426 lines
21 KiB
Python
426 lines
21 KiB
Python
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import colorsys
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import os
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import time
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import numpy as np
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import torch
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import torch.nn as nn
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from PIL import ImageDraw, ImageFont
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from nets.yolo import YoloBody
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from utils.utils import (cvtColor, get_anchors, get_classes, preprocess_input,
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resize_image, show_config)
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from utils.utils_bbox import DecodeBox
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'''
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训练自己的数据集必看注释！
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'''
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class YOLO(object):
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_defaults = {
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# --------------------------------------------------------------------------#
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# 使用自己训练好的模型进行预测一定要修改model_path和classes_path！
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# model_path指向logs文件夹下的权值文件，classes_path指向model_data下的txt
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#
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# 训练好后logs文件夹下存在多个权值文件，选择验证集损失较低的即可。
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# 验证集损失较低不代表mAP较高，仅代表该权值在验证集上泛化性能较好。
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# 如果出现shape不匹配，同时要注意训练时的model_path和classes_path参数的修改
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# --------------------------------------------------------------------------#
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# "model_path": 'model_data/yolo_weights.pth',
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# "classes_path": 'model_data/coco_classes.txt',
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"model_path": 'logs/best_epoch_weights.pth',
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"classes_path": 'model_data/cctsdb_classes.txt',
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# ---------------------------------------------------------------------#
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# anchors_path代表先验框对应的txt文件，一般不修改。
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# anchors_mask用于帮助代码找到对应的先验框，一般不修改。
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# ---------------------------------------------------------------------#
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"anchors_path": 'model_data/yolo_anchors.txt',
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"anchors_mask": [[6, 7, 8], [3, 4, 5], [0, 1, 2]],
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# ---------------------------------------------------------------------#
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# 输入图片的大小，必须为32的倍数。
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# ---------------------------------------------------------------------#
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"input_shape": [416, 416],
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# ---------------------------------------------------------------------#
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# 只有得分大于置信度的预测框会被保留下来
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# ---------------------------------------------------------------------#
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"confidence": 0.5,
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# ---------------------------------------------------------------------#
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# 非极大抑制所用到的nms_iou大小
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# ---------------------------------------------------------------------#
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"nms_iou": 0.3,
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# ---------------------------------------------------------------------#
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# 该变量用于控制是否使用letterbox_image对输入图像进行不失真的resize，
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# 在多次测试后，发现关闭letterbox_image直接resize的效果更好
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# ---------------------------------------------------------------------#
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"letterbox_image": False,
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# -------------------------------#
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# 是否使用Cuda
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# 没有GPU可以设置成False
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# -------------------------------#
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"cuda": True
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}
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@classmethod
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def get_defaults(cls, n):
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if n in cls._defaults:
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return cls._defaults[n]
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else:
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return "Unrecognized attribute name '" + n + "'"
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# ---------------------------------------------------#
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# 初始化YOLO
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# ---------------------------------------------------#
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def __init__(self, **kwargs):
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self.__dict__.update(self._defaults) # 用类的_defaults变量更新当前对象的属性字典
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for name, value in kwargs.items():
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setattr(self, name, value)
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# ---------------------------------------------------#
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# 获得种类和先验框的数量
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# ---------------------------------------------------#
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self.class_names, self.num_classes = get_classes(self.classes_path)
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self.anchors, self.num_anchors = get_anchors(self.anchors_path)
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self.bbox_util = DecodeBox(self.anchors, self.num_classes, (self.input_shape[0], self.input_shape[1]),
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self.anchors_mask)
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# ---------------------------------------------------#
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# 画框设置不同的颜色
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# ---------------------------------------------------#
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hsv_tuples = [(x / self.num_classes, 1., 1.) for x in range(self.num_classes)]
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self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
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self.colors = list(map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)), self.colors))
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self.generate()
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show_config(**self._defaults)
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# ---------------------------------------------------#
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# 生成模型
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# ---------------------------------------------------#
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def generate(self, onnx=False):
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# ---------------------------------------------------#
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# 建立yolov3模型，载入yolov3模型的权重
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# ---------------------------------------------------#
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self.net = YoloBody(self.anchors_mask, self.num_classes)
