frcnn.py

import cv2
import numpy as np
import colorsys
import os
import torch
import time
import torch.backends.cudnn as cudnn
from torch.nn import functional as F
from utils.utils import loc2bbox, nms, DecodeBox
from nets.frcnn import FasterRCNN
from nets.frcnn_training import get_new_img_size
from PIL import Image, ImageFont, ImageDraw
import copy
import math
from sobel import sobel_function

class FRCNN(object):
    _defaults = {
        "model_path"    : 'model_data/voc_weights_resnet.pth',
        "classes_path"  : 'model_data/voc_classes.txt',
        "confidence"    : 0.5,
        "iou"           : 0.5,
        "backbone"      : "resnet50"
    }

    @classmethod
    def get_defaults(cls, n):
        if n in cls._defaults:
            return cls._defaults[n]
        else:
            return "Unrecognized attribute name '" + n + "'"

    #---------------------------------------------------#
    #   初始化faster RCNN
    #---------------------------------------------------#
    def __init__(self, **kwargs):
        self.__dict__.update(self._defaults)
        self.class_names = self._get_class()
        self.generate()
        self.mean = torch.Tensor([0,0,0,0]).cuda().repeat(self.num_classes+1)[None]
        self.std = torch.Tensor([0.1, 0.1, 0.2, 0.2]).cuda().repeat(self.num_classes+1)[None]

    #---------------------------------------------------#
    #   获得所有的分类
    #---------------------------------------------------#
    def _get_class(self):
        classes_path = os.path.expanduser(self.classes_path)
        with open(classes_path) as f:
            class_names = f.readlines()
        class_names = [c.strip() for c in class_names]
        return class_names

    #---------------------------------------------------#
    #   获得所有的分类
    #---------------------------------------------------#
    def generate(self):
        # 计算总的种类
        self.num_classes = len(self.class_names)

        # 载入模型，如果原来的模型里已经包括了模型结构则直接载入。
        # 否则先构建模型再载入
        self.model = FasterRCNN(self.num_classes,"predict",backbone=self.backbone).cuda()
        self.model = self.model.eval()
        self.model.load_state_dict(torch.load(self.model_path))
        cudnn.benchmark = True
                
        print('{} model, anchors, and classes loaded.'.format(self.model_path))

        # 画框设置不同的颜色
        hsv_tuples = [(x / len(self.class_names), 1., 1.)
                      for x in range(len(self.class_names))]
        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))
    
    #---------------------------------------------------#
    #   检测图片
    #---------------------------------------------------#
    def detect_image(self, image):
        image = sobel_function(image)
        start_time = time.time()
        image_shape = np.array(np.shape(image)[0:2])
        old_width = image_shape[1]
        old_height = image_shape[0]
        old_image = copy.deepcopy(image)
        width,height = get_new_img_size(old_width,old_height)

        image = image.resize([width,height], Image.BICUBIC)
        photo = np.array(image,dtype = np.float32)/255
        photo = np.transpose(photo, (2, 0, 1))
        
        with torch.no_grad():
            images = []
            images.append(photo)
            images = np.asarray(images)
            images = torch.from_numpy(images).cuda()

            roi_cls_locs, roi_scores, rois, roi_indices = self.model(images)
            decodebox = DecodeBox(self.std, self.mean, self.num_classes)
            outputs = decodebox.forward(roi_cls_locs, roi_scores, rois, height = height, width = width, nms_iou = self.iou, score_thresh = self.confidence)
            if len(outputs)==0:
                return old_image
            bbox = outputs[:,:4]
            conf = outputs[:, 4]
            label = outputs[:, 5]

            bbox[:, 0::2] = (bbox[:, 0::2])/width*old_width
            bbox[:, 1::2] = (bbox[:, 1::2])/height*old_height
            bbox = np.array(bbox,np.int32)

        image = old_image
        thickness = (np.shape(old_image)[0] + np.shape(old_image)[1]) // old_width*2
        font = ImageFont.truetype(font='model_data/simhei.ttf',size=np.floor(3e-2 * np.shape(image)[1] + 0.5).astype('int32'))
                
        for i, c in enumerate(label):
            predicted_class = self.class_names[int(c)]
            score = conf[i]

            left, top, right, bottom = bbox[i]
            top = top - 5
            left = left - 5
            bottom = bottom + 5
            right = right + 5

            top = max(0, np.floor(top + 0.5).astype('int32'))
            left = max(0, np.floor(left + 0.5).astype('int32'))
            bottom = min(np.shape(image)[0], np.floor(bottom + 0.5).astype('int32'))
            right = min(np.shape(image)[1], np.floor(right + 0.5).astype('int32'))

            # 画框框
            label = '{} {:.2f}'.format(predicted_class, score)
            draw = ImageDraw.Draw(image)
            label_size = draw.textsize(label, font)
            label = label.encode('utf-8')
            print(label)
            
            if top - label_size[1] >= 0:
                text_origin = np.array([left, top - label_size[1]])
            else:
                text_origin = np.array([left, top + 1])

            for i in range(thickness):
                draw.rectangle(
                    [left + i, top + i, right - i, bottom - i],
                    outline=self.colors[int(c)])
            draw.rectangle(
                [tuple(text_origin), tuple(text_origin + label_size)],
                fill=self.colors[int(c)])
            draw.text(text_origin, str(label,'UTF-8'), fill=(0, 0, 0), font=font)
            del draw
        
        print("time:",time.time()-start_time)
        return image