Enabled GPU Acceleration for Identification Node.

Installation Requirements.txt

@@ -0,0 +1,5 @@
+We need to run the following commands to install the prerequisites for the project.
+
+
+This command is used to install pytorch and torchvision for the purposes of GPU-accelerated vision tasks.
+pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121

Modules/data_collector.py

@@ -6,13 +6,22 @@ import re
 import sys
 import numpy as np
 import cv2
+
+# Vision-related Imports
 import pytesseract
+import easyocr
+import torch
+
 from PIL import Image, ImageGrab, ImageFilter
 
 from PyQt5.QtWidgets import QApplication, QWidget
 from PyQt5.QtCore import QRect, QPoint, Qt, QMutex, QTimer
 from PyQt5.QtGui import QPainter, QPen, QColor, QFont
 
+# Initialize EasyOCR with CUDA support
+reader_cpu = easyocr.Reader(['en'], gpu=False)
+reader_gpu = easyocr.Reader(['en'], gpu=True if torch.cuda.is_available() else False)
+
 pytesseract.pytesseract.tesseract_cmd = r"C:\Program Files\Tesseract-OCR\tesseract.exe"
 
 DEFAULT_WIDTH = 180
@@ -29,11 +38,11 @@ app_instance = None
 def _ensure_qapplication():
     """
     Ensures that QApplication is initialized before creating widgets.
+    Must be called from the main thread.
     """
     global app_instance
-    if QApplication.instance() is None:
+    if app_instance is None:
-        app_instance = QApplication(sys.argv)
+        app_instance = QApplication(sys.argv)  # Start in main thread
-        threading.Thread(target=app_instance.exec_, daemon=True).start()
 
 
 def create_ocr_region(region_id, x=250, y=50, w=DEFAULT_WIDTH, h=DEFAULT_HEIGHT, color=(255, 255, 0)):
@@ -102,11 +111,11 @@ def _preprocess_image(image):
     return thresh.filter(ImageFilter.MedianFilter(3))
 
 
-def find_word_positions(region_id, word, offset_x=0, offset_y=0, margin=5):
+def find_word_positions(region_id, word, offset_x=0, offset_y=0, margin=5, ocr_engine="CPU"):
     """
     Finds positions of a specific word within the OCR region.
     Applies user-defined offset and margin adjustments.
-    Returns a list of bounding box coordinates relative to the OCR box.
+    Uses Tesseract (CPU) or EasyOCR (GPU) depending on the selected engine.
     """
     collector_mutex.lock()
     if region_id not in regions:
@@ -136,28 +145,36 @@ def find_word_positions(region_id, word, offset_x=0, offset_y=0, margin=5):
         scale_x = orig_width / proc_width
         scale_y = orig_height / proc_height
 
+        word_positions = []
+
+        if ocr_engine == "CPU":
+            # Use Tesseract (CPU)
             data = pytesseract.image_to_data(processed, config='--psm 6 --oem 1', output_type=pytesseract.Output.DICT)
 
-        word_positions = []
             for i in range(len(data['text'])):
                 if re.search(rf"\b{word}\b", data['text'][i], re.IGNORECASE):
-                # Scale the detected coordinates back to region-relative positions
                     x_scaled = int(data['left'][i] * scale_x)
                     y_scaled = int(data['top'][i] * scale_y)
                     w_scaled = int(data['width'][i] * scale_x)
                     h_scaled = int(data['height'][i] * scale_y)
 
-                # Apply user-configured margin
+                    word_positions.append((x_scaled + offset_x, y_scaled + offset_y, w_scaled + (margin * 2), h_scaled + (margin * 2)))
-                x_margin = max(0, x_scaled - margin)
-                y_margin = max(0, y_scaled - margin)
-                w_margin = w_scaled + (margin * 2)
-                h_margin = h_scaled + (margin * 2)
 
-                # Apply user-configured offset
+        else:
-                x_final = x_margin + offset_x
+            # Use EasyOCR (GPU) - Convert PIL image to NumPy array
-                y_final = y_margin + offset_y
+            image_np = np.array(processed)
+            results = reader_gpu.readtext(image_np)
 
