File size: 2,216 Bytes
248607a 8037bf7 58e8f4d 8037bf7 58e8f4d 8037bf7 58e8f4d 8037bf7 248607a 8037bf7 b69abfd 00c41dd 8037bf7 b69abfd 8037bf7 b69abfd 8037bf7 b69abfd 8037bf7 b69abfd 8037bf7 248607a 58e8f4d 8037bf7 58e8f4d 8037bf7 58e8f4d 8037bf7 58e8f4d 8037bf7 58e8f4d 248607a 8037bf7 248607a 8037bf7 248607a 8037bf7 58e8f4d 248607a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 |
from flask import Flask, request, jsonify
from PIL import Image
import base64
from io import BytesIO
from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation
import torch
import numpy as np
import matplotlib.pyplot as plt
import cv2
app = Flask(__name__)
processor = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined")
model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64-refined")
def process_image(image, prompt, threshold, alpha_value, draw_rectangles):
inputs = processor(
text=prompt, images=image, padding="max_length", return_tensors="pt"
)
# predict
with torch.no_grad():
outputs = model(**inputs)
preds = outputs.logits
pred = torch.sigmoid(preds)
mat = pred.cpu().numpy()
mask = Image.fromarray(np.uint8(mat * 255), "L")
mask = mask.resize(image.size)
mask = np.array(mask)[:, :, 0]
# normalize the mask
mask_min = mask.min()
mask_max = mask.max()
mask = (mask - mask_min) / (mask_max - mask_min)
# threshold the mask
bmask = mask > threshold
# zero out values below the threshold
mask[mask < threshold] = 0
bmask = Image.fromarray(bmask.astype(np.uint8) * 255, "L")
return bmask
@app.route('/')
def index():
return "Hello, World! clipseg2"
@app.route('/api/mask_image', methods=['POST'])
def mask_image_api():
data = request.get_json()
base64_image = data.get('base64_image', '')
prompt = data.get('prompt', '')
threshold = data.get('threshold', 0.4)
alpha_value = data.get('alpha_value', 0.5)
draw_rectangles = data.get('draw_rectangles', False)
# Decode base64 image
image_data = base64.b64decode(base64_image.split(',')[1])
image = Image.open(BytesIO(image_data))
# Process the image
output_mask = process_image(image, prompt, threshold, alpha_value, draw_rectangles)
# Convert the output mask to base64
buffered_mask = BytesIO()
output_mask.save(buffered_mask, format="PNG")
result_mask = base64.b64encode(buffered_mask.getvalue()).decode('utf-8')
return jsonify({'result_mask': result_mask})
if __name__ == '__main__':
app.run(host='0.0.0.0', port=7860, debug=False)
|