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Runtime error
File size: 3,078 Bytes
af4d6e0 9f0ff72 af4d6e0 9f0ff72 fd06368 af4d6e0 0159203 af4d6e0 9f0ff72 fd06368 9f0ff72 af4d6e0 fd06368 af4d6e0 9f0ff72 af4d6e0 fd06368 9f0ff72 af4d6e0 fd06368 b5d008d fd06368 665fc46 fd06368 b5d008d 3f12567 fd06368 3f12567 fd06368 83995ab 9f0ff72 83995ab af4d6e0 fd06368 | 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 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 | from fastapi import FastAPI, File, UploadFile
from fastapi.responses import JSONResponse
from fastapi.middleware.cors import CORSMiddleware
from PIL import Image
import io
import torch
from transformers import AutoImageProcessor, AutoBackbone
import pytesseract # OCR
app = FastAPI()
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_methods=["*"],
allow_headers=["*"],
)
processor = AutoImageProcessor.from_pretrained("czczup/textnet-base")
model = AutoBackbone.from_pretrained("czczup/textnet-base")
model.eval()
@app.post("/detect")
async def detect_text(file: UploadFile = File(...)):
try:
# Lire image
image_bytes = await file.read()
image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
# Entrée TextNet
inputs = processor(image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
# Feature map et heatmap
fm = outputs.feature_maps[-1][0] # dernière layer
heatmap = fm.mean(dim=0).numpy()
H, W = heatmap.shape
threshold = heatmap.max() * 0.2
# Points chauds
points = [(x, y) for y in range(H) for x in range(W) if heatmap[y, x] > threshold]
if not points:
return JSONResponse([])
# Regrouper par lignes simples
lines = {}
for x, y in points:
key = int(y / 10)
lines.setdefault(key, []).append((x, y))
# Générer boxes et extraire texte OCR
scale_x = image.width / W
scale_y = image.height / H
boxes = []
for line in lines.values():
xs = [p[0] for p in line]
ys = [p[1] for p in line]
min_x, max_x = min(xs), max(xs)
min_y, max_y = min(ys), max(ys)
if (max_x - min_x) < 5 or (max_y - min_y) < 2:
continue
# Crop pour OCR
crop = image.crop((
int(min_x * scale_x),
int(min_y * scale_y),
int(max_x * scale_x),
int(max_y * scale_y)
))
text = pytesseract.image_to_string(crop, lang='eng').strip()
if len(text) < 2:
continue
if len(boxes) == 0:
boxes.append({
"x": 10,
"y": 10,
"w": 100,
"h": 50,
"text": "Aucun texte détecté"
})
boxes.append({
"x": int(min_x * scale_x),
"y": int(min_y * scale_y),
"w": int((max_x - min_x) * scale_x),
"h": int((max_y - min_y) * scale_y),
"text": text or "texte non reconnu"
})
print("BOXES:", boxes)
return JSONResponse({
"image_width": image.width,
"image_height": image.height,
"boxes": boxes
})
except Exception as e:
return JSONResponse({"success": False, "error": str(e)}, status_code=500) |