85 lines
3.3 KiB
Python
85 lines
3.3 KiB
Python
import math
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from operator import itemgetter
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from image_prediction.config import CONFIG
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from image_prediction.transformer.transformer import Transformer
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from image_prediction.utils import get_logger
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logger = get_logger()
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class ResponseTransformer(Transformer):
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def transform(self, data):
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logger.debug("ResponseTransformer.transform")
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return build_image_info(data)
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def build_image_info(data: dict) -> dict:
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def compute_geometric_quotient():
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page_area_sqrt = math.sqrt(abs(page_width * page_height))
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image_area_sqrt = math.sqrt(abs(x2 - x1) * abs(y2 - y1))
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return image_area_sqrt / page_area_sqrt
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def is_max_image_to_page_quotient_breached(quotient, label):
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default_max_quotient = CONFIG.filters.image_to_page_quotient.max
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customized_entries = CONFIG.filters.image_to_page_quotient.customized.max
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max_quotient = customized_entries.get(label, default_max_quotient)
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max_quotient = max_quotient if max_quotient else default_max_quotient
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return bool(quotient > max_quotient)
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page_width, page_height, x1, x2, y1, y2, width, height, alpha = itemgetter(
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"page_width", "page_height", "x1", "x2", "y1", "y2", "width", "height", "alpha"
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)(data)
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quotient = round(compute_geometric_quotient(), 4)
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min_image_to_page_quotient_breached = bool(quotient < CONFIG.filters.image_to_page_quotient.min)
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max_image_to_page_quotient_breached = is_max_image_to_page_quotient_breached(
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quotient, data["classification"]["label"]
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)
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min_image_width_to_height_quotient_breached = bool(
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width / height < CONFIG.filters.image_width_to_height_quotient.min
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)
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max_image_width_to_height_quotient_breached = bool(
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width / height > CONFIG.filters.image_width_to_height_quotient.max
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)
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classification = data["classification"]
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representation = data["representation"]
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min_confidence_breached = bool(max(classification["probabilities"].values()) < CONFIG.filters.min_confidence)
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image_info = {
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"classification": classification,
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"representation": representation,
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"position": {"x1": x1, "x2": x2, "y1": y1, "y2": y2, "pageNumber": data["page_idx"] + 1},
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"geometry": {"width": width, "height": height},
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"alpha": alpha,
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"filters": {
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"geometry": {
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"imageSize": {
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"quotient": quotient,
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"tooLarge": max_image_to_page_quotient_breached,
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"tooSmall": min_image_to_page_quotient_breached,
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},
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"imageFormat": {
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"quotient": round(width / height, 4),
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"tooTall": min_image_width_to_height_quotient_breached,
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"tooWide": max_image_width_to_height_quotient_breached,
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},
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},
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"probability": {"unconfident": min_confidence_breached},
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"allPassed": not any(
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[
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max_image_to_page_quotient_breached,
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min_image_to_page_quotient_breached,
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min_image_width_to_height_quotient_breached,
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max_image_width_to_height_quotient_breached,
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min_confidence_breached,
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]
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),
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},
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}
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return image_info
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