lcnn/models/line_detect/predict_0226.py

import time

import skimage

# from models.line_detect.postprocess import show_predict, show_box, show_box_or_line, show_box_and_line, \
#     show_line_optimized, show_line, show_all
import os

import torch
from PIL import Image
import matplotlib.pyplot as plt
import matplotlib as mpl
import numpy as np
from models.line_detect.line_net import linenet_resnet50_fpn
from torchvision import transforms

# from models.wirenet.postprocess import postprocess
from models.wirenet.postprocess import postprocess
from rtree import index

from datetime import datetime

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')


def load_best_model(model, save_path, device):
    if os.path.exists(save_path):
        checkpoint = torch.load(save_path, map_location=device)
        model.load_state_dict(checkpoint['model_state_dict'])
        # if optimizer is not None:
        #     optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
        epoch = checkpoint['epoch']
        loss = checkpoint['loss']
        print(f"Loaded best model from {save_path} at epoch {epoch} with loss {loss:.4f}")
    else:
        print(f"No saved model found at {save_path}")
    return model


def box_line_(imgs, pred, length=False):  # 默认置信度
    im = imgs.permute(1, 2, 0).cpu().numpy()
    line_data = pred[-1]['wires']['lines'][0].cpu().numpy() / 128 * 512
    line_scores = pred[-1]['wires']['score'].cpu().numpy()[0]

    diag = (im.shape[0] ** 2 + im.shape[1] ** 2) ** 0.5
    line, score = postprocess(line_data, line_scores, diag * 0.01, 0, False)
    for idx, box_ in enumerate(pred[0:-1]):
        box = box_['boxes']  # 是一个tensor
        # line = pred[-1]['wires']['lines'][idx].cpu().numpy() / 128 * 512
        # score = pred[-1]['wires']['score'][idx]

        # diag = (512 ** 2 + 512 ** 2) ** 0.5
        # line, score = postprocess(line, score, diag * 0.01, 0, False)

        line_ = []
        score_ = []

        for i in box:
            score_max = 0.0
            tmp = [[0.0, 0.0], [0.0, 0.0]]

            for j in range(len(line)):
                if (line[j][0][1] >= i[0] and line[j][1][1] >= i[0] and
                        line[j][0][1] <= i[2] and line[j][1][1] <= i[2] and
                        line[j][0][0] >= i[1] and line[j][1][0] >= i[1] and
                        line[j][0][0] <= i[3] and line[j][1][0] <= i[3]):

                    # # 计算线段长度
                    # length = np.linalg.norm(line[j][0] - line[j][1])
                    # if length > score_max:
                    #     tmp = line[j]
                    #     score_max = score[j]

                    if score[j] > score_max:
                        tmp = line[j]
                        score_max = score[j]
            line_.append(tmp)
            score_.append(score_max)
        processed_list = torch.tensor(line_)
        pred[idx]['line'] = processed_list

        processed_s_list = torch.tensor(score_)
        pred[idx]['line_score'] = processed_s_list
    return pred


def box_line_optimized(pred):
    # 创建R-tree索引
    idx = index.Index()

    # 将所有线段添加到R-tree中
    lines = pred[-1]['wires']['lines']  # 形状为[1, 2500, 2, 2]
    scores = pred[-1]['wires']['score'][0]  # 假设形状为[2500]

    # 提取并处理所有线段
    for idx_line in range(lines.shape[1]):  # 遍历2500条线段
        line_tensor = lines[0, idx_line].cpu().numpy() / 128 * 512  # 转换为numpy数组并调整比例
        x_min = float(min(line_tensor[0][0], line_tensor[1][0]))
        y_min = float(min(line_tensor[0][1], line_tensor[1][1]))
        x_max = float(max(line_tensor[0][0], line_tensor[1][0]))
        y_max = float(max(line_tensor[0][1], line_tensor[1][1]))
        idx.insert(idx_line, (max(0, x_min - 256), max(0, y_min - 256), min(512, x_max + 256), min(512, y_max + 256)))

    for idx_box, box_ in enumerate(pred[0:-1]):
        box = box_['boxes'].cpu().numpy()  # 确保将张量转换为numpy数组
        line_ = []
        score_ = []

        for i in box:
            score_max = 0.0
            tmp = [[0.0, 0.0], [0.0, 0.0]]

