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"""consistency.py: Integrity Check, Correction by Mapping for Annotation Class, Metadata Cleaning, Statistics""" |
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import os |
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import re |
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import argparse |
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from loader import load_classes, load_properties, read_dataset, write_dataset, read_image, sample_name_tracable |
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from utils import bbdist |
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import matplotlib.pyplot as plt |
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import numpy as np |
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__author__ = "Johannes Bayer, Shabi Haider" |
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__copyright__ = "Copyright 2021-2023, DFKI, 2024-2025, Johannes Bayer" |
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__license__ = "CC" |
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__version__ = "0.0.2" |
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__email__ = "johannes.bayer@mail.de" |
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__status__ = "Prototype" |
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MAPPING_LOOKUP = { |
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"integrated_cricuit": "integrated_circuit", |
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"zener": "diode.zener" |
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} |
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def consistency(db: list, classes: dict, recover: dict = {}, check_texts=True, check_images=True) -> tuple: |
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"""Checks Whether Annotation Classes are in provided Classes Dict and Attempts Recovery""" |
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total, ok, mapped, faulty, rotation, mirror_h, mirror_v, text = 0, 0, 0, 0, 0, 0, 0, 0 |
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for sample in db: |
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for annotation in sample["bboxes"] + sample["polygons"] + sample["points"]: |
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total += 1 |
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if annotation["class"] in classes: |
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ok += 1 |
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if annotation["class"] in recover: |
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annotation["class"] = recover[annotation["class"]] |
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mapped += 1 |
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if annotation["class"] not in classes and annotation["class"] not in recover: |
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print(f"Can't recover faulty label in {sample_name_tracable(sample)}: {annotation['class']}") |
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faulty += 1 |
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if annotation["rotation"] is not None: |
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rotation += 1 |
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if annotation["mirror_horizontal"]: |
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mirror_h += 1 |
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if annotation["mirror_vertical"]: |
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mirror_v += 1 |
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if check_texts: |
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if annotation["class"] == "text" and annotation["text"] is None: |
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print(f"Missing Text in {sample_name_tracable(sample)} -> {annotation['xmin']}, {annotation['ymin']}") |
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if annotation["text"] is not None: |
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if annotation["text"].strip() != annotation["text"]: |
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print(f"Removing leading of trailing spaces from: {annotation['text']}") |
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annotation["text"] = annotation["text"].strip() |
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if annotation["class"] != "text": |
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print(f"Text string outside Text Annotation in {sample_name_tracable(sample)} [{annotation['xmin']:4}, {annotation['ymin']:4}]: {annotation['class']}: {annotation['text']}") |
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text += 1 |
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if check_images: |
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try: |
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height, width, _ = read_image(sample).shape |
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if (not sample['width'] == width) or (not sample['height'] == height): |
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sample['width'] = width |
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sample['height'] = height |
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print(f"Corrected Image Dimensions in Sample {sample_name_tracable(sample)}") |
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except AttributeError: |
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print(f"Missing or Corrupt Image for Sample {sample_name_tracable(sample)}") |
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return total, ok, mapped, faulty, rotation, mirror_h, mirror_v, text |
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def consistency_circuit(db: list, classes: dict) -> None: |
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"""Checks whether the Amount of Annotation per Class is Consistent Among the Samples of a Circuits""" |
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print("BBox Inconsistency Report:") |
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sample_cls_bb_count = {(sample["circuit"], sample["drawing"], sample["picture"]): |
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{cls: len([bbox for bbox in sample["bboxes"] if bbox["class"] == cls]) |
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for cls in classes} for sample in db} |
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for circuit in set(sample["circuit"] for sample in db): |
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circuit_samples = [sample for sample in sample_cls_bb_count if sample[0] == circuit] |
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for cls in classes: |
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check = [sample_cls_bb_count[sample][cls] for sample in circuit_samples] |
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if not all(c == check[0] for c in check): |
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print(f" Circuit {circuit}: {cls}: {check}") |
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def consistency_text(db: list) -> None: |
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"""Reports all Text Labels that Exist in a Strict Subset of Image Annotations of the same Circuit""" |
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for circuit in set(sample["circuit"] for sample in db): |
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circuit_samples = [sample for sample in db if sample["circuit"] == circuit] |
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circuit_samples_texts = [sorted([bbox["text"] for bbox in sample["bboxes"] if bbox["text"]]) |
