Commit
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4a2ff96
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Parent(s):
a90ff45
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Browse files- .gitignore +1 -0
- handler.py +2 -2
- requirements.txt +3 -0
- server.py +53 -0
.gitignore
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@@ -0,0 +1 @@
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sam_vit_l_0b3195.pth
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handler.py
CHANGED
@@ -23,9 +23,9 @@ class EndpointHandler():
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for index, mask in enumerate(masks):
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cv_image = np.array(raw_image)
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mask_image = np.zeros(cv_image.shape[:3], np.uint8)
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mask_image[mask ==
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retval, buffer = imencode('.png', mask_image)
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encoded_mask = b64encode(buffer)
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data.append({
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"score": outputs["scores"][index].item(),
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"mask": encoded_mask,
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for index, mask in enumerate(masks):
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cv_image = np.array(raw_image)
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mask_image = np.zeros(cv_image.shape[:3], np.uint8)
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mask_image[mask == True] = 255
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retval, buffer = imencode('.png', mask_image)
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encoded_mask = b64encode(buffer).decode("ascii")
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data.append({
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"score": outputs["scores"][index].item(),
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"mask": encoded_mask,
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requirements.txt
CHANGED
@@ -1,6 +1,9 @@
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1 |
torch
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transformers
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pillow
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numpy
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requests
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opencv-python
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torch
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torchvision
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transformers
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pillow
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numpy
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requests
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opencv-python
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segment_anything
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flask
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server.py
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import torch
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from flask import Flask, request, jsonify
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import numpy as np
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from PIL import Image
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from segment_anything import SamAutomaticMaskGenerator, sam_model_registry
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from cv2 import imencode
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from base64 import b64encode
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import requests
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import time
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device = "cuda" if torch.cuda.is_available() else "cpu"
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print(f"Using device: {device}")
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sam = sam_model_registry["vit_l"](checkpoint="sam_vit_l_0b3195.pth")
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sam.to(device=device)
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mask_generator = SamAutomaticMaskGenerator(sam)
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print("Loaded model")
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app = Flask(__name__)
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@app.route('/', methods=['POST'])
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def index():
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app.logger.info('Got request !')
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start = time.time()
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input = request.json
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url = input.get('url')
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app.logger.info('Got request for url %s', url)
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image = np.array(Image.open(requests.get(url, stream=True).raw).convert("RGB"))
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masks = mask_generator.generate(image)
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data = []
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for mask in masks:
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mask_image = np.zeros(image.shape[:3], np.uint8)
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mask_image[mask["segmentation"] == True] = 255
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retval, buffer = imencode('.png', mask_image)
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encoded_mask = b64encode(buffer).decode("ascii")
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data.append({
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"label": "",
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"mask": encoded_mask,
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"score": mask["predicted_iou"]
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})
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end = time.time()
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return jsonify({ "data": data, "time": end - start })
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@app.route('/health', methods=['GET'])
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def health():
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return jsonify({ "success": True })
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if __name__ == '__main__':
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app.run(host='0.0.0.0', port=8000)
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