Update main.py
Browse files
main.py
CHANGED
@@ -2,9 +2,10 @@ from fastapi import FastAPI, File, UploadFile, Form
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from fastapi.responses import StreamingResponse
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from fastapi.staticfiles import StaticFiles
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import torch
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import cv2
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import numpy as np
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import
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from io import BytesIO
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app = FastAPI()
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@@ -18,9 +19,6 @@ def load_model():
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model = Model(device='cuda' if torch.cuda.is_available() else 'cpu')
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model.load_model('cartoon4')
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# Configure logging
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logging.basicConfig(level=logging.INFO)
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@app.post("/upload/")
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async def process_image(file: UploadFile = File(...), top: int = Form(...), bottom: int = Form(...), left: int = Form(...), right: int = Form(...)):
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global model
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@@ -32,26 +30,13 @@ async def process_image(file: UploadFile = File(...), top: int = Form(...), bott
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# Convert the uploaded image to numpy array
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nparr = np.frombuffer(contents, np.uint8)
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if frame_bgr is None:
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logging.error("Failed to decode the image.")
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return {"error": "Failed to decode the image. Please ensure the file is a valid image format."}
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logging.info(f"Uploaded image shape: {frame_bgr.shape}")
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# Process the uploaded image
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aligned_face, instyle, message = model.detect_and_align_image(
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logging.error("Failed to process the image: No face detected or alignment failed.")
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return {"error": message}
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processed_image, message = model.image_toonify(aligned_face, instyle, model.exstyle, style_degree=0.5, style_type='cartoon1')
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if processed_image is None:
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logging.error("Failed to toonify the image.")
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return {"error": message}
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# Convert BGR to RGB
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processed_image_rgb = cv2.cvtColor(processed_image, cv2.COLOR_BGR2RGB)
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# Convert processed image to bytes
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@@ -60,11 +45,12 @@ async def process_image(file: UploadFile = File(...), top: int = Form(...), bott
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# Return the processed image as a streaming response
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return StreamingResponse(BytesIO(encoded_image.tobytes()), media_type="image/jpeg")
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# Mount static files directory
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app.mount("/", StaticFiles(directory="AB", html=True), name="static")
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# Define index route
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@app.get("/")
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def index():
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from fastapi.responses import StreamingResponse
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from fastapi.staticfiles import StaticFiles
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import torch
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import shutil
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import cv2
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import numpy as np
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import io
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from io import BytesIO
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app = FastAPI()
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model = Model(device='cuda' if torch.cuda.is_available() else 'cpu')
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model.load_model('cartoon4')
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@app.post("/upload/")
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async def process_image(file: UploadFile = File(...), top: int = Form(...), bottom: int = Form(...), left: int = Form(...), right: int = Form(...)):
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global model
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# Convert the uploaded image to numpy array
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nparr = np.frombuffer(contents, np.uint8)
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frame_rgb = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
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# Process the uploaded image
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aligned_face, instyle, message = model.detect_and_align_image(frame_rgb, top, bottom, left, right)
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processed_image, message = model.image_toonify(aligned_face, instyle, model.exstyle, style_degree=0.5, style_type='cartoon4')
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# Convert BGR to RGB
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processed_image_rgb = cv2.cvtColor(processed_image, cv2.COLOR_BGR2RGB)
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# Convert processed image to bytes
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# Return the processed image as a streaming response
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return StreamingResponse(BytesIO(encoded_image.tobytes()), media_type="image/jpeg")
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# Mount static files directory
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app.mount("/", StaticFiles(directory="AB", html=True), name="static")
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# Define index route
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@app.get("/")
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def index():
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return FileResponse(path="/app/AB/index.html", media_type="text/html")
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