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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self.net.load_state_dict(torch.load(self.model_path, map_location=device))
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self.net = self.net.eval()
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print('{} model, anchors, and classes loaded.'.format(self.model_path))
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# if not onnx:
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# if self.cuda:
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# self.net = nn.DataParallel(self.net)
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# self.net = self.net.cuda()
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if not onnx:
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if self.cuda:
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self.net = self.net.cuda()
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# ---------------------------------------------------#
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# 检测图片
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# ---------------------------------------------------#
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def detect_image(self, image, crop=False, count=False):
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image_shape = np.array(np.shape(image)[0:2]) # np.shape(image) 的形状 h,w,c
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# ---------------------------------------------------------#
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# 在这里将图像转换成RGB图像，防止灰度图在预测时报错。
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# 代码仅仅支持RGB图像的预测，所有其它类型的图像都会转化成RGB
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# ---------------------------------------------------------#
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image = cvtColor(image)
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# ---------------------------------------------------------#
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# 给图像增加灰条，实现不失真的resize
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# 也可以直接resize进行识别
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# ---------------------------------------------------------#
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image_data = resize_image(image, (self.input_shape[1], self.input_shape[0]), self.letterbox_image)
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# ---------------------------------------------------------#
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# 添加上batch_size维度
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# ---------------------------------------------------------#
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image_data = np.expand_dims(np.transpose(preprocess_input(np.array(image_data, dtype='float32')), (2, 0, 1)), 0)
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# image_data 变换后的维度是 1, 3, 416, 416
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with torch.no_grad():
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images = torch.from_numpy(image_data)
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if self.cuda:
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images = images.cuda()
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# ---------------------------------------------------------#
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# 将图像输入网络当中进行预测！
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# ---------------------------------------------------------#
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outputs = self.net(images)
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outputs = self.bbox_util.decode_box(outputs)
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# ---------------------------------------------------------#
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# 将预测框进行堆叠，然后进行非极大抑制
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# ---------------------------------------------------------#
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results = self.bbox_util.non_max_suppression(torch.cat(outputs, 1), self.num_classes, self.input_shape,
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image_shape, self.letterbox_image, conf_thres=self.confidence,
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nms_thres=self.nms_iou)
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if results[0] is None:
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return image
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top_label = np.array(results[0][:, 6], dtype='int32')
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top_conf = results[0][:, 4] * results[0][:, 5]
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top_boxes = results[0][:, :4]
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# ---------------------------------------------------------#
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# 设置字体与边框厚度
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# ---------------------------------------------------------#
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font = ImageFont.truetype(font='model_data/simhei.ttf',
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size=np.floor(3e-2 * image.size[1] + 0.5).astype('int32'))
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thickness = int(max((image.size[0] + image.size[1]) // np.mean(self.input_shape), 1))
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# ---------------------------------------------------------#
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# 计数
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# ---------------------------------------------------------#
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if count:
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print("top_label:", top_label)
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classes_nums = np.zeros([self.num_classes])
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for i in range(self.num_classes):
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num = np.sum(top_label == i)
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if num > 0:
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print(self.class_names[i], " : ", num)
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classes_nums[i] = num
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print("classes_nums:", classes_nums)
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# ---------------------------------------------------------#
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# 是否进行目标的裁剪
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# ---------------------------------------------------------#
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if crop:
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for i, c in list(enumerate(top_label)):
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top, left, bottom, right = top_boxes[i]
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top = max(0, np.floor(top).astype('int32'))
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left = max(0, np.floor(left).astype('int32'))
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bottom = min(image.size[1], np.floor(bottom).astype('int32'))
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right = min(image.size[0], np.floor(right).astype('int32'))
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dir_save_path = "img_crop"
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if not os.path.exists(dir_save_path):
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os.makedirs(dir_save_path)
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crop_image = image.crop([left, top, right, bottom])
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crop_image.save(os.path.join(dir_save_path, "crop_" + str(i) + ".png"), quality=95, subsampling=0)
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print("save crop_" + str(i) + ".png to " + dir_save_path)
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# ---------------------------------------------------------#
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# 图像绘制
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# ---------------------------------------------------------#
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for i, c in list(enumerate(top_label)):
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predicted_class = self.class_names[int(c)]
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box = top_boxes[i]
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score = top_conf[i]
