pokouqiege/models/line_detect/line_dataset.py

import cv2
import imageio
import numpy as np
from skimage.draw import ellipse
from torch.utils.data.dataset import T_co

from libs.vision_libs.utils import draw_keypoints
from models.base.base_dataset import BaseDataset

import json
import os
import PIL
import matplotlib as mpl
from torchvision.utils import draw_bounding_boxes
import torchvision.transforms.v2 as transforms
import torch

import matplotlib.pyplot as plt
from models.base.transforms import get_transforms
from utils.data_process.mask.show_mask import save_full_mask
from utils.data_process.show_prams import print_params


def validate_keypoints(keypoints, image_width, image_height):
    for kp in keypoints:
        x, y, v = kp
        if not (0 <= x < image_width and 0 <= y < image_height):
            raise ValueError(f"Key point ({x}, {y}) is out of bounds for image size ({image_width}, {image_height})")


"""
直接读取xanlabel标注的数据集json格式

"""


class LineDataset(BaseDataset):
    def __init__(self, dataset_path, data_type, transforms=None, augmentation=False, dataset_type=None, img_type='rgb',
                 target_type='pixel'):
        super().__init__(dataset_path)

        self.data_path = dataset_path
        self.data_type = data_type
        print(f'data_path:{dataset_path}')
        self.transforms = transforms
        self.img_path = os.path.join(dataset_path, "images/" + dataset_type)
        self.lbl_path = os.path.join(dataset_path, "labels/" + dataset_type)
        self.imgs = os.listdir(self.img_path)
        self.lbls = os.listdir(self.lbl_path)
        self.target_type = target_type
        self.img_type = img_type
        self.augmentation = augmentation
        print(f'augmentation:{augmentation}')
        # self.default_transform = DefaultTransform()

    def __getitem__(self, index) -> T_co:
        img_path = os.path.join(self.img_path, self.imgs[index])

        if self.data_type == 'tiff':
            lbl_path = os.path.join(self.lbl_path, self.imgs[index][:-4] + 'json')
            img = imageio.v3.imread(img_path)[:, :, 0]
            print(f'img shape:{img.shape}')
            w, h = img.shape[:2]
            img = img.reshape(w, h, 1)
            img_3channel = np.zeros((w, h, 3), dtype=img.dtype)
            img_3channel[:, :, 2] = img[:, :, 0]

            img = torch.from_numpy(img_3channel).permute(2, 1, 0)
        else:
            lbl_path = os.path.join(self.lbl_path, self.imgs[index][:-3] + 'json')
            img = PIL.Image.open(img_path).convert('RGB')
            w, h = img.size
        # wire_labels, target = self.read_target(item=index, lbl_path=lbl_path, shape=(h, w))
        target = self.read_target(item=index, lbl_path=lbl_path, shape=(h, w),image=img)

        self.transforms = get_transforms(augmention=self.augmentation)

        img, target = self.transforms(img, target)

        return img, target

    def __len__(self):
        return len(self.imgs)

    def read_target(self, item, lbl_path, shape,extra=None,image=None):
        # print(f'shape:{shape}')
        # print(f'lbl_path:{lbl_path}')
        with open(lbl_path, 'r') as file:
            lable_all = json.load(file)

        objs = lable_all["shapes"]
        point_pairs = objs[0]['points']

        # print(f'point_pairs:{point_pairs}')
        target = {}

        target["image_id"] = torch.tensor(item)

        #boxes, line_point_pairs, points, labels, mask_ends, mask_params
        boxes, lines, points, labels, arc_ends, arc_params = get_boxes_lines(objs, shape)

        # print_params(arc_ends, arc_params)

        if points is not None:
            target["points"] = points
        # if lines is not None:
        #     a = torch.full((lines.shape[0],), 2).unsqueeze(1)
        #     lines = torch.cat((lines, a), dim=1)
        #     target["lines"] = lines.to(torch.float32).view(-1, 2, 3)
        if lines is not None:
            label_3d = labels.view(-1, 1, 1).expand(-1, 2, -1)  # [N] -> [N,2,1]
            line1 = torch.cat([lines, label_3d], dim=-1)  # [N,2,3]
            target["lines"] = line1.to(torch.float32)

        if arc_ends is not None:
            target['mask_ends'] = arc_ends
        if arc_params is not None:
            target['mask_params'] = arc_params


