Update app.py

app.py

@@ -7,28 +7,28 @@ from PIL import Image
 def get_grayscale(image):
     return cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
 
-def thresholding(src):
-    return cv2.threshold(src, 127, 255, cv2.THRESH_TOZERO)[1]
+def thresholding(src, threshold_value):
+    return cv2.threshold(src, threshold_value, 255, cv2.THRESH_BINARY)[1]
 
-def ocr_with_easy(img):
+def ocr_with_easy(img_path):
     reader = easyocr.Reader(['en'])
-    bounds = reader.readtext(img, paragraph="False", detail=0)
+    bounds = reader.readtext(img_path, paragraph="False", detail=0)
     bounds = ''.join(bounds)
     return bounds
 
-def process_image(img, steps):
+def process_image(img, steps, threshold_value):
     for step in steps:
         if step == "Grayscale Conversion":
             img = get_grayscale(img)
         elif step == "Thresholding":
-            img = thresholding(img)
+            img = thresholding(img, threshold_value)
     cv2.imwrite('processed_image.png', img)
     return 'processed_image.png'
 
-def generate_ocr(img, steps):
+def generate_ocr(img, steps, threshold_value):
     text_output = ''
-    if img is not None and (img).any():
-        processed_image_path = process_image(img, steps)
+    if img is not None and img.any():
+        processed_image_path = process_image(img, steps, threshold_value)
         text_output = ocr_with_easy(processed_image_path)
     else:
         raise gr.Error("Please upload an image and select the processing steps!")
@@ -105,17 +105,18 @@ Optical Character Recognition (OCR) is a technology used to convert different ty
 2. **Thresholding:** This step converts the grayscale image into a binary image, where the text is in black, and the background is in white. This makes it easier for the OCR algorithm to distinguish text from the background.
 3. **OCR using EasyOCR:** We use the EasyOCR library to recognize and extract text from the preprocessed image.
 **Interactive Tutorial:**
-Please upload an image and select the correct order of steps to perform OCR.
+Please upload an image and select the correct order of steps to perform OCR. You can also adjust the threshold value using the slider.
 """
 
 image = gr.Image()
 steps = gr.CheckboxGroup(choices=tutorial_steps, label="Select and order the steps for OCR")
+threshold = gr.Slider(0, 255, value=127, step=1, label="Threshold Value")
 output = gr.Textbox(label="OCR Output")
 explanation = gr.Markdown(explanation_text)
 
 ocr_app = gr.Interface(
     fn=generate_ocr,
-    inputs=[image, steps],
+    inputs=[image, steps, threshold],
     outputs=output,
     title="Optical Character Recognition",
     description=explanation_text,
@@ -129,3 +130,4 @@ quiz_app = gr.TabbedInterface(
 )
 
 quiz_app.launch()
+