garmentiq.utils.clean_detection_dict

Trimming a detection record down to its reportable fields.

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 1"""Trimming a detection record down to its reportable fields."""
 2import os
 3from garmentiq.utils import compute_measurement_distances
 4
 5
 6def clean_detection_dict(class_name: str, image_name: str, detection_dict: dict):
 7    """
 8    Cleans and reformats a detection dictionary, computes measurement distances,
 9    and nests the cleaned data under the image name, with class name stored inside.
10
11    Args:
12        class_name (str): The name of the garment class.
13        image_name (str): The original filename of the image.
14        detection_dict (dict): The raw detection dictionary containing landmark and measurement data.
15
16    Returns:
17        dict: A dictionary structured as {image_name: {..., "class": class_name}}.
18    """
19
20    transformed_name = os.path.splitext(image_name)[0]
21
22    # Compute distances and get a fresh copy of detection_dict
23    _, clean_dict = compute_measurement_distances(detection_dict)
24
25    # Safely extract and clean the content under the class_name
26    original_data = clean_dict.get(class_name, {})
27
28    # Clean landmarks
29    if "landmarks" in original_data:
30        for lm_id in list(original_data["landmarks"].keys()):
31            lm = original_data["landmarks"][lm_id]
32            original_data["landmarks"][lm_id] = {
33                k: lm[k] for k in ("x", "y", "conf") if k in lm
34            }
35
36    # Clean measurements
37    if "measurements" in original_data:
38        for m_id in list(original_data["measurements"].keys()):
39            m = original_data["measurements"][m_id]
40            original_data["measurements"][m_id] = {
41                k: m[k] for k in ("landmarks", "distance") if k in m
42            }
43
44    # Insert the class name as metadata
45    original_data["class"] = class_name
46
47    # Return dict keyed by image name
48    final_dict = {image_name: original_data}
49    return final_dict

def clean_detection_dict(class_name: str, image_name: str, detection_dict: dict): View Source

 7def clean_detection_dict(class_name: str, image_name: str, detection_dict: dict):
 8    """
 9    Cleans and reformats a detection dictionary, computes measurement distances,
10    and nests the cleaned data under the image name, with class name stored inside.
11
12    Args:
13        class_name (str): The name of the garment class.
14        image_name (str): The original filename of the image.
15        detection_dict (dict): The raw detection dictionary containing landmark and measurement data.
16
17    Returns:
18        dict: A dictionary structured as {image_name: {..., "class": class_name}}.
19    """
20
21    transformed_name = os.path.splitext(image_name)[0]
22
23    # Compute distances and get a fresh copy of detection_dict
24    _, clean_dict = compute_measurement_distances(detection_dict)
25
26    # Safely extract and clean the content under the class_name
27    original_data = clean_dict.get(class_name, {})
28
29    # Clean landmarks
30    if "landmarks" in original_data:
31        for lm_id in list(original_data["landmarks"].keys()):
32            lm = original_data["landmarks"][lm_id]
33            original_data["landmarks"][lm_id] = {
34                k: lm[k] for k in ("x", "y", "conf") if k in lm
35            }
36
37    # Clean measurements
38    if "measurements" in original_data:
39        for m_id in list(original_data["measurements"].keys()):
40            m = original_data["measurements"][m_id]
41            original_data["measurements"][m_id] = {
42                k: m[k] for k in ("landmarks", "distance") if k in m
43            }
44
45    # Insert the class name as metadata
46    original_data["class"] = class_name
47
48    # Return dict keyed by image name
49    final_dict = {image_name: original_data}
50    return final_dict

Cleans and reformats a detection dictionary, computes measurement distances, and nests the cleaned data under the image name, with class name stored inside.

Arguments:

class_name (str): The name of the garment class.
image_name (str): The original filename of the image.
detection_dict (dict): The raw detection dictionary containing landmark and measurement data.

Returns:

dict: A dictionary structured as {image_name: {..., "class": class_name}}.