lcnn/models/line_detect/predict.py

import os

import skimage
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

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


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([])


def c(x):
    return sm.to_rgba(x)


def imshow(im):
    plt.close()
    plt.tight_layout()
    plt.imshow(im)
    plt.colorbar(sm, fraction=0.046)
    plt.xlim([0, im.shape[0]])
    plt.ylim([im.shape[0], 0])


def show_line(img, pred):
    im = img.permute(1, 2, 0)

    # 创建图形和坐标轴
    fig, ax = plt.subplots(figsize=(10, 10))
    # 绘制原始图像
    ax.imshow(np.array(im))
    # 绘制边界框
    boxes = pred[0]['boxes'].cpu().numpy()
    boxes_scores = pred[0]['scores'].cpu().numpy()

    for b, s in zip(boxes, boxes_scores):
        # print(f'box:{b}, box_score:{s}')
        if s < 0.7:
            continue
        x0, y0, x1, y1 = b
        rect = plt.Rectangle((x0, y0), x1 - x0, y1 - y0, fill=False, edgecolor='yellow', linewidth=1)
        ax.add_patch(rect)  # 将矩形添加到 Axes 对象上

    PLTOPTS = {"color": "#33FFFF", "s": 15, "edgecolors": "none", "zorder": 5}
    H = pred[-1]['wires']
    lines = H["lines"][0].cpu().numpy() / 128 * im.shape[:2]
    scores = H["score"][0].cpu().numpy()
    for i in range(1, len(lines)):
        if (lines[i] == lines[0]).all():
            lines = lines[:i]
            scores = scores[:i]
            break

    # 后处理线条以去除重叠的线条
    diag = (im.shape[0] ** 2 + im.shape[1] ** 2) ** 0.5
    nlines, nscores = postprocess(lines, scores, diag * 0.01, 0, False)

    # 根据分数绘制线条
    for i, t in enumerate([0.9]):
        for (a, b), s in zip(nlines, nscores):
            if s < t:
                continue
            ax.plot([a[1], b[1]], [a[0], b[0]], c=c(s), linewidth=2, zorder=s)  # 在 Axes 上绘制线条
            ax.scatter(a[1], a[0], **PLTOPTS)  # 在 Axes 上绘制散点
            ax.scatter(b[1], b[0], **PLTOPTS)  # 在 Axes 上绘制散点

    # 隐藏坐标轴
    ax.set_axis_off()
    plt.subplots_adjust(top=1, bottom=0, right=1, left=0, hspace=0, wspace=0)
    plt.margins(0, 0)
    ax.xaxis.set_major_locator(plt.NullLocator())
    ax.yaxis.set_major_locator(plt.NullLocator())

    # 显示图像
    plt.show()


def predict(pt_path, model, img):
    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)

    # 将图像调整为512x512大小
    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_tensor = torch.tensor(im_resized).permute(2, 0, 1)

    with torch.no_grad():
        predictions = model([img_tensor])
        # print(predictions[0])

    show_line(img_tensor, predictions)


if __name__ == '__main__':
    model = linenet_resnet50_fpn()
    pt_path = r'D:\python\PycharmProjects\20250214\weight\resnet50_best_e100.pth'
    # img_path = f'D:\python\PycharmProjects\data2\images/train/2024-11-27-15-41-38_SaveImage.png'  # 工件图
    # img_path = f'D:\python\PycharmProjects\data\images/train/00558656_3.png'  # wireframe图
    img_path = r'C:\Users\m2337\Desktop\p\24.jpg'
    predict(pt_path, model, img_path)