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for sample in circuit_samples] |
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print(circuit) |
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for c in circuit_samples_texts: |
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print(c) |
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def circuit_annotations(db: list, classes: dict) -> None: |
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"""Plots the Annotations per Sample and Class""" |
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fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(8, 6)) |
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axes.plot([len(sample["bboxes"]) for sample in db], label="all") |
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for cls in classes: |
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axes.plot([len([annotation for annotation in sample["bboxes"] |
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if annotation["class"] == cls]) for sample in db], label=cls) |
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plt.minorticks_on() |
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axes.set_xticks(np.arange(0, len(db)+1, step=8)) |
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axes.set_xticks(np.arange(0, len(db), step=8)+4, minor=True) |
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axes.grid(axis='x', linestyle='solid') |
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axes.grid(axis='x', linestyle='dotted', alpha=0.7, which="minor") |
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plt.title("Class Distribution in Samples") |
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plt.xlabel("Image Sample") |
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plt.ylabel("BB Annotation Count") |
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plt.yscale('log') |
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plt.legend(ncol=2, loc='center left', bbox_to_anchor=(1.0, 0.5)) |
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plt.show() |
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def annotation_distribution(db: list) -> None: |
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amount_distribution([sample['bboxes'] for sample in db], |
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"Image Sample Count by BB Annotation Count", |
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"BB Annotation Count", |
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"Image Sample Count", |
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ticks=False) |
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def class_distribution(db: list, classes: dict) -> None: |
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"""Plots the Class Distribution over the Dataset""" |
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class_nbrs = np.arange(len(classes)) |
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class_counts = [sum([len([annotation for annotation in sample["bboxes"] + sample["polygons"] + sample["points"] |
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if annotation["class"] == cls]) |
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for sample in db]) for cls in classes] |
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bars = plt.bar(class_nbrs, class_counts) |
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plt.xticks(class_nbrs, labels=classes, rotation=90) |
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plt.yscale('log') |
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plt.title("Class Distribution") |
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plt.xlabel("Class") |
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plt.ylabel("BB Annotation Count") |
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for rect in bars: |
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height = rect.get_height() |
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plt.annotate('{}'.format(height), |
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xy=(rect.get_x() + rect.get_width() / 2, height), |
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xytext=(0, -3), textcoords="offset points", ha='center', va='top', rotation=90) |
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plt.show() |
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def image_sizes(db: list) -> None: |
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"""Statistics of the Raw Image's Widths and Heights""" |
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widths = [sample['width'] for sample in db] |
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heights = [sample['height'] for sample in db] |
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print(f"Raw Image Width Range: [{min(widths)}, {max(widths)}]") |
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print(f"Raw Image Height Range: [{min(heights)}, {max(heights)}]") |
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plt.title('Image Sizes') |
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plt.boxplot([heights, widths], vert=False) |
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plt.yticks([2, 1], labels=["width", "height"]) |
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plt.show() |
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def class_sizes(db: list, classes: dict) -> None: |
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"""""" |
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plt.title('BB Sizes') |
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plt.boxplot([[max(bbox["xmax"]-bbox["xmin"], bbox["ymax"]-bbox["ymin"]) |
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for sample in db for bbox in sample["bboxes"] if bbox["class"] == cls] |
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for cls in list(classes)[::-1]], vert=False) |
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class_nbrs = np.arange(len(classes))+1 |
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plt.yticks(class_nbrs, labels=list(classes)[::-1]) |
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plt.tight_layout() |
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plt.show() |
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def image_count(drafter: int = None, segmentation: bool = False) -> int: |
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"""Counts the Raw Images or Segmentation Maps in the Dataset""" |
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return len([file_name for root, _, files in os.walk(".") |
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for file_name in files |
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if (f"segmentation{os.sep}" if segmentation else "annotation") in root and |
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(drafter is None or f"drafter_{drafter}{os.sep}" in root)]) |
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def read_check_write(classes: dict, drafter: int = None, segmentation: bool = False, |
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check_images: bool = False, check_texts: bool = False) -> list: |
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"""Reads Annotations, Checks Consistency with Provided Classes |
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Writes Corrected Annotations Back and Returns the Annotations""" |
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db = read_dataset(drafter=drafter, segmentation=segmentation) |
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ann_total, ann_ok, ann_mapped, ann_faulty, ann_rot, ann_mirror_h, ann_mirror_v, ann_text = consistency(db, |
