update threshodling methods

app.py

@@ -1,15 +1,24 @@
+'''
+Sometimes you just want to take an image from your database and see how it changes by running different image processing functions, to find the best starting point for your computer vision application. In this Hugging Face space, I have included various pattern recognition functions that can easily be applied to your input images, so you can see the output of each function. I will continue to update this space with additional modes, methods, and deep learning frameworks/models, to make them easy to use for demonstration purposes. Please let me know if you would like me to include any other specific functionality.
+
+'''
+
 import gradio as gr
 from threshold_methods import threshold_methods
 import cv2
 
 new_outputs = [
 gr.outputs.Image(type="numpy", label="Output Image"), 
-gr.outputs.Textbox(type="text", label="My HuggingFace URL") 
+gr.outputs.Textbox(type="text", label="My HuggingFace URL"), 
+gr.outputs.Textbox(type="text", label="My linkedin URL"), 
+gr.outputs.Textbox(type="text", label="info") 
 ]
 def show_image():
     img = cv2.imread('huggingface.png')   
-    text = 'https://huggingface.co/spaces/pirahansiah/ComputerVision' 
-    return img,text
+    text1 = 'https://huggingface.co/spaces/pirahansiah/ComputerVision' 
+    text2 = 'https://www.linkedin.com/in/pirahansiah/' 
+    text3 = ' Sometimes you just want to take an image from your database and see how it changes by running different image processing functions, to find the best starting point for your computer vision application. In this Hugging Face space, I have included various pattern recognition functions that can easily be applied to your input images, so you can see the output of each function. I will continue to update this space with additional modes, methods, and deep learning frameworks/models, to make them easy to use for demonstration purposes. Please let me know if you would like me to include any other specific functionality.    '
+    return img,text1,text2,text3
 
 HuggingFace = gr.Interface(
     fn=show_image,

threshold_methods.py

@@ -29,7 +29,7 @@ def pirahansiah_threshold_method_find_threshold_values_2(grayImg):
     th=int(th/mean_psnr)
     # Find the threshold values that satisfy the condition
     thresh = th #np.argwhere((mean_psnr / k1 < psnr_values) & (psnr_values < mean_psnr / k2)).flatten()
-    print(th)
+    #print(th)
     return thresh
 def pirahansiah_threshold_method_find_threshold_values_1(grayImg):      
     #https://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf                      
@@ -78,7 +78,7 @@ gr.outputs.Image(type="numpy", label="Output Threshold Image")
 def process_image(input_image, radio_choice,slider_val):
     img = cv2.imread(input_image,0)  
     binaryImg=img.copy()    
-    print(radio_choice) 
+    #print(radio_choice) 
     if radio_choice == "cv2.threshold(grayImg, 128, 255, cv2.THRESH_BINARY)":
             _, binaryImg=cv2.threshold(img, 128, 255, cv2.THRESH_BINARY)
     elif radio_choice == "cv2.threshold(grayImg, 128, 255, cv2.THRESH_BINARY_INV)":