]*2]=img
+      img=result
+
+    img=cv2.resize(img, dsize=(800,100), interpolation = cv2.INTER_AREA)
+    img=(img-img.min())/(img.max()-img.min())
+    xx_pad = np.zeros((100, 800), dtype='float32')
+    xx_pad[:,:] =1
+
+    xx_pad = xx_pad[None, :, :]
+    img=img[None, :, :]
+    return img, xx_pad
+
+
+worddicts,_ = load_dict_picklefile('vocabulary.pkl')
+worddicts_r = [None] * len(worddicts)
+i=1
+for kk, vv in worddicts.items():
+    if(i<len(worddicts)):
+        worddicts_r[vv] = kk
+    else:
+        break
+    i=i+1
+
+# Create an instance of the model
+CALTEXT = CALTextModel.CALTEXT_Model(training=False)
+CALTEXT.load_weights('final_caltextModel/cp-0037.ckpt')
+test_loss = tf.keras.metrics.Mean(name='test_loss')
+
+
+@tf.function(experimental_relax_shapes=True)
+def execute_model(xx,xx_mask,CALTEXT):
+
+  anno = CALTEXT(xx,xx_mask, training=False)
+  hidden_state_0 = CALTEXT.get_hidden_state_0(anno)
+  return anno,hidden_state_0
+
+
+def test_error( images, x_mask):
+  # training=False is only needed if there are layers with different
+  # behavior during training versus inference (e.g. Dropout).
+  batch_loss=0
+  img_ind=1
+  for img_ind in range(len(images)):
+    xx = images[img_ind][tf.newaxis, ... ]
+    xx_mask = x_mask[img_ind][tf.newaxis, ... ]
+    anno,hidden_state_0=execute_model(xx,xx_mask,CALTEXT)
+
+    sample, score,hypalpha=CALTextModel.get_sample(anno, hidden_state_0,10, 130, False, False, CALTEXT)
+
+
+    score = score / np.array([len(s) for s in sample])
+    ss = sample[score.argmin()]
+    img_ind=img_ind+1
+
+    ind=0
+    num=int(len(ss)/2)
+
+
+
+
+    ####   output string
+    ind=0
+    outstr=u''
+    frames = []
+    #font = ImageFont.truetype("Jameel Noori Nastaleeq.ttf",60)
+    while (ind<len(ss)-1):
+      k=(len(ss)-2)-ind
+      outstr=outstr+worddicts_r[int(ss[k])]
+      '''textimg = Image.new('RGB', (1400,100),(255,255,255))
+      drawtext = ImageDraw.Draw(textimg)
+      drawtext.text((20, 20), outstr ,(0,0,0),font=font)
+      fig,axes=plt.subplots(2,1)
+      axes[0].imshow(textimg)
+      axes[0].axis('off')
+      axes[1].axis('off')
+      axes[1].imshow(xx[0,:,:],cmap='gray')
+      visualization=resize(hypalpha[k], (100,800),anti_aliasing=True)
+      axes[1].imshow(255-(255 * visualization), alpha=0.2)
+      plt.axis('off')
+
+      plt.savefig('/content/gdrive/My Drive/CALText_Demo/res.png')
+      frames.append(Image.fromarray(cv2.imread('/content/gdrive/My Drive/CALText_Demo/res.png'), 'RGB'))'''
+      ind=ind+1
+    '''frame_one = frames[0]
+    frame_one.save("/content/gdrive/My Drive/CALText_Demo/'vis.gif", format="GIF", append_images=frames,save_all=True, duration=300, loop=0)
+    gif_image="/content/gdrive/My Drive/CALText_Demo/'vis.gif"'''
+  return outstr,gif_image
+
+
+
+''''''examples = [
+    ['/content/gdrive/My Drive/CALText_Demo/sample_test_images/59-11.png'],
+		['/content/gdrive/My Drive/CALText_Demo/sample_test_images/59-21.png'],
+		['/content/gdrive/My Drive/CALText_Demo/sample_test_images/59-32.png'],
+		['/content/gdrive/My Drive/CALText_Demo/sample_test_images/59-37.png'],
+		['/content/gdrive/My Drive/CALText_Demo/sample_test_images/91-47.png'],
+		['/content/gdrive/My Drive/CALText_Demo/sample_test_images/91-49.png'],
+    ]''''''
+
+
+import gradio as gr
+
+def recognize_text(input_image):
+    x, x_mask=preprocess_img(input_image)
+    output_str,gif_image=test_error(x, x_mask)
+    return output_str,gif_image
+
+
+title = "CALText Demo"
+description = "<p style='text-align: center'>Gradio demo for an CALText model architecture <a href='https://github.com/nazar-khan/CALText'>[GitHub Code]</a> trained on the <a href='http://faculty.pucit.edu.pk/nazarkhan/work/urdu_ohtr/pucit_ohul_dataset.html'>PUCIT-OHUL</a> dataset. To use it, simply add your image, or click one of the examples to load them.  </p>"
+article = "<p style='text-align: center'></p>"
+css = "#0 {object-fit: contain;} #1 {object-fit: contain;}"
+inputs = gr.inputs.Image(label="Input Image")
+
+demo = gr.Interface(fn=recognize_text,inputs=inputs,outputs=[gr.Textbox(label="Output"),gr.Image(label="Demonstration of attention")],title=title,
+                    description=description,
+                    article=article,allow_flagging='never')
+
+demo.launch()
+
+
+
+
+
+
+
+
+