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classes, |
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MAPPING_LOOKUP, |
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check_texts=check_texts and not segmentation, |
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check_images=check_images) |
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write_dataset(db, segmentation=segmentation) |
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print("") |
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print(" Class and File Consistency Report") |
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print(" -------------------------------------") |
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print(f"Annotation Type: {'Polygon' if segmentation else 'Bounding Box'}") |
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print(f"Class Label Count: {len(classes)}") |
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print(f"Raw Image Files: {image_count(drafter=drafter, segmentation=segmentation)}") |
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print(f"Processed Annotation Files: {len(db)}") |
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print(f"Total Annotation Count: {ann_total}") |
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print(f"Consistent Annotations: {ann_ok}") |
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print(f"Faulty Annotations (no recovery): {ann_faulty}") |
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print(f"Corrected Annotations by Mapping: {ann_mapped}") |
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print(f"Annotations with Rotation: {ann_rot}") |
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print(f"Annotations with Mirror: {ann_mirror_h+ann_mirror_v} = {ann_mirror_h}(H) + {ann_mirror_v}(V)") |
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print(f"Annotations with Text: {ann_text}") |
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print("") |
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return db |
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def unique_characters(texts: list) -> list: |
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"""Returns the Sorted Set of Unique Characters""" |
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char_set = set([char for text in texts for char in text]) |
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return sorted(list(char_set)) |
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def character_distribution(texts: list, chars: list): |
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"""Plots and Returns the Character Distribution""" |
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char_nbrs = np.arange(len(chars)) |
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char_counts = [sum([len([None for text_char in text_label if text_char == char]) |
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for text_label in texts]) |
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for char in chars] |
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plt.bar(char_nbrs, char_counts) |
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plt.xticks(char_nbrs, chars) |
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plt.title("Character Distribution") |
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plt.xlabel("Character") |
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plt.ylabel("Overall Count") |
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plt.show() |
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return char_counts |
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def amount_distribution(list_of_lists: list, title: str, x_label: str, y_label: str, ticks: bool = True) -> None: |
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"""Plots a Histogram of the Amount of Things Contained in a List of Lists""" |
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max_bin = max([len(lst) for lst in list_of_lists]) |
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bin_numbers = np.arange(max_bin)+1 |
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text_count_by_length = [len([None for lst in list_of_lists if len(lst) == amount]) |
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for amount in bin_numbers] |
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plt.bar(bin_numbers, text_count_by_length) |
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if ticks: |
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plt.xticks(bin_numbers, rotation=90) |
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plt.title(title) |
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plt.xlabel(x_label) |
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plt.ylabel(y_label) |
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plt.show() |
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def text_proximity(db: list, cls_name: str, cls_regex: str): |
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"""Proximity-Based Regex Validation""" |
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cls_stat = {} |
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for sample in db: |
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bbs_text = [bbox for bbox in sample["bboxes"] if bbox["class"] == "text"] |
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bbs_symbol = [bbox for bbox in sample["bboxes"] if bbox["class"] not in ["text", "junction", "crossover"]] |
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for bb_text in bbs_text: |
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if bb_text["text"]: |
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if re.match(cls_regex, bb_text["text"]): |
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bb_closest_class = sorted(bbs_symbol, key=lambda bb: bbdist(bb_text, bb))[0]["class"] |
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cls_stat[bb_closest_class] = cls_stat.get(bb_closest_class, 0) + 1 |
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cls_stat = sorted(cls_stat.items(), key=lambda cls: -cls[1]) |
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print(cls_stat) |
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plt.bar(range(len(cls_stat)), [name for _, name in cls_stat]) |
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plt.xticks(range(len(cls_stat)), labels=[name for name, _ in cls_stat], rotation=90) |
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plt.title(f"Neighbor Distribution for {cls_name} Text Annotations") |
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plt.xlabel("Symbol Class") |
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plt.ylabel("Number of Closest Neighbors") |
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plt.tight_layout() |
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plt.show() |
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def text_statistics(db: list, plot_unique_labels: bool = False): |
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"""Generates and Plots Statistics on Text Classes""" |
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text_bbs = [bbox for sample in db for bbox in sample["bboxes"] if bbox["class"] == "text"] |
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text_labels = [bbox["text"] for bbox in text_bbs if type(bbox["text"]) is str and len(text_bbs) > 0] |
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text_labels_unique = set(text_labels) |
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chars_unique = unique_characters(text_labels) |
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char_counts = character_distribution(text_labels, chars_unique) |