            arc_angles = compute_arc_angles(arc_ends, arc_params)
            # print_params(arc_angles)
            arc_masks = []

            for i in range(len(arc_params)):
                arc_param_i = arc_params[i].view(-1)  # shape (5,)
                arc_angle_i = arc_angles[i].view(-1)  # shape (2,)
                arc7 = torch.cat([arc_param_i, arc_angle_i], dim=0)  # shape (7,)


                mask = arc_to_mask(arc7, shape, line_width=1)

                arc_masks.append(mask)

            print_params(arc_masks,image)

            target['circle_masks'] = torch.stack(arc_masks, dim=0)

            # for i, m in enumerate(target['circle_masks']):
            #     save_full_mask(
            #         m,
            #         name=f"arc_mask_{i}",
            #         out_dir=r"/home/zhaoyinghan/py_ws/code/circle_huayan/MultiVisionModels/models/line_detect/out_feature_dataset",
            #         save_png=True,
            #         save_npy=True,
            #         image=image,
            #         show_on_image=True
            #     )




        target["boxes"] = boxes
        target["labels"] = labels
        # target["boxes"], lines,target["points"], target["labels"] = get_boxes_lines(objs,shape)
        # print(f'lines:{lines}')
        # target["labels"] = torch.ones(len(target["boxes"]), dtype=torch.int64)
        # print(f'target points:{target["points"]}')

        # target["lines"] = lines.to(torch.float32).view(-1,2,3)

        # print(f'')

        # print(f'lines:{target["lines"].shape}')
        target["img_size"] = shape

        # validate_keypoints(lines, shape[0], shape[1])
        return target



    def show(self, idx, show_type='all'):
        image, target = self.__getitem__(idx)

        cmap = plt.get_cmap("jet")
        norm = mpl.colors.Normalize(vmin=0.4, vmax=1.0)
        sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
        sm.set_array([])

        # img_path = os.path.join(self.img_path, self.imgs[idx])
        # print(f'boxes:{target["boxes"]}')
        img = image

        if show_type == 'circle_masks':
            boxed_image = draw_bounding_boxes((img * 255).to(torch.uint8), target["boxes"],
                                              colors="yellow", width=1)
            # arc = target['arc']
            arc_mask = target['circle_masks']
            # print(f'taget circle:{arc.shape}')
            print(f'target circle_masks:{arc_mask.shape}')
            combined = torch.cat(list(arc_mask), dim=1)
            plt.imshow(combined)
            plt.show()

        if show_type == 'circle_masks11':
            boxed_image = draw_bounding_boxes((img * 255).to(torch.uint8), target["boxes"],
                                              colors="yellow", width=1)
            circle = target['circles']
            circle_mask = target['circle_masks']
            print(f'taget circle:{circle.shape}')
            print(f'target circle_masks:{circle_mask.shape}')
            plt.imshow(circle_mask.squeeze(0))
            keypoint_img = draw_keypoints(boxed_image, circle, colors='red', width=3)
            # plt.imshow(keypoint_img.permute(1, 2, 0).numpy())
            plt.show()

        # if show_type=='lines':
        #     keypoint_img=draw_keypoints((img * 255).to(torch.uint8),target['lines'],colors='red',width=3)
        #     plt.imshow(keypoint_img.permute(1, 2, 0).numpy())
        #     plt.show()

        if show_type == 'points':
            # print(f'points:{target['points'].shape}')
            keypoint_img = draw_keypoints((img * 255).to(torch.uint8), target['points'].unsqueeze(1), colors='red',
                                          width=3)
            plt.imshow(keypoint_img.permute(1, 2, 0).numpy())
            plt.show()

        if show_type == 'boxes':
            boxed_image = draw_bounding_boxes((img * 255).to(torch.uint8), target["boxes"],
                                              colors="yellow", width=1)
            plt.imshow(boxed_image.permute(1, 2, 0).numpy())
            plt.show()

    def show_img(self, img_path):
        pass


def draw_el(all):
    # 解析椭圆参数
    if isinstance(all, torch.Tensor):
        all = all.cpu().numpy()
    x, y, a, b, q, q1, q2 = all
    theta = np.radians(q)
    phi1 = np.radians(q1)  # 第一个点的参数角
    phi2 = np.radians(q2)  # 第二个点的参数角

