From ce392d7a0404f368c0a1f65ac8187e0a15d7a8a3 Mon Sep 17 00:00:00 2001
From: Nicole Rappe <nicole.rappe@bunny-lab.io>
Date: Wed, 26 Feb 2025 02:08:00 -0700
Subject: [PATCH] Further optimized identification_overlay node GPU
acceleration / processing.
---
.../data_collector.cpython-312.pyc | Bin 14564 -> 14312 bytes
Modules/data_collector.py | 41 ++++++++----------
2 files changed, 19 insertions(+), 22 deletions(-)
diff --git a/Modules/__pycache__/data_collector.cpython-312.pyc b/Modules/__pycache__/data_collector.cpython-312.pyc
index 107d0944c578b79ecfebb370ae1ad1b1545a2489..2341494517f6f9553ec66384d6fbe41fc8ee5b77 100644
GIT binary patch
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diff --git a/Modules/data_collector.py b/Modules/data_collector.py
index 0f598df..82d9760 100644
--- a/Modules/data_collector.py
+++ b/Modules/data_collector.py
@@ -113,9 +113,9 @@ def _preprocess_image(image):
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.
- Uses Tesseract (CPU) or EasyOCR (GPU) depending on the selected engine.
+ Optimized function to detect word positions in an OCR region.
+ Uses raw screen data without preprocessing for max performance.
+ Uses Tesseract (CPU) or EasyOCR (GPU) depending on user selection.
"""
collector_mutex.lock()
if region_id not in regions:
@@ -134,45 +134,42 @@ def find_word_positions(region_id, word, offset_x=0, offset_y=0, margin=5, ocr_e
return []
try:
+ # Capture raw screen image (NO preprocessing)
image = ImageGrab.grab(bbox=(left, top, right, bottom))
- processed = _preprocess_image(image)
- # Get original and processed image sizes
+ # Get original image size
orig_width, orig_height = image.size
- proc_width, proc_height = processed.size
-
- # Scale factor between processed image and original screenshot
- 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)
+ # Use Tesseract directly on raw PIL image (no preprocessing)
+ data = pytesseract.image_to_data(image, config='--psm 6 --oem 1', output_type=pytesseract.Output.DICT)
for i in range(len(data['text'])):
if re.search(rf"\b{word}\b", data['text'][i], re.IGNORECASE):
- 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)
+ x_scaled = int(data['left'][i])
+ y_scaled = int(data['top'][i])
+ w_scaled = int(data['width'][i])
+ h_scaled = int(data['height'][i])
word_positions.append((x_scaled + offset_x, y_scaled + offset_y, w_scaled + (margin * 2), h_scaled + (margin * 2)))
else:
- # Use EasyOCR (GPU) - Convert PIL image to NumPy array
- image_np = np.array(processed)
+ # Convert PIL image to NumPy array for EasyOCR
+ image_np = np.array(image)
+
+ # Run GPU OCR
results = reader_gpu.readtext(image_np)
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)
+ x_scaled = int(x_min)
+ y_scaled = int(y_min)
+ w_scaled = int(x_max - x_min)
+ h_scaled = int(y_max - y_min)
word_positions.append((x_scaled + offset_x, y_scaled + offset_y, w_scaled + (margin * 2), h_scaled + (margin * 2)))