pokouqiege/models/line_detect/predict.py

# import time
# import torch
# from PIL import Image
# from torchvision import transforms
# from skimage.transform import resize

import time

import cv2
import skimage

import os

import torch
from PIL import Image
import matplotlib.pyplot as plt
import numpy as np

from torchvision import transforms

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 box_line_(imgs, pred):  # 默认置信度
    im = imgs.permute(1, 2, 0).cpu().numpy()
    lines = pred[-1]['wires']['lines'][0].cpu().numpy() / 128 * np.array([2000, 2000])
    scores = pred[-1]['wires']['score'].cpu().numpy()[0]

    # print(f'111:{len(lines)}')
    for i in range(1, len(lines)):
        if (lines[i] == lines[0]).all():
            lines = lines[:i]
            scores = scores[:i]
            break
    diag = (im.shape[0] ** 2 + im.shape[1] ** 2) ** 0.5
    line, score = postprocess(lines, scores, diag * 0.01, 0, False)
    # print(f'333:{len(lines)}')
    for idx, box_ in enumerate(pred[0:-2]):
        box = box_['boxes']  # 是一个tensor

        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]):

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

        processed_s_list = torch.tensor(score_)
        pred[idx]['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):
    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()
    # lines = pred[-1]['wires']['lines'][0].cpu().numpy() / 128 * 512
    lines = pred[-1]['wires']['lines'][0].cpu().numpy() / 128 * np.array([2000, 2000])
    scores = pred[-1]['wires']['score'].cpu().numpy()[0]

    for i in range(1, len(lines)):
        if (lines[i] == lines[0]).all():
            lines = lines[:i]
            scores = scores[:i]
            break
    diag = (im.shape[0] ** 2 + im.shape[1] ** 2) ** 0.5
    line, line_score = postprocess(lines, scores, 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)

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

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

        for i in range(1, len(lines)):
            if (lines[i] == lines[0]).all():
                lines = lines[:i]
                scores = scores[:i]
                break
        diag = (im.shape[0] ** 2 + im.shape[1] ** 2) ** 0.5
        line, line_score = postprocess(lines, scores, diag * 0.01, 0, False)

        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()
    scores = pred[0]['line_score'].cpu().numpy()

    for i in range(1, len(lines)):
        if (lines[i] == lines[0]).all():
            lines = lines[:i]
            scores = scores[:i]
            break
    diag = (im.shape[0] ** 2 + im.shape[1] ** 2) ** 0.5
    line1, line_score1 = postprocess(lines, scores, diag * 0.01, 0, False)

    # 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, line1, box_scores, line_score1):
        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()


class Predict:
    def __init__(self, model, img, type=0, threshold=0.5, save_path=None, show_line=False, show_box=False):
        """
        初始化预测器。

        参数：
            pt_path: 模型权重文件路径。
            model: 模型定义（未加载权重）。
            img: 输入图像（路径或 PIL 图像对象）。
            type: 预测类型（0: 全部显示,线图、框图、线框匹配图，1: 显示线图，2: 显示框图，3: 线框匹配图）。
            threshold: 阈值，用于过滤预测结果。
            save_path: 保存结果的路径（可选）。
            show: 是否显示结果。
            device: 运行设备（默认 'cuda'）。
        """
        # self.device = torch.device(device if torch.cuda.is_available() else 'cpu')

        self.model = model
        self.device = next(model.parameters()).device
        # self.pt_path = pt_path
        self.img = self.load_image(img)
        self.type = type
        self.threshold = threshold
        self.save_path = save_path
        self.show_line = show_line
        self.show_box = show_box

    def load_best_model(self, 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 load_image(self, img):
        """加载图像"""
        if isinstance(img, str):
            img = Image.open(img).convert("RGB")
        return img

    def preprocess_image(self, img):
        """预处理图像"""
        transform = transforms.ToTensor()
        img_tensor = transform(img)  # [3, H, W]