    # 生成椭圆上的点
    phi = np.linspace(0, 2 * np.pi, 500)
    x_ellipse = x + a * np.cos(phi) * np.cos(theta) - b * np.sin(phi) * np.sin(theta)
    y_ellipse = y + a * np.cos(phi) * np.sin(theta) + b * np.sin(phi) * np.cos(theta)

    # 计算两个指定点的坐标
    def param_to_point(phi, xc, yc, a, b, theta):
        x = xc + a * np.cos(phi) * np.cos(theta) - b * np.sin(phi) * np.sin(theta)
        y = yc + a * np.cos(phi) * np.sin(theta) + b * np.sin(phi) * np.cos(theta)
        return x, y

    P1 = param_to_point(phi1, x, y, a, b, theta)
    P2 = param_to_point(phi2, x, y, a, b, theta)

    # 创建画布并显示背景图片（使用传入的background_img，shape为[H, W, C]）
    plt.figure(figsize=(10, 10))
    # plt.imshow(background_img)  # 直接显示背景图

    # 绘制椭圆及相关元素
    plt.plot(x_ellipse, y_ellipse, 'b-', linewidth=2)
    plt.plot(x, y, 'ko', markersize=8)
    plt.plot(P1[0], P1[1], 'ro', markersize=10)
    plt.plot(P2[0], P2[1], 'go', markersize=10)

    plt.show()


def arc_to_mask(arc7, shape, line_width=1):
    """
    Generate a binary mask of an elliptical arc.

    Args:
        xc, yc (float): 椭圆中心
        a, b (float): 长半轴、短半轴 (a >= b)
        theta (float): 椭圆旋转角度（**弧度**，逆时针，相对于 x 轴）
        phi1, phi2 (float): 起始和终止参数角（**弧度**，在 [0, 2π) 内）
        H, W (int): 输出 mask 的高度和宽度
        line_width (int): 弧线宽度（像素）

    Returns:
        mask (Tensor): [H, W], dtype=torch.uint8, 0/255
    """
    # print_params(arc7)
    # 确保 phi1 -> phi2 是正向（可处理跨 2π 的情况）
    if torch.all(arc7 == 0):
        return torch.zeros(shape, dtype=torch.uint8)
    print_params(arc7)

    xc, yc, a, b, theta, phi1, phi2 = arc7
    H, W = shape
    if phi2 < phi1:
        phi2 += 2 * np.pi

    # 生成参数角（足够密集，避免断线）
    num_points = max(int(200 * abs(phi2 - phi1) / (2 * np.pi)), 10)
    phi = np.linspace(phi1, phi2, num_points)

    # 椭圆参数方程（先在未旋转坐标系下计算）
    x_local = a * np.cos(phi)
    y_local = b * np.sin(phi)

    # 应用旋转和平移
    cos_t = np.cos(theta)
    sin_t = np.sin(theta)
    x_rot = x_local * cos_t - y_local * sin_t + xc
    y_rot = x_local * cos_t + y_local * sin_t + yc

    # 转为整数坐标（OpenCV 需要 int32）
    points = np.stack([x_rot, y_rot], axis=1).astype(np.int32)

    # 创建空白图像
    img = np.zeros((H, W), dtype=np.uint8)

    # 绘制折线（antialias=False 更适合 mask）
    cv2.polylines(img, [points], isClosed=False, color=255, thickness=line_width, lineType=cv2.LINE_AA)

    return torch.from_numpy(img).float()  # [H, W], values: 0 or 255


def compute_arc_angles(gt_mask_ends, gt_mask_params):
    """
    给定椭圆上的一个点，计算其对应的参数角 phi（弧度）。

    Parameters:
        point: tuple or array-like, (x, y)
        ellipse_param: tuple or array-like, (xc, yc, a, b, theta)

    Returns:
        phi: float, in [0, 2*pi)
    """
    # print_params(gt_mask_ends, gt_mask_params)
    results = []
    if not isinstance(gt_mask_params, torch.Tensor):
        gt_mask_params_tensor = torch.tensor(gt_mask_params, dtype=gt_mask_ends.dtype, device=gt_mask_ends.device)
    else:
        gt_mask_params_tensor = gt_mask_params.clone().detach().to(gt_mask_ends)

    for ends_img, params_img in zip(gt_mask_ends, gt_mask_params_tensor):
        # print(f'params_img:{params_img}')
        if torch.norm(params_img) < 1e-6:  # L2 norm near zero
            results.append(torch.zeros(2, device=params_img.device, dtype=params_img.dtype))
            continue
        x, y = ends_img
        xc, yc, a, b, theta = params_img