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amount_distribution(text_labels, "Text Length Distribution", "Character Count", "Annotation Count") |
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print("") |
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print(" Text Statistics") |
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print("---------------------") |
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print(f"Text BB Annotations: {len(text_bbs)}") |
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print(f"Overall Text Label Count: {len(text_labels)}") |
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print(f"Annotation Completeness: {100*len(text_labels)/len(text_bbs):.2f}%") |
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print(f"Unique Text Label Count: {len(text_labels_unique)}") |
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print(f"Total Character Count: {sum([len(text_label) for text_label in text_labels])}") |
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print(f"Character Types: {len(chars_unique)}") |
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print("\n\nSet of all characters occurring in all text labels:") |
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print(chars_unique) |
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print("\n\nSet of Text Labels:") |
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print(text_labels_unique) |
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print("\nCharacter Frequencies:") |
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print({char: 1/char_count for char, char_count in zip(chars_unique, char_counts)}) |
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text_instances = text_labels_unique if plot_unique_labels else text_labels |
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text_classes_names = [] |
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text_classes_instances = [] |
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for text_class in load_properties(): |
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text_classes_names.append(text_class["name"]) |
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text_classes_instances.append([text_instance for text_instance in text_instances |
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if re.match(text_class["regex"], text_instance)]) |
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text_classified = [text for text_class_instances in text_classes_instances for text in text_class_instances] |
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text_classes_names.append("Unclassified") |
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text_classes_instances.append([text_instance for text_instance in text_instances |
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if text_instance not in text_classified]) |
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for text_class_name, text_class_instances in zip(text_classes_names, text_classes_instances): |
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print(f"\n{text_class_name}:") |
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print(sorted(list(set(text_class_instances)))) |
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plt.bar(text_classes_names, [len(text_class_instances) for text_class_instances in text_classes_instances]) |
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plt.title('Count of matching pattern') |
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plt.xlabel('Regex') |
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plt.ylabel('No. of text matched') |
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plt.xticks(rotation=90) |
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plt.tight_layout() |
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plt.show() |
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text_proximity(db, "Capacitor Name", "^C[0-9]+$") |
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text_proximity(db, "Resistor Name", "^R[0-9]+$") |
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text_proximity(db, "Inductor Name", "^L[0-9]+$") |
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if __name__ == "__main__": |
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parser = argparse.ArgumentParser(prog='CGHD Consistency', |
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description="Performs Integrity Checks and Statistics on the Dataset.") |
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parser.add_argument("-d", "--drafter", type=int, default=None, |
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help="Performs the actions on a given drafter only. If none is given, the entire dataset is used.") |
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parser.add_argument('-i', "--image-check", action='store_true', |
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help="Enables Image Dimension Verification") |
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parser.add_argument('-c', "--text-check", action='store_true', |
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help="searches for text labels outside text annotations and text annotations without text Label") |
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parser.add_argument('-a', "--annotation-consistency", action='store_true', |
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help="Enables Annotation Consistency Check (Class Count between Images of the Same Circuit)") |
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parser.add_argument('-t', "--text-consistency", action='store_true', |
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help="Enables Text Consistency Check (Label Equality between Images of the same Circuit)") |
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parser.add_argument('-s', "--statistics", action='store_true', |
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help="Performs Extended Statistics") |
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args = parser.parse_args() |
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classes = load_classes() |
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db_bb = read_check_write(classes, args.drafter, segmentation=False, |
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check_images=args.image_check, check_texts=args.text_check) |
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db_poly = read_check_write(classes, args.drafter, segmentation=True, |
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check_images=args.image_check, check_texts=args.text_check) |
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if args.annotation_consistency: |
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consistency_circuit(db_bb, classes) |
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if args.text_consistency: |
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consistency_text(db_bb) |
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if args.statistics: |
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image_sizes(db_bb) |
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class_sizes(db_bb, classes) |
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circuit_annotations(db_bb, classes) |
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annotation_distribution(db_bb) |
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class_distribution(db_bb, classes) |
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class_distribution(db_poly, classes) |
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text_statistics(db_bb) |
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