150342bb9e
- Extract helpers to reduce function sizes (listing_tasks, app.py, query.py, listing_fetcher) - Replace nonlocal mutations with _PipelineState dataclass in listing_tasks - Fix bugs: isinstance→equality check in repository, verify_exp for OIDC tokens - Consolidate duplicate filter methods in listing_repository - Move hardcoded config to env vars with backward-compatible defaults - Simplify CLI decorator to auto-build QueryParameters - Add deprecation docstring to data_access.py - Test count: 158 → 387 (all passing)
67 lines
2.3 KiB
Python
67 lines
2.3 KiB
Python
import logging
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import re
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from pathlib import Path
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from typing import Any
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from PIL import Image
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import cv2
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import numpy as np
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logger = logging.getLogger(__name__)
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MIN_SQM = 30
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MAX_SQM = 160
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def inference(image_path: str | Path) -> tuple[str, Any]:
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from transformers import Pix2StructProcessor, Pix2StructForConditionalGeneration
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image = Image.open(image_path)
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question = "How many living rooms are displayed on this floor plan?" # not sure if it even has an effect
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processor = Pix2StructProcessor.from_pretrained("google/deplot")
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model = Pix2StructForConditionalGeneration.from_pretrained("google/deplot")
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inputs = processor(images=image, text=question, return_tensors="pt")
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predictions = model.generate(**inputs, max_new_tokens=512)
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output = processor.decode(predictions[0], skip_special_tokens=True)
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return output, predictions
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def extract_total_sqm(input_str: str) -> float | None:
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# Note: can be used on the output of inference() to extract sqm from model predictions.
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sqmregex = r"(\d+\.?\d*) ?(sq ?m|sq. ?m)"
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matches = re.findall(sqmregex, input_str.lower())
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sqms = [float(m[0]) for m in matches]
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filtered = [sqm for sqm in sqms if MIN_SQM < sqm < MAX_SQM]
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if len(filtered) == 0:
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return None
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return max(filtered)
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def improve_img_for_ocr(img: Image.Image) -> Image.Image:
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grayscale_image = np.array(img.convert("L"))
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grayscale_image = cv2.resize(grayscale_image, None, fx=1.2, fy=1.2, interpolation=cv2.INTER_CUBIC)
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thresh = cv2.adaptiveThreshold(
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grayscale_image, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2
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)
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return Image.fromarray(thresh)
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def calculate_ocr(image_path: str | Path) -> tuple[float | None, str]:
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import pytesseract
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path = Path(image_path)
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if not path.exists():
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raise FileNotFoundError(f"Image not found: {image_path}")
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img = Image.open(path)
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text = pytesseract.image_to_string(img)
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estimated_sqm = extract_total_sqm(text)
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if estimated_sqm is None:
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improved_img = improve_img_for_ocr(img)
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text2 = pytesseract.image_to_string(improved_img)
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estimated_sqm2 = extract_total_sqm(text2)
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logger.debug(f"before: {estimated_sqm} after: {estimated_sqm2} - {image_path}")
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return estimated_sqm2, text2
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return estimated_sqm, text
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