        # 调整大小为 512x512
        t_start = time.time()
        im = img_tensor.permute(1, 2, 0)  # [H, W, 3]
        # im_resized = skimage.transform.resize(im.cpu().numpy().astype(np.float32), (512, 512))  # (512, 512, 3)
        if im.shape != (512, 512, 3):
            im_resized = cv2.resize(im.cpu().numpy().astype(np.float32), (512, 512), interpolation=cv2.INTER_LINEAR)
        img_ = torch.tensor(im).permute(2, 0, 1)  # [3, 512, 512]
        t_end = time.time()
        print(f"Image preprocessing used: {t_end - t_start:.4f} seconds")

        return img_

    def predict(self):
        """执行预测"""
        # model = self.load_best_model(self.model, self.pt_path, device)
        #
        # model.eval()

        # 预处理图像
        img_ = self.preprocess_image(self.img)

        # 模型推理
        with torch.no_grad():
            predictions =self.model([img_.to(self.device)])
            print("Model predictions completed.")

        # 后处理
        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")

        # 根据类型显示或保存结果
        if self.type == 0:
            show_all(img_, pred, self.threshold, save_path=self.save_path)
        elif self.type == 1:
            show_box_or_line(img_, predictions, self.threshold, save_path=self.save_path, show_line=True)
        elif self.type == 2:
            show_box_or_line(img_, predictions, self.threshold, save_path=self.save_path, show_box=True)
        elif self.type == 3:
            show_predict(img_, pred, self.threshold, t_start)

    def run(self):
        """运行预测流程"""
        self.predict()

class Predict1:
    def __init__(self, model, img, type=0, threshold=0.5, save_path=None, show_line=False, show_box=False):
        """
        初始化预测器。

        参数：
            pt_path: 模型权重文件路径。
            model: 模型定义（未加载权重）。
            img: 输入图像（路径或 PIL 图像对象）。
            type: 预测类型（0: 全部显示,线图、框图、线框匹配图，1: 显示线图，2: 显示框图，3: 线框匹配图）。
            threshold: 阈值，用于过滤预测结果。
            save_path: 保存结果的路径（可选）。
            show: 是否显示结果。
            device: 运行设备（默认 'cuda'）。
        """
        self.device = torch.device(device if torch.cuda.is_available() else 'cpu')
        self.model = model
        self.img = self.load_image(img)
        self.type = type
        self.threshold = threshold
        self.save_path = save_path
        self.show_line = show_line
        self.show_box = show_box

    def load_best_model(self, 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 load_image(self, img):
        """加载图像"""
        if isinstance(img, str):
            img = Image.open(img).convert("RGB")
        return img

    def preprocess_image(self, img):
        """预处理图像"""
        transform = transforms.ToTensor()
        img_tensor = transform(img)  # [3, H, W]

        # 调整大小为 512x512
        t_start = time.time()
        im = img_tensor.permute(1, 2, 0)  # [H, W, 3]
        # im_resized = skimage.transform.resize(im.cpu().numpy().astype(np.float32), (512, 512))  # (512, 512, 3)
        if im.shape != (512, 512, 3):
            im_resized = cv2.resize(im.cpu().numpy().astype(np.float32), (512, 512), interpolation=cv2.INTER_LINEAR)
        img_ = torch.tensor(im).permute(2, 0, 1)  # [3, 512, 512]
        t_end = time.time()
        print(f"Image preprocessing used: {t_end - t_start:.4f} seconds")

        return img_

    def predict(self):
        """执行预测"""
        # model = self.load_best_model(self.model, self.pt_path, device)
        model = self.model

        model.eval()

        # 预处理图像
        img_ = self.preprocess_image(self.img)

        # 模型推理
        with torch.no_grad():
            predictions = model([img_.to(self.device)])
            print("Model predictions completed.")


        # 根据类型显示或保存结果
        if self.type == 0:
            # 后处理
            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")
            show_all(img_, pred, self.threshold, save_path=self.save_path)
        elif self.type == 1:
            show_box_or_line(img_, predictions, self.threshold, save_path=self.save_path, show_line=True)
        elif self.type == 2:
            show_box_or_line(img_, predictions, self.threshold, save_path=self.save_path, show_box=True)
        elif self.type == 3:
            # 后处理
            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")
            show_predict(img_, pred, self.threshold, t_start)

    def run(self):
        """运行预测流程"""
        self.predict()