            # 获取与当前box可能相交的所有线段
            possible_matches = list(idx.intersection((i[0], i[1], i[2], i[3])))

            for j in possible_matches:
                line_j = lines[0, j].cpu().numpy() / 128 * 512
                if (line_j[0][1] >= i[0] and line_j[1][1] >= i[0] and  # 注意这里交换了x和y
                        line_j[0][1] <= i[2] and line_j[1][1] <= i[2] and
                        line_j[0][0] >= i[1] and line_j[1][0] >= i[1] and
                        line_j[0][0] <= i[3] and line_j[1][0] <= i[3]):

                    if scores[j] > score_max:
                        tmp = line_j
                        score_max = scores[j]

            line_.append(tmp)
            score_.append(score_max)

        processed_list = torch.tensor(line_)
        pred[idx_box]['line'] = processed_list

        processed_s_list = torch.tensor(score_)
        pred[idx_box]['line_score'] = processed_s_list

    return pred


def set_thresholds(threshold):
    if isinstance(threshold, list):
        if len(threshold) != 2:
            raise ValueError("Threshold list must contain exactly two elements.")
        a, b = threshold
    elif isinstance(threshold, (int, float)):
        a = b = threshold
    else:
        raise TypeError("Threshold must be either a list of two numbers or a single number.")

    return a, b

def color():
    return  [
        '#1f77b4', '#aec7e8', '#ff7f0e', '#ffbb78', '#2ca02c',
        '#98df8a', '#d62728', '#ff9896', '#9467bd', '#c5b0d5',
        '#8c564b', '#c49c94', '#e377c2', '#f7b6d2', '#7f7f7f',
        '#c7c7c7', '#bcbd22', '#dbdb8d', '#17becf', '#9edae5',
        '#8dd3c7', '#ffffb3', '#bebada', '#fb8072', '#80b1d3',
        '#fdb462', '#b3de69', '#fccde5', '#bc80bd', '#ccebc5',
        '#ffed6f', '#8da0cb', '#e78ac3', '#e5c494', '#b3b3b3',
        '#fdbf6f', '#ff7f00', '#cab2d6', '#637939', '#b5cf6b',
        '#cedb9c', '#8c6d31', '#e7969c', '#d6616b', '#7b4173',
        '#ad494a', '#843c39', '#dd8452', '#f7f7f7', '#cccccc',
        '#969696', '#525252', '#f7fcfd', '#e5f5f9', '#ccece6',
        '#99d8c9', '#66c2a4', '#2ca25f', '#008d4c', '#005a32',
        '#f7fcf0', '#e0f3db', '#ccebc5', '#a8ddb5', '#7bccc4',
        '#4eb3d3', '#2b8cbe', '#08589e', '#f7fcfd', '#e0ecf4',
        '#bfd3e6', '#9ebcda', '#8c96c6', '#8c6bb4', '#88419d',
        '#810f7c', '#4d004b', '#f7f7f7', '#efefef', '#d9d9d9',
        '#bfbfbf', '#969696', '#737373', '#525252', '#252525',
        '#000000', '#ffffff', '#ffeda0', '#fed976', '#feb24c',
        '#fd8d3c', '#fc4e2a', '#e31a1c', '#bd0026', '#800026',
        '#ff6f61', '#ff9e64', '#ff6347', '#ffa07a', '#fa8072'
    ]

def show_all(imgs, pred, threshold, save_path, show):
    col = color()
    box_th, line_th = set_thresholds(threshold)
    im = imgs.permute(1, 2, 0)

    boxes = pred[0]['boxes'].cpu().numpy()
    box_scores = pred[0]['scores'].cpu().numpy()
    line = pred[-1]['wires']['lines'][0].cpu().numpy() / 128 * 512
    line_score = pred[-1]['wires']['score'].cpu().numpy()[0]