        # 1. 平移到中心
        dx = x - xc
        dy = y - yc

        # 2. 逆旋转（旋转 -theta）
        cos_t = torch.cos(theta)
        sin_t = torch.sin(theta)
        X = dx * cos_t + dy * sin_t
        Y = -dx * sin_t + dy * cos_t

        # 3. 归一化到单位圆（除以 a, b）
        cos_phi = X / a
        sin_phi = Y / b

        # 4. 用 atan2 求角度（自动处理象限）
        phi = torch.atan2(sin_phi, cos_phi)

        # 5. 转换到 [0, 2π)
        phi = torch.where(phi < 0, phi + 2 * torch.pi, phi)

        results.append(phi)
    return results


def points_to_ellipse(points):
    """
    根据提供的四个点估计椭圆参数。

    :param points: Tensor of shape (4, 2) 表示椭圆上的四个点
    :return: 返回 (cx, cy, r1, r2, orientation) 其中 cx, cy 是中心坐标，r1, r2 分别是长轴和短轴半径，orientation 是椭圆的方向（弧度）
    """
    # 转换为numpy数组进行计算
    pts = points.numpy()
    pts = pts.reshape(-1, 2)

    center = np.mean(pts, axis=0)

    A = np.hstack(
        [pts[:, 0:1] ** 2, pts[:, 0:1] * pts[:, 1:2], pts[:, 1:2] ** 2, pts[:, :2], np.ones((pts.shape[0], 1))])
    b = np.ones(pts.shape[0])
    x = np.linalg.lstsq(A, b, rcond=None)[0]

    # 解析解参见 https://en.wikipedia.org/wiki/Ellipse#General_ellipse
    a, b, c, d, f, g = x.ravel()
    numerator = 2 * (a * f * f + c * d * d + g * b * b - 2 * b * d * f - a * c * g)
    denominator1 = (b * b - a * c) * ((c - a) * np.sqrt(1 + 4 * b * b / ((a - c) * (a - c))) - (c + a))
    denominator2 = (b * b - a * c) * ((a - c) * np.sqrt(1 + 4 * b * b / ((a - c) * (a - c))) - (c + a))
    major_axis = np.sqrt(numerator / denominator1)
    minor_axis = np.sqrt(numerator / denominator2)

    distances = np.linalg.norm(pts - center, axis=1)
    long_axis_length = np.max(distances) * 2
    short_axis_length = np.min(distances) * 2

    orientation = np.arctan2(pts[1, 1] - pts[0, 1], pts[1, 0] - pts[0, 0])

    return center[0], center[1], long_axis_length / 2, short_axis_length / 2, orientation


def generate_ellipse_mask(shape, ellipse_params):
    """
    在指定形状的图像上生成椭圆mask。

    :param shape: 输出mask的形状 (HxW)
    :param ellipse_params: 椭圆参数 (cx, cy, rx, ry, orientation)
    :return: 椭圆mask
    """
    cx, cy, rx, ry, orientation = ellipse_params
    img = np.zeros(shape, dtype=np.uint8)
    cx, cy, rx, ry = int(cx), int(cy), int(rx), int(ry)

    rr, cc = ellipse(cy, cx, ry, rx, shape)
    img[rr, cc] = 1

    return img


def sort_points_clockwise(points):
    points = np.array(points)

    top_left_idx = np.lexsort((points[:, 0], points[:, 1]))[0]
    reference_point = points[top_left_idx]

    def angle_to_reference(point):
        return np.arctan2(point[1] - reference_point[1], point[0] - reference_point[0])

    angles = np.apply_along_axis(angle_to_reference, 1, points)

    angles[angles < 0] += 2 * np.pi

    sorted_indices = np.argsort(angles)
    sorted_points = points[sorted_indices]

    return sorted_points.tolist()


def get_boxes_lines(objs, shape):
    boxes = []
    labels = []
    h, w = shape

    line_point_pairs = []
    points = []
    mask_ends = []
    mask_params = []

    for obj in objs:
        # plt.plot([a[1], b[1]], [a[0], b[0]], c="red", linewidth=1)  # a[1], b[1]无明确大小

