PaddleX/paddlex/inference/components/task_related/det.py

# copyright (c) 2024 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import os

import numpy as np
from ...utils.io import ImageReader
from ..base import BaseComponent


def restructured_boxes(boxes, labels, img_size):

    box_list = []
    w, h = img_size

    for box in boxes:
        xmin, ymin, xmax, ymax = list(map(int, box[2:]))
        xmin = max(0, xmin)
        ymin = max(0, ymin)
        xmax = min(w, xmax)
        ymax = min(h, ymax)
        box_list.append(
            {
                "cls_id": int(box[0]),
                "label": labels[int(box[0])],
                "score": float(box[1]),
                "coordinate": [xmin, ymin, xmax, ymax],
            }
        )

    return box_list


class DetPostProcess(BaseComponent):
    """Save Result Transform"""

    INPUT_KEYS = ["img_path", "boxes", "img_size"]
    OUTPUT_KEYS = ["boxes"]
    DEAULT_INPUTS = {"boxes": "boxes", "img_size": "ori_img_size"}
    DEAULT_OUTPUTS = {"boxes": "boxes"}

    def __init__(self, threshold=0.5, labels=None):
        super().__init__()
        self.threshold = threshold
        self.labels = labels

    def apply(self, boxes, img_size):
        """apply"""
        expect_boxes = (boxes[:, 1] > self.threshold) & (boxes[:, 0] > -1)
        boxes = boxes[expect_boxes, :]
        boxes = restructured_boxes(boxes, self.labels, img_size)
        result = {"boxes": boxes}

        return result


class CropByBoxes(BaseComponent):
    """Crop Image by Box"""

    YIELD_BATCH = False
    INPUT_KEYS = ["img_path", "boxes"]
    OUTPUT_KEYS = ["img", "box", "label"]
    DEAULT_INPUTS = {"img_path": "img_path", "boxes": "boxes"}
    DEAULT_OUTPUTS = {"img": "img", "box": "box", "label": "label"}

    def __init__(self):
        super().__init__()
        self._reader = ImageReader(backend="opencv")

    def apply(self, img_path, boxes):
        output_list = []
        img = self._reader.read(img_path)
        for bbox in boxes:
            label_id = bbox["cls_id"]
            box = bbox["coordinate"]
            label = bbox.get("label", label_id)
            xmin, ymin, xmax, ymax = [int(i) for i in box]
            img_crop = img[ymin:ymax, xmin:xmax]
            output_list.append({"img": img_crop, "box": box, "label": label})

        return output_list


class DetPad(BaseComponent):

    INPUT_KEYS = "img"
    OUTPUT_KEYS = "img"
    DEAULT_INPUTS = {"img": "img"}
    DEAULT_OUTPUTS = {"img": "img"}

    def __init__(self, size, fill_value=[114.0, 114.0, 114.0]):
        """
        Pad image to a specified size.
        Args:
            size (list[int]): image target size
            fill_value (list[float]): rgb value of pad area, default (114.0, 114.0, 114.0)
        """

        super().__init__()
        if isinstance(size, int):
            size = [size, size]
        self.size = size
        self.fill_value = fill_value

    def apply(self, img):
        im = img
        im_h, im_w = im.shape[:2]
        h, w = self.size
        if h == im_h and w == im_w:
            return {"img": im}

        canvas = np.ones((h, w, 3), dtype=np.float32)
        canvas *= np.array(self.fill_value, dtype=np.float32)
        canvas[0:im_h, 0:im_w, :] = im.astype(np.float32)
        return {"img": canvas}