-                word_positions.append((x_final, y_final, w_margin, h_margin))
+            for (bbox, text, _) in results:
+                if re.search(rf"\b{word}\b", text, re.IGNORECASE):
+                    (x_min, y_min), (x_max, y_max) = bbox[0], bbox[2]
+
+                    x_scaled = int(x_min * scale_x)
+                    y_scaled = int(y_min * scale_y)
+                    w_scaled = int((x_max - x_min) * scale_x)
+                    h_scaled = int((y_max - y_min) * scale_y)
+
+                    word_positions.append((x_scaled + offset_x, y_scaled + offset_y, w_scaled + (margin * 2), h_scaled + (margin * 2)))
 
         return word_positions
     except Exception as e:
@@ -165,9 +182,6 @@ def find_word_positions(region_id, word, offset_x=0, offset_y=0, margin=5):
         return []
 
 
-
-
-
 def draw_identification_boxes(region_id, positions, color=(0, 0, 255)):
     """
     Draws non-interactive rectangles at specified positions within the given OCR region.

Nodes/General Purpose/identification_overlay.py

@@ -3,14 +3,13 @@
 Identification Overlay Node:
 - Creates an OCR region in data_collector with a blue overlay.
 - Detects instances of a specified word and draws adjustable overlays.
-- Users can configure offset, margin, and polling frequency dynamically.
+- Users can configure offset, margin, polling frequency, and select OCR engine.
 """
 
 import re
 from OdenGraphQt import BaseNode
-from PyQt5.QtWidgets import QMessageBox
 from PyQt5.QtCore import QTimer
-from Modules import data_manager, data_collector
+from Modules import data_collector
 
 
 class IdentificationOverlayNode(BaseNode):
@@ -24,7 +23,9 @@ class IdentificationOverlayNode(BaseNode):
         self.add_text_input("search_term", "Search Term", text="Aibatt")
         self.add_text_input("offset_value", "Offset Value (X,Y)", text="0,0")  # X,Y Offset
         self.add_text_input("margin", "Margin", text="5")  # Box Margin
-        self.add_text_input("polling_freq", "Polling Frequency (ms)", text="500")  # New input
+        self.add_text_input("polling_freq", "Polling Frequency (ms)", text="500")  # Polling Rate
+        self.add_combo_menu("ocr_engine", "Type", items=["CPU", "GPU"])
+        self.set_property("ocr_engine", "CPU")  # Set default value after adding the menu
 
         self.region_id = "identification_overlay"
         data_collector.create_ocr_region(self.region_id, x=250, y=50, w=300, h=200, color=(0, 0, 255))
@@ -45,7 +46,7 @@ class IdentificationOverlayNode(BaseNode):
         """
         polling_text = self.get_property("polling_freq")
         try:
-            polling_interval = max(100, int(polling_text))  # Minimum 100ms to avoid overloading
+            polling_interval = max(50, int(polling_text))  # Minimum 50ms for near real-time
         except ValueError:
             polling_interval = 500  # Default to 500ms
 
@@ -58,6 +59,7 @@ class IdentificationOverlayNode(BaseNode):
         search_term = self.get_property("search_term")
         offset_text = self.get_property("offset_value")
         margin_text = self.get_property("margin")
+        ocr_engine = self.get_property("ocr_engine")
 
         # Read and apply polling frequency updates
         self.update_polling_frequency()
@@ -77,8 +79,10 @@ class IdentificationOverlayNode(BaseNode):
         if not search_term:
             return
 
-        # Get detected word positions
+        # Get detected word positions using the selected OCR engine
-        detected_positions = data_collector.find_word_positions(self.region_id, search_term, offset_x, offset_y, margin)
+        detected_positions = data_collector.find_word_positions(
+            self.region_id, search_term, offset_x, offset_y, margin, ocr_engine
+        )
 
         # Draw detected word boxes
         data_collector.draw_identification_boxes(self.region_id, detected_positions, color=(0, 0, 255))