UNet_UAE_for_Lane_Detection/unet.py

import colorsys
import copy

import cv2
import numpy as np
import torch
import torch.nn.functional as F
from PIL import Image
from torch import nn

from nets.unet import Unet as unet
from utils.utils import cvtColor, preprocess_input, resize_image


class Unet(object):
    _defaults = {
        "model_path": None,
        "num_classes": 2,
        "backbone": "vgg",
        "input_shape": [1696, 864],
        "mix_type": 1,
        "cuda": True,
    }

    def __init__(self, _defaults,**kwargs):
        self._defaults = _defaults
        self.__dict__.update(self._defaults)
        for name, value in kwargs.items():
            setattr(self, name, value)

        if self.num_classes <= 2:
            self.colors = [(0, 0, 0), (255,255,255)]
        else:
            hsv_tuples = [(x / self.num_classes, 1., 1.) for x in range(self.num_classes)]
            self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
            self.colors = list(map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)), self.colors))

        self.generate()


    def generate(self, onnx=False):
        self.net = unet(num_classes=self.num_classes, backbone=self.backbone)

        device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
        self.net.load_state_dict(torch.load(self.model_path, map_location=device))
        self.net = self.net.eval()
        print('{} model, and classes loaded.'.format(self.model_path))
        if not onnx:
            if self.cuda:
                self.net = nn.DataParallel(self.net)
                self.net = self.net.cuda()

    def detect_image(self, image, count=False, name_classes=None):

        image = cvtColor(image)

        old_img = copy.deepcopy(image)
        orininal_h = np.array(image).shape[0]
        orininal_w = np.array(image).shape[1]

        image_data, nw, nh = resize_image(image, (self.input_shape[1], self.input_shape[0]))

        image_data = np.expand_dims(np.transpose(preprocess_input(np.array(image_data, np.float32)), (2, 0, 1)), 0)

        with torch.no_grad():
            images = torch.from_numpy(image_data)
            if self.cuda:
                images = images.cuda()

            pr = self.net(images)[0]

            pr = F.softmax(pr.permute(1, 2, 0), dim=-1).cpu().numpy()

            pr = pr[int((self.input_shape[0] - nh) // 2): int((self.input_shape[0] - nh) // 2 + nh), \
                 int((self.input_shape[1] - nw) // 2): int((self.input_shape[1] - nw) // 2 + nw)]

            pr = cv2.resize(pr, (orininal_w, orininal_h), interpolation=cv2.INTER_LINEAR)

            pr = pr.argmax(axis=-1)

        if count:
            classes_nums = np.zeros([self.num_classes])
            total_points_num = orininal_h * orininal_w
            print('-' * 63)
            print("|%25s | %15s | %15s|" % ("Key", "Value", "Ratio"))
            print('-' * 63)
            for i in range(self.num_classes):
                num = np.sum(pr == i)
                ratio = num / total_points_num * 100
                if num > 0:
                    print("|%25s | %15s | %14.2f%%|" % (str(name_classes[i]), str(num), ratio))
                    print('-' * 63)
                classes_nums[i] = num
            print("classes_nums:", classes_nums)

        if self.mix_type == 0:
            # seg_img = np.zeros((np.shape(pr)[0], np.shape(pr)[1], 3))
            # for c in range(self.num_classes):
            #     seg_img[:, :, 0] += ((pr[:, :] == c ) * self.colors[c][0]).astype('uint8')
            #     seg_img[:, :, 1] += ((pr[:, :] == c ) * self.colors[c][1]).astype('uint8')
            #     seg_img[:, :, 2] += ((pr[:, :] == c ) * self.colors[c][2]).astype('uint8')
            seg_img = np.reshape(np.array(self.colors, np.uint8)[np.reshape(pr, [-1])], [orininal_h, orininal_w, -1])
            # ------------------------------------------------#
            #   将新图片转换成Image的形式
            # ------------------------------------------------#
            image = Image.fromarray(np.uint8(seg_img))
            # ------------------------------------------------#
            #   将新图与原图及进行混合
            # ------------------------------------------------#
            image = Image.blend(old_img, image, 0.7)

        elif self.mix_type == 1:
            # seg_img = np.zeros((np.shape(pr)[0], np.shape(pr)[1], 3))
            # for c in range(self.num_classes):
            #     seg_img[:, :, 0] += ((pr[:, :] == c ) * self.colors[c][0]).astype('uint8')
            #     seg_img[:, :, 1] += ((pr[:, :] == c ) * self.colors[c][1]).astype('uint8')
            #     seg_img[:, :, 2] += ((pr[:, :] == c ) * self.colors[c][2]).astype('uint8')
            seg_img = np.reshape(np.array(self.colors, np.uint8)[np.reshape(pr, [-1])], [orininal_h, orininal_w, -1])
            # ------------------------------------------------#
            #   将新图片转换成Image的形式
            # ------------------------------------------------#
            image = Image.fromarray(np.uint8(seg_img))

        elif self.mix_type == 2:
            seg_img = (np.expand_dims(pr != 0, -1) * np.array(old_img, np.float32)).astype('uint8')
            # ------------------------------------------------#
            #   将新图片转换成Image的形式
            # ------------------------------------------------#
            image = Image.fromarray(np.uint8(seg_img))

        return image