Merge pull request #138 from litesh1123/smart_select

smart select feature created and added

Smart_select features/smart_select.py

@@ -0,0 +1,125 @@
+import os
+import cv2
+import pytesseract
+import numpy as np
+from tkinter import Tk, filedialog
+from PIL import Image, ImageEnhance, ImageFilter
+
+# Manually specify the path to the Tesseract executable
+pytesseract.pytesseract.tesseract_cmd = r'C:\Users\kulitesh\Tesseract-OCR\Tesseract-OCR\tesseract.exe'
+
+def preprocess_image(image_path):
+    # Open the image using PIL
+    image = Image.open(image_path)
+
+    # Convert to grayscale
+    image = image.convert('L')
+
+    # Increase contrast
+    enhancer = ImageEnhance.Contrast(image)
+    image = enhancer.enhance(2)
+
+    # Apply a median filter to remove noise
+    image = image.filter(ImageFilter.MedianFilter(size=3))
+
+    # Convert the image to a NumPy array for OpenCV processing
+    image = cv2.cvtColor(np.array(image), cv2.COLOR_GRAY2BGR)
+
+    return image
+
+def segment_image(image):
+    # Convert to grayscale
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+
+    # Apply adaptive thresholding
+    binary = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2)
+
+    # Find contours
+    contours, _ = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+    # Sort contours by area (largest first)
+    contours = sorted(contours, key=cv2.contourArea, reverse=True)
+
+    sections = []
+    for contour in contours:
+        x, y, w, h = cv2.boundingRect(contour)
+        if w > 50 and h > 50:  # Filter out small contours
+            sections.append((x, y, w, h))
+
+    return sections
+
+def extract_text_from_section(image, section):
+    x, y, w, h = section
+    roi = image[y:y+h, x:x+w]
+
+    # Use Tesseract to extract text with custom configuration
+    custom_config = r'--oem 3 --psm 6 -l eng'
+    text = pytesseract.image_to_string(roi, config=custom_config)
+
+    return text
+
+def save_extracted_text(image_path, extracted_text):
+    base_name = os.path.splitext(image_path)[0]
+    text_file_path = base_name + "_extracted_text.txt"
+
+    with open(text_file_path, 'w', encoding='utf-8') as file:
+        file.write(extracted_text)
+
+    print(f"Extracted text saved to {text_file_path}")
+
+def main():
+    # Hide the root window
+    Tk().withdraw()
+
+    # Get the directory of the current script
+    script_dir = os.path.dirname(os.path.abspath(__file__))
+    screenshots_smart_dir = os.path.join(script_dir, 'screenshots_smart')
+
+    if os.path.exists(screenshots_smart_dir):
+        # If screenshots_smart folder exists, ask the user to select an image from there
+        image_path = filedialog.askopenfilename(initialdir=screenshots_smart_dir, title="Select a Screenshot", filetypes=[("Image Files", ".png;.jpg;.jpeg;.bmp;*.tiff")])
+    else:
+        print(f"'screenshots_smart' folder not found in {script_dir}. Please select an image file.")
+        # Ask user to add a photo
+        image_path = filedialog.askopenfilename(title="Select a Screenshot", filetypes=[("Image Files", ".png;.jpg;.jpeg;.bmp;*.tiff")])
+
+        # If no file is selected, search for images on the Desktop
+        if not image_path:
+            desktop_dir = os.path.join(os.path.expanduser("~"), "Desktop")
+            image_files = [f for f in os.listdir(desktop_dir) if f.lower().endswith(('.png', '.jpg', '.jpeg', '.bmp', '.tiff'))]
+
+            if image_files:
+                image_path = os.path.join(desktop_dir, image_files[0])
+                print(f"Using image found on Desktop: {image_path}")
+            else:
+                print("No images found on Desktop.")
+                return
+
+    # Check if the user selected a file
+    if not image_path:
+        print("No file selected.")
+        return
+
+    # Preprocess the image
+    image = preprocess_image(image_path)
+
+    # Segment the image into sections
+    sections = segment_image(image)
+
+    # Extract text from each section and aggregate it
+    extracted_text = ""
+    for section in sections:
+        text = extract_text_from_section(image, section)
+        if text:
+            extracted_text += text + "\n" + "-" * 40 + "\n"
+
+    # Print and save the extracted text
+    if extracted_text:
+        print("Extracted Text:")
+        print(extracted_text)
+        save_extracted_text(image_path, extracted_text)
+    else:
+        print("No text extracted.")
+
+if __name__ == "__main__":
+    main()