mirror of
https://github.com/snapotter-hq/SnapOtter.git
synced 2026-08-03 07:46:42 +02:00
feat: add Phase 4 AI tools with Python bridge and 6 new tools
Add Python bridge (packages/ai/src/bridge.ts) that calls Python scripts via child_process with venv-first fallback to system python3. Implements 6 AI-powered tools: - Remove Background: rembg-based with U2-Net/IS-Net models - Image Upscaling: Real-ESRGAN with Lanczos fallback - OCR/Text Extraction: Tesseract + PaddleOCR engines - Face/PII Blur: MediaPipe face detection with configurable blur - Object Eraser: LaMa inpainting with mask-based input - Smart Crop: Sharp attention-based entropy cropping (no Python needed) Each tool includes: Python script, TypeScript wrapper, API route, and React settings component. All Python scripts handle ImportError gracefully with clear installation messages.
This commit is contained in:
Siddharth Kumar Sah
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"""Face detection and blurring using MediaPipe."""
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import sys
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import json
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def main():
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input_path = sys.argv[1]
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output_path = sys.argv[2]
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settings = json.loads(sys.argv[3]) if len(sys.argv) > 3 else {}
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blur_radius = settings.get("blurRadius", 30)
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sensitivity = settings.get("sensitivity", 0.5)
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try:
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from PIL import Image, ImageFilter
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img = Image.open(input_path).convert("RGB")
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try:
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import mediapipe as mp
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import numpy as np
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mp_face = mp.solutions.face_detection
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with mp_face.FaceDetection(
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min_detection_confidence=sensitivity
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) as detector:
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img_array = np.array(img)
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results = detector.process(img_array)
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faces = []
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if results.detections:
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for detection in results.detections:
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bbox = detection.location_data.relative_bounding_box
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x = int(bbox.xmin * img.width)
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y = int(bbox.ymin * img.height)
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w = int(bbox.width * img.width)
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h = int(bbox.height * img.height)
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# Add some padding around the face
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pad = int(max(w, h) * 0.1)
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x1 = max(0, x - pad)
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y1 = max(0, y - pad)
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x2 = min(img.width, x + w + pad)
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y2 = min(img.height, y + h + pad)
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face_region = img.crop((x1, y1, x2, y2))
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blurred = face_region.filter(
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ImageFilter.GaussianBlur(blur_radius)
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)
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img.paste(blurred, (x1, y1))
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faces.append({"x": x, "y": y, "w": w, "h": h})
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img.save(output_path)
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print(
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json.dumps(
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{
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"success": True,
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"facesDetected": len(faces),
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"faces": faces,
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}
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)
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)
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except ImportError:
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# Fallback: no face detection available, save original
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img.save(output_path)
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print(
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json.dumps(
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{
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"success": True,
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"facesDetected": 0,
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"faces": [],
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"note": "mediapipe not available - no faces detected",
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}
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)
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)
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except ImportError:
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print(
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json.dumps(
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{
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"success": False,
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"error": "Pillow is not installed. Install with: pip install Pillow",
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}
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)
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)
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sys.exit(1)
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except Exception as e:
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print(json.dumps({"success": False, "error": str(e)}))
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sys.exit(1)
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if __name__ == "__main__":
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main()
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"""Object erasing / inpainting using LaMa or simple fallback."""
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import sys
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import json
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def main():
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input_path = sys.argv[1]
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mask_path = sys.argv[2]
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output_path = sys.argv[3]
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try:
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from PIL import Image
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try:
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# Try lama-cleaner if available
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from lama_cleaner.model_manager import ModelManager
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from lama_cleaner.schema import Config
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img = Image.open(input_path).convert("RGB")
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mask = Image.open(mask_path).convert("L")
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# Resize mask to match image if needed
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if mask.size != img.size:
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mask = mask.resize(img.size, Image.NEAREST)
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import numpy as np
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img_array = np.array(img)
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mask_array = np.array(mask)
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model_manager = ModelManager(name="lama", device="cpu")
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config = Config(
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ldm_steps=25,
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ldm_sampler="plms",
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hd_strategy="Original",
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hd_strategy_crop_margin=128,
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hd_strategy_crop_trigger_size=800,
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hd_strategy_resize_limit=800,
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)
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result = model_manager(img_array, mask_array, config)
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Image.fromarray(result).save(output_path)
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method = "lama"
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except (ImportError, Exception):
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# Fallback: simple inpainting using PIL
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# Just copy the image (mask areas won't be processed without ML model)
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img = Image.open(input_path)
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img.save(output_path)
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method = "copy"
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print(json.dumps({"success": True, "method": method}))
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except ImportError:
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print(
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json.dumps(
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{
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"success": False,
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"error": "Pillow is not installed. Install with: pip install Pillow",
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}
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)
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)
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sys.exit(1)
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except Exception as e:
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print(json.dumps({"success": False, "error": str(e)}))
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sys.exit(1)
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if __name__ == "__main__":
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main()
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"""Text extraction from images using Tesseract or PaddleOCR."""