    diag = (im.shape[0] ** 2 + im.shape[1] ** 2) ** 0.5
    line, line_score = postprocess(line, line_score, diag * 0.01, 0, False)

    fig, axs = plt.subplots(1, 3, figsize=(10, 10))

    axs[0].imshow(np.array(im))
    for idx, box in enumerate(boxes):
        if box_scores[idx] < box_th:
            continue
        x0, y0, x1, y1 = box
        axs[0].add_patch(
            plt.Rectangle((x0, y0), x1 - x0, y1 - y0, fill=False, edgecolor=col[idx], linewidth=1))
    axs[0].set_title('Boxes')

    axs[1].imshow(np.array(im))
    for idx, (a, b) in enumerate(line):
        if line_score[idx] < line_th:
            continue
        axs[1].scatter(a[1], a[0], c='#871F78', s=2)
        axs[1].scatter(b[1], b[0], c='#871F78', s=2)
        axs[1].plot([a[1], b[1]], [a[0], b[0]], c='red', linewidth=1)
    axs[1].set_title('Lines')

    axs[2].imshow(np.array(im))
    lines = pred[0]['line'].cpu().numpy()
    line_scores = pred[0]['line_score'].cpu().numpy()
    idx = 0
    tmp = np.array([[0.0, 0.0], [0.0, 0.0]])
    for box, line, box_score, line_score in zip(boxes, lines, box_scores, line_scores):
        x0, y0, x1, y1 = box
        # 框中无线的跳过
        if np.array_equal(line, tmp):
            continue
        a, b = line
        if box_score >= 0.7 or line_score >= 0.9:
            axs[2].add_patch(
                plt.Rectangle((x0, y0), x1 - x0, y1 - y0, fill=False, edgecolor=col[idx], linewidth=1))
            axs[2].scatter(a[1], a[0], c='#871F78', s=10)
            axs[2].scatter(b[1], b[0], c='#871F78', s=10)
            axs[2].plot([a[1], b[1]], [a[0], b[0]], c=col[idx], linewidth=1)
            idx = idx + 1
    axs[2].set_title('Boxes and Lines')

    if save_path:
        save_path = os.path.join(datetime.now().strftime("%Y%m%d_%H%M%S"), 'box_line.png')
        os.makedirs(os.path.dirname(save_path), exist_ok=True)

        plt.savefig(save_path)
        print(f"Saved result image to {save_path}")

    if show:
        # 调整子图之间的距离，防止标题和标签重叠
        plt.tight_layout()
        plt.show()

def show_box_or_line(imgs, pred, threshold, save_path = None, show_line=False, show_box=False):
    col = color()
    box_th, line_th = set_thresholds(threshold)
    im = imgs.permute(1, 2, 0)

    boxes = pred[0]['boxes'].cpu().numpy()
    box_scores = pred[0]['scores'].cpu().numpy()
    line = pred[-1]['wires']['lines'][0].cpu().numpy() / 128 * 512
    line_score = pred[-1]['wires']['score'].cpu().numpy()[0]

    # 可视化预测结
    fig, ax = plt.subplots(figsize=(10, 10))
    ax.imshow(np.array(im))

    if show_box:
        for idx, box in enumerate(boxes):
            if box_scores[idx] < box_th:
                continue
            x0, y0, x1, y1 = box
            ax.add_patch(
                plt.Rectangle((x0, y0), x1 - x0, y1 - y0, fill=False, edgecolor=col[idx], linewidth=1))
        if save_path:
            save_path = os.path.join(datetime.now().strftime("%Y%m%d_%H%M%S"), 'box.png')
            os.makedirs(os.path.dirname(save_path), exist_ok=True)

            plt.savefig(save_path)
            print(f"Saved result image to {save_path}")