        # print(f"points:{obj['points']}")
        label = obj['label']
        if label == 'line' or label == 'dseam1':
            a, b = obj['points'][0], obj['points'][1]

            # line_point_pairs.append(a)
            # line_point_pairs.append(b)
            line_point_pairs.append([a, b])

            xmin = max(0, (min(a[0], b[0]) - 6))
            xmax = min(w, (max(a[0], b[0]) + 6))
            ymin = max(0, (min(a[1], b[1]) - 6))
            ymax = min(h, (max(a[1], b[1]) + 6))

            boxes.append([xmin, ymin, xmax, ymax])
            labels.append(torch.tensor(2))

            points.append(torch.tensor([0.0]))
            mask_ends.append([[0, 0], [0, 0]])
            mask_params.append([0, 0, 0, 0, 0])
            # circle_4points.append([[0, 0], [0, 0], [0, 0], [0, 0]])



        elif label == 'point':
            p = obj['points'][0]
            xmin = max(0, p[0] - 12)
            xmax = min(w, p[0] + 12)
            ymin = max(0, p[1] - 12)
            ymax = min(h, p[1] + 12)

            points.append(p)
            labels.append(torch.tensor(1))
            boxes.append([xmin, ymin, xmax, ymax])

            line_point_pairs.append([[0, 0], [0, 0]])
            mask_ends.append([[0, 0], [0, 0]])
            mask_params.append([0, 0, 0, 0, 0])
            # circle_4points.append([[0, 0], [0, 0], [0, 0], [0, 0]])


        # elif label == 'arc':
        #     arc_points = obj['points']
        #     arc_params = obj['params']
        #     arc_ends = obj['ends']
        #     line_mask.append(arc_points)
        #     mask_ends.append(arc_ends)
        #     mask_params.append(arc_params)
        #
        #     xs = [p[0] for p in arc_points]
        #     ys = [p[1] for p in arc_points]
        #     xmin, xmax = min(xs), max(xs)
        #     ymin, ymax = min(ys), max(ys)
        #
        #     boxes.append([xmin, ymin, xmax, ymax])
        #     labels.append(torch.tensor(3))
        #
        #     points.append(torch.tensor([0.0]))
        #     line_point_pairs.append([[0, 0], [0, 0]])
        #     circle_4points.append([[0, 0], [0, 0], [0, 0], [0, 0]])

        elif label == 'arc':

            arc_params = obj['params']
            arc_ends = obj['ends']
            mask_ends.append(arc_ends)
            mask_params.append(arc_params)
            arc3points = obj['points']
            xs = [p[0] for p in arc3points]
            ys = [p[1] for p in arc3points]
            xmin_raw = min(xs)
            xmax_raw = max(xs)
            ymin_raw = min(ys)
            ymax_raw = max(ys)
            xmin = max(xmin_raw - 40, 0)
            xmax = min(xmax_raw + 40, w)
            ymin = max(ymin_raw - 40, 0)
            ymax = min(ymax_raw + 40, h)

            boxes.append([xmin, ymin, xmax, ymax])
            labels.append(torch.tensor(4))
            points.append(torch.tensor([0.0]))
            line_point_pairs.append([[0, 0], [0, 0]])


    boxes = torch.tensor(boxes, dtype=torch.float32)
    print(f'boxes:{boxes.shape}')
    labels = torch.tensor(labels)

    if points:
        points = torch.tensor(points, dtype=torch.float32)
    else:
        points = None

    if line_point_pairs:
        line_point_pairs = torch.tensor(line_point_pairs, dtype=torch.float32)
    else:
        line_point_pairs = None

    if mask_ends:
        mask_ends = torch.tensor(mask_ends, dtype=torch.float32)
    else:
        mask_ends = None

    if mask_params:
        mask_params = torch.tensor(mask_params, dtype=torch.float32)
    else:
        mask_params = None






    return boxes, line_point_pairs, points, labels, mask_ends, mask_params


if __name__ == '__main__':
    path = r'/data/share/zyh/master_dataset/pokou/251115/a_dataset_pokou_mask'
    dataset = LineDataset(dataset_path=path, dataset_type='train', augmentation=False, data_type='jpg')
    dataset.show(9, show_type='circle_masks')