main.py

from flask import Flask, request, send_file, Response, jsonify
from flask_cors import CORS
import numpy as np
import io
import torch
import cv2
from segment_anything import sam_model_registry, SamAutomaticMaskGenerator
from PIL import Image
import zipfile

app = Flask(__name__)
CORS(app)

cudaOrNah = "cuda" if torch.cuda.is_available() else "cpu"
print(cudaOrNah)

# Global model setup 
# running out of memory adjusted
# checkpoint = "sam_vit_h_4b8939.pth"
# model_type = "vit_h"
checkpoint = "sam_vit_l_0b3195.pth"
model_type = "vit_l"
sam = sam_model_registry[model_type](checkpoint=checkpoint)
sam.to(device=cudaOrNah)
mask_generator = SamAutomaticMaskGenerator(
    model=sam,
    min_mask_region_area=0.0015  # Adjust this value as needed
)
print('Setup SAM model')

@app.route('/')
def hello():
    return {"hei": "Shredded to peices"}

@app.route('/health', methods=['GET'])
def health_check():
    # Simple health check endpoint
    return jsonify({"status": "ok"}), 200

@app.route('/get-masks', methods=['POST'])
def get_masks():
    try:
        print('received image from frontend')
        # Get the image file from the request
        if 'image' not in request.files:
            return jsonify({"error": "No image file provided"}), 400
        
        image_file = request.files['image']
        if image_file.filename == '':
            return jsonify({"error": "No image file provided"}), 400

        # Read image file using OpenCV-style approach (similar to cv2.imread)s
        # Convert the image file to a NumPy array using OpenCV
        file_bytes = np.fromstring(image_file.read(), np.uint8)
        image = cv2.imdecode(file_bytes, cv2.IMREAD_COLOR)

        # Convert BGR to RGB using OpenCV (similar to cv2.cvtColor)
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

        if image is None:
            raise ValueError("Image not found or unable to read.")
        
        if cudaOrNah == "cuda":
            torch.cuda.empty_cache()
        
        masks = mask_generator.generate(image)

        if cudaOrNah == "cuda":
            torch.cuda.empty_cache()

        masks = sorted(masks, key=(lambda x: x['area']), reverse=True)

        def is_background(segmentation):
            val = (segmentation[10, 10] or segmentation[-10, 10] or
                   segmentation[10, -10] or segmentation[-10, -10])
            return val

        masks = [mask for mask in masks if not is_background(mask['segmentation'])]

        for i in range(0, len(masks) - 1)[::-1]:
            large_mask = masks[i]['segmentation']
            for j in range(i+1, len(masks)):
                not_small_mask = np.logical_not(masks[j]['segmentation'])
                masks[i]['segmentation'] = np.logical_and(large_mask, not_small_mask)
                masks[i]['area'] = masks[i]['segmentation'].sum()
                large_mask = masks[i]['segmentation']

        def sum_under_threshold(segmentation, threshold):
            return segmentation.sum() / segmentation.size < 0.0015

        masks = [mask for mask in masks if not sum_under_threshold(mask['segmentation'], 100)]
        masks = sorted(masks, key=(lambda x: x['area']), reverse=True)

        # Create a zip file in memory
        zip_buffer = io.BytesIO()
        with zipfile.ZipFile(zip_buffer, 'w', zipfile.ZIP_DEFLATED) as zip_file:
            for idx, mask in enumerate(masks):
                alpha = mask['segmentation'].astype('uint8') * 255
                mask_image = Image.fromarray(alpha)
                mask_io = io.BytesIO()
                mask_image.save(mask_io, format="PNG")
                mask_io.seek(0)
                zip_file.writestr(f'mask_{idx+1}.png', mask_io.read())

        zip_buffer.seek(0)
        
        return send_file(zip_buffer, mimetype='application/zip', as_attachment=True, download_name='masks.zip')
    except Exception as e:
        # Log the error message if needed
        print(f"Error processing the image: {e}")
        # Return a JSON response with the error message and a 400 Bad Request status
        return jsonify({"error": "Error processing the image", "details": str(e)}), 400

if __name__ == '__main__':
    app.run(debug=True)