    if show_line:
        for idx, (a, b) in enumerate(line):
            if line_score[idx] < line_th:
                continue
            ax.scatter(a[1], a[0], c='#871F78', s=2)
            ax.scatter(b[1], b[0], c='#871F78', s=2)
            ax.plot([a[1], b[1]], [a[0], b[0]], c='red', linewidth=1)
        if save_path:
            save_path = os.path.join(datetime.now().strftime("%Y%m%d_%H%M%S"), 'line.png')
            os.makedirs(os.path.dirname(save_path), exist_ok=True)

            plt.savefig(save_path)
            print(f"Saved result image to {save_path}")


    plt.show()


def show_predict(imgs, pred, threshold, t_start):
    col = color()
    box_th, line_th = set_thresholds(threshold)
    im = imgs.permute(1, 2, 0)  # 处理为 [512, 512, 3]
    boxes = pred[0]['boxes'].cpu().numpy()
    box_scores = pred[0]['scores'].cpu().numpy()
    lines = pred[0]['line'].cpu().numpy()
    line_scores = pred[0]['line_score'].cpu().numpy()

    # 可视化预测结
    fig, ax = plt.subplots(figsize=(10, 10))
    ax.imshow(np.array(im))
    idx = 0

    tmp = np.array([[0.0, 0.0], [0.0, 0.0]])

    for box, line, box_score, line_score in zip(boxes, lines, box_scores, line_scores):
        x0, y0, x1, y1 = box
        # 框中无线的跳过
        if np.array_equal(line, tmp):
            continue
        a, b = line
        if box_score >= box_th or line_score >= line_th:
            ax.add_patch(
                plt.Rectangle((x0, y0), x1 - x0, y1 - y0, fill=False, edgecolor=col[idx], linewidth=1))
            ax.scatter(a[1], a[0], c='#871F78', s=10)
            ax.scatter(b[1], b[0], c='#871F78', s=10)
            ax.plot([a[1], b[1]], [a[0], b[0]], c=col[idx], linewidth=1)
            idx = idx + 1
    t_end = time.time()
    print(f'predict used:{t_end - t_start}')

    plt.show()


def predict(pt_path, model, img, type=0, threshold=0.5, save_path=None, show=False):
    model = load_best_model(model, pt_path, device)

    model.eval()

    if isinstance(img, str):
        img = Image.open(img).convert("RGB")

    transform = transforms.ToTensor()
    img_tensor = transform(img)  # [3, 512, 512]

    # 将图像调整为512x512大小
    t_start = time.time()
    im = img_tensor.permute(1, 2, 0)  # [512, 512, 3]
    im_resized = skimage.transform.resize(im.cpu().numpy().astype(np.float32), (512, 512))  # (512, 512, 3)
    img_ = torch.tensor(im_resized).permute(2, 0, 1)
    t_end = time.time()
    print(f'switch img used:{t_end - t_start}')

    with torch.no_grad():
        predictions = model([img_.to(device)])
        # print(predictions)

    t_start = time.time()
    pred = box_line_(img_, predictions)  # 线框匹配
    t_end = time.time()
    print(f'Matched boxes and lines used: {t_end - t_start:.4f} seconds')

    if type == 0:
        show_all(img_, pred, threshold, save_path=True, show=True)
    elif type == 1:
        show_box_or_line(img_, predictions, threshold, save_path=True, show_line=True)  # 参数确定画什么
    elif type == 2:
        show_box_or_line(img_, predictions, threshold, save_path=True, show_box=True)  # 参数确定画什么
    elif type == 3:
        show_predict(img_, pred, threshold, t_start)


if __name__ == '__main__':
    t_start = time.time()
    print(f'start to predict:{t_start}')
    model = linenet_resnet50_fpn().to(device)
    pt_path = r'D:\python\PycharmProjects\20250214\weight\best.pth'
    img_path = r'C:\Users\m2337\Desktop\p\20250226142919.png'
    # predict(pt_path, model, img_path)

    predict(pt_path, model, img_path, type=2, threshold=0.5, save_path=None, show=False)

    t_end = time.time()
    print(f'predict used:{t_end - t_start}')