update threshodling methods

app.py

@@ -3,11 +3,13 @@ from threshold_methods import threshold_methods
 import cv2
 
 new_outputs = [
-gr.outputs.Image(type="numpy", label="Output Image") 
+gr.outputs.Image(type="numpy", label="Output Image"), 
+gr.outputs.Textbox(type="text", label="My HuggingFace URL") 
 ]
 def show_image():
-    img = cv2.imread('huggingface.png')    
-    return img
+    img = cv2.imread('huggingface.png')   
+    text = 'https://huggingface.co/spaces/pirahansiah/ComputerVision' 
+    return img,text
 
 HuggingFace = gr.Interface(
     fn=show_image,
@@ -20,7 +22,7 @@ HuggingFace = gr.Interface(
     show_clear=False,
     show_generate=False,
     allow_flagging=False,
-    title="https://huggingface.co/spaces/pirahansiah/ComputerVision",
+    title="Computer Vision and Deep Learning by Farshid PirahanSiah",
 )
 
 gr.TabbedInterface(

gitignore

@@ -1,6 +1,5 @@
 flagged/
 *.pt
-*.png
 *.jpg
 *.mp4
 *.mkv

threshold_methods.py

@@ -8,7 +8,7 @@ def pirahansiah_threshold_method_find_threshold_values_2(grayImg):
     #https://pdfs.semanticscholar.org/05b2/d39fce4e8a99897e95f8c75416f65a5a0acc.pdf
     assert grayImg is not None, "file could not be read, check with os.path.exists()"
     #img = cv2.GaussianBlur(self.grayImg, (3, 3), 0)       
-    img = grayImg
+    image = grayImg
     # Initialize an array to store the PSNR values for each threshold value
     psnr_values = np.zeros(256)        
     psnr_max=0
@@ -16,9 +16,9 @@ def pirahansiah_threshold_method_find_threshold_values_2(grayImg):
     # Iterate over all possible threshold values with a step size of 5
     for t in range(0, 256, 5):
         # Threshold the image using the current threshold value
-        _, binary = cv2.threshold(img, t, 255, cv2.THRESH_BINARY)            
+        _, binary = cv2.threshold(image, t, 255, cv2.THRESH_BINARY)            
         # Calculate the PSNR between the binary image and the original image
-        psnr = cv2.PSNR(binary, img)            
+        psnr = cv2.PSNR(binary, image)            
         # Store the PSNR value
         psnr_values[t] = psnr
         if (psnr_max<psnr):
@@ -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)
     return thresh
 def pirahansiah_threshold_method_find_threshold_values_1(grayImg):      
     #https://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf                      
@@ -51,12 +51,11 @@ def pirahansiah_threshold_method_find_threshold_values_1(grayImg):
 
 
 
-path  = [['image_0.png']]
+path  = [['image_0.png'],['huggingface.png']]
 inputs_thresh = [
 gr.inputs.Image(type="filepath", label="Input Image"),
-gr.components.Slider(label="Manual Threshold Value", value=125, minimum=10, maximum=255, step=5),    
 gr.inputs.Radio(label="Threshold Methods", 
-                choices=[                    
+                choices=[                         
                         "cv2.threshold(grayImg, 128, 255, cv2.THRESH_BINARY)"
                         ,"cv2.threshold(grayImg, 128, 255, cv2.THRESH_BINARY_INV)"
                         ,"cv2.threshold(grayImg, 128, 255, cv2.THRESH_TRUNC)"
@@ -66,17 +65,20 @@ gr.inputs.Radio(label="Threshold Methods",
                         ,"cv2.threshold(grayImg, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU,)"
                         ,"Adapted from PirahanSiah Threshold Method I derivative demo"
                         ,"Inspired by PirahanSiah Threshold Method II derivative demo"
+                        ,"Manual Threshold Value select by Slider"                 
                     ]),  
+gr.components.Slider(label="Manual Threshold Value", value=125, minimum=10, maximum=255, step=5),    
 ]
 
 outputs_thresh = [
-gr.outputs.Image(type="numpy", label="Output Image") 
+gr.outputs.Image(type="numpy", label="Output Threshold Image") 
+
 ]
 
-def process_image(input_image, slider_val, radio_choice):
-    img = cv2.imread(input_image,0)
-    _, binaryImg = cv2.threshold(img, slider_val, 255, cv2.THRESH_BINARY)  
-    
+def process_image(input_image, radio_choice,slider_val):
+    img = cv2.imread(input_image,0)  
+    binaryImg=img.copy()    
+    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)":
@@ -96,10 +98,12 @@ def process_image(input_image, slider_val, radio_choice):
            _, binaryImg = cv2.threshold(img, threshval, 255, cv2.THRESH_BINARY)                         
     elif radio_choice == "Inspired by PirahanSiah Threshold Method II derivative demo":
            threshval=pirahansiah_threshold_method_find_threshold_values_2(img)        
-           _, binaryImg = cv2.threshold(img, threshval, 255, cv2.THRESH_BINARY)                         
+           _, binaryImg = cv2.threshold(img, threshval, 255, cv2.THRESH_BINARY)    
+    elif  radio_choice == "Manual Threshold Value select by Slider":
+          _, binaryImg = cv2.threshold(img, slider_val, 255, cv2.THRESH_BINARY)            
     return binaryImg
 
-def on_change(slider_val, radio_choice):
+def on_change(radio_choice,slider_val):
     # Update output
     outputs_thresh[0].update(process_image(
         inputs_thresh[0].value,