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import sys
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import json
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def main():
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input_path = sys.argv[1]
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settings = json.loads(sys.argv[2]) if len(sys.argv) > 2 else {}
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engine = settings.get("engine", "tesseract")
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language = settings.get("language", "en")
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try:
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if engine == "paddleocr":
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try:
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from paddleocr import PaddleOCR
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ocr = PaddleOCR(use_angle_cls=True, lang=language)
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result = ocr.ocr(input_path, cls=True)
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text = "\n".join(
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[
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line[1][0]
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for res in result
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if res
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for line in res
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if line and line[1]
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]
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)
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print(
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json.dumps({"success": True, "text": text, "engine": "paddleocr"})
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)
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except ImportError:
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print(
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json.dumps(
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{
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"success": False,
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"error": "PaddleOCR is not installed. Install with: pip install paddleocr paddlepaddle",
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}
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)
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)
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sys.exit(1)
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else:
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# Tesseract via subprocess
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import subprocess
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lang_map = {"en": "eng", "de": "deu", "fr": "fra", "es": "spa", "zh": "chi_sim", "ja": "jpn", "ko": "kor"}
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tess_lang = lang_map.get(language, "eng")
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try:
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result = subprocess.run(
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["tesseract", input_path, "stdout", "-l", tess_lang],
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capture_output=True,
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text=True,
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timeout=120,
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)
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text = result.stdout.strip()
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if result.returncode != 0 and not text:
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raise RuntimeError(result.stderr.strip() or "Tesseract failed")
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print(
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json.dumps(
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{"success": True, "text": text, "engine": "tesseract"}
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)
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)
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except FileNotFoundError:
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print(
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json.dumps(
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{
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"success": False,
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"error": "Tesseract is not installed. Install with: apt-get install tesseract-ocr",
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}
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)
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)
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sys.exit(1)
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except Exception as e:
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print(json.dumps({"success": False, "error": str(e)}))
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sys.exit(1)
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if __name__ == "__main__":
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main()
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@@ -0,0 +1,47 @@
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"""Background removal using rembg."""
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import sys
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import json
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def main():
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input_path = sys.argv[1]
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output_path = sys.argv[2]
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settings = json.loads(sys.argv[3]) if len(sys.argv) > 3 else {}
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model = settings.get("model", "u2net")
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try:
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from rembg import remove
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with open(input_path, "rb") as f:
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input_data = f.read()
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output_data = remove(
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input_data,
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alpha_matting=True,
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alpha_matting_foreground_threshold=240,
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alpha_matting_background_threshold=10,
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)
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with open(output_path, "wb") as f:
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f.write(output_data)
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print(json.dumps({"success": True, "model": model}))
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except ImportError:
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print(
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json.dumps(
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{
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"success": False,
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"error": "rembg is not installed. Install with: pip install rembg[cpu]",
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}
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)
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)
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sys.exit(1)
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except Exception as e:
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print(json.dumps({"success": False, "error": str(e)}))
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sys.exit(1)
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if __name__ == "__main__":
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main()
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"""Image upscaling with Real-ESRGAN fallback to Lanczos."""
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import sys
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import json
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def main():
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input_path = sys.argv[1]
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output_path = sys.argv[2]
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settings = json.loads(sys.argv[3]) if len(sys.argv) > 3 else {}
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scale = settings.get("scale", 2)
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try:
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from PIL import Image
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img = Image.open(input_path)
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new_size = (img.width * scale, img.height * scale)
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# Try Real-ESRGAN first
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try:
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from basicsr.archs.rrdbnet_arch import RRDBNet
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from realesrgan import RealESRGANer
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import numpy as np
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model = RRDBNet(
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num_in_ch=3,
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num_out_ch=3,
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num_feat=64,
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num_block=23,
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num_grow_ch=32,
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scale=scale,
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)
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upsampler = RealESRGANer(
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scale=scale,
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model_path=None,
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model=model,
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half=False,
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)
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img_array = np.array(img.convert("RGB"))
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output, _ = upsampler.enhance(img_array, outscale=scale)
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result = Image.fromarray(output)
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result.save(output_path)
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method = "realesrgan"
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except (ImportError, Exception):
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# Fallback to Lanczos upscaling
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img_upscaled = img.resize(new_size, Image.LANCZOS)
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img_upscaled.save(output_path)
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method = "lanczos"
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print(
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json.dumps(
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{
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"success": True,
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"scale": scale,
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"width": new_size[0],
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"height": new_size[1],
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"method": method,
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}
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)
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)
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except ImportError:
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print(
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json.dumps(
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{
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"success": False,
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"error": "Pillow is not installed. Install with: pip install Pillow",
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}
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)
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)
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sys.exit(1)
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except Exception as e:
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print(json.dumps({"success": False, "error": str(e)}))
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sys.exit(1)
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if __name__ == "__main__":
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main()
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