postprocess.py

"""Evaluation for paa"""

import os
import numpy as np
from PIL import Image
from src.coco_eval import metrics
from src.model_utils.config import config


def get_pred(result_path, img_id):
    boxes_file = os.path.join(result_path, img_id + '_0.bin')
    scores_file = os.path.join(result_path, img_id + '_1.bin')

    boxes = np.fromfile(boxes_file, dtype=np.float32).reshape(67995, 4)
    scores = np.fromfile(scores_file, dtype=np.float32).reshape(67995, 81)
    return boxes, scores


def get_img_size(file_name):
    img = Image.open(file_name)
    return img.size


def get_img_id(img_id_file):
    f = open(img_id_file)
    lines = f.readlines()

    ids = []
    for line in lines:
        ids.append(int(line))

    return ids


def cal_acc(result_path, img_path, img_id_file):
    ids = get_img_id(img_id_file)
    imgs = os.listdir(img_path)
    pred_data = []

    for img in imgs:
        img_id = img.split('.')[0]
        if int(img_id) not in ids:
            continue
        boxes, box_scores = get_pred(result_path, img_id)

        w, h = get_img_size(os.path.join(img_path, img))
        img_shape = np.array((h, w), dtype=np.float32)
        pred_data.append({"boxes": boxes,
                          "box_scores": box_scores,
                          "img_id": int(img_id),
                          "image_shape": img_shape})

    mAP = metrics(pred_data)
    print(f"mAP: {mAP}")


if __name__ == '__main__':
    cal_acc(config.result_path, config.img_path, config.img_id_file)