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top, left, bottom, right = box
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top = max(0, np.floor(top).astype('int32'))
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left = max(0, np.floor(left).astype('int32'))
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bottom = min(image.size[1], np.floor(bottom).astype('int32'))
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right = min(image.size[0], np.floor(right).astype('int32'))
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label = '{} {:.2f}'.format(predicted_class, score)
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draw = ImageDraw.Draw(image)
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label_size = draw.textsize(label, font)
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label = label.encode('utf-8')
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# print(label, top, left, bottom, right)
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if top - label_size[1] >= 0: # 框到顶的距离大于 label_size，就是可以在顶部放标签
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text_origin = np.array([left, top - label_size[1]])
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else: # 否则放在框内部
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text_origin = np.array([left, top + 1])
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for i in range(thickness): # 画粗细的实现？是画6次？
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draw.rectangle([left + i, top + i, right - i, bottom - i], outline=self.colors[c])
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draw.rectangle([tuple(text_origin), tuple(text_origin + label_size)], fill=self.colors[c])
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draw.text(text_origin, str(label, 'UTF-8'), fill=(0, 0, 0), font=font)
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del draw
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return image
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def get_FPS(self, image, test_interval):
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image_shape = np.array(np.shape(image)[0:2])
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# ---------------------------------------------------------#
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# 在这里将图像转换成RGB图像，防止灰度图在预测时报错。
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# 代码仅仅支持RGB图像的预测，所有其它类型的图像都会转化成RGB
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# ---------------------------------------------------------#
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image = cvtColor(image)
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# ---------------------------------------------------------#
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# 给图像增加灰条，实现不失真的resize
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# 也可以直接resize进行识别
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# ---------------------------------------------------------#
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image_data = resize_image(image, (self.input_shape[1], self.input_shape[0]), self.letterbox_image)
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# ---------------------------------------------------------#
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# 添加上batch_size维度
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# ---------------------------------------------------------#
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image_data = np.expand_dims(np.transpose(preprocess_input(np.array(image_data, dtype='float32')), (2, 0, 1)), 0)
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with torch.no_grad():
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images = torch.from_numpy(image_data)
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if self.cuda:
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images = images.cuda()
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# ---------------------------------------------------------#
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# 将图像输入网络当中进行预测！
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# ---------------------------------------------------------#
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outputs = self.net(images)
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outputs = self.bbox_util.decode_box(outputs)
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# ---------------------------------------------------------#
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# 将预测框进行堆叠，然后进行非极大抑制
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# ---------------------------------------------------------#
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results = self.bbox_util.non_max_suppression(torch.cat(outputs, 1), self.num_classes, self.input_shape,
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image_shape, self.letterbox_image, conf_thres=self.confidence,
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nms_thres=self.nms_iou)
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t1 = time.time()
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for _ in range(test_interval):
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with torch.no_grad():
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# ---------------------------------------------------------#
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# 将图像输入网络当中进行预测！
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# ---------------------------------------------------------#
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outputs = self.net(images)
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outputs = self.bbox_util.decode_box(outputs)
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# ---------------------------------------------------------#
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# 将预测框进行堆叠，然后进行非极大抑制
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# ---------------------------------------------------------#
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results = self.bbox_util.non_max_suppression(torch.cat(outputs, 1), self.num_classes, self.input_shape,
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image_shape, self.letterbox_image,
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conf_thres=self.confidence, nms_thres=self.nms_iou)
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t2 = time.time()
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tact_time = (t2 - t1) / test_interval
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return tact_time
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def detect_heatmap(self, image, heatmap_save_path):
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import cv2
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import matplotlib.pyplot as plt
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def sigmoid(x):
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y = 1.0 / (1.0 + np.exp(-x))
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return y
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# ---------------------------------------------------------#
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# 在这里将图像转换成RGB图像，防止灰度图在预测时报错。
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# 代码仅仅支持RGB图像的预测，所有其它类型的图像都会转化成RGB
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# ---------------------------------------------------------#
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image = cvtColor(image)
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# ---------------------------------------------------------#
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# 给图像增加灰条，实现不失真的resize
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# 也可以直接resize进行识别
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# ---------------------------------------------------------#
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image_data = resize_image(image, (self.input_shape[1], self.input_shape[0]), self.letterbox_image)
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# ---------------------------------------------------------#
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# 添加上batch_size维度
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# ---------------------------------------------------------#
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image_data = np.expand_dims(np.transpose(preprocess_input(np.array(image_data, dtype='float32')), (2, 0, 1)), 0)
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|
|||
|
|
with torch.no_grad():
|
|||
|
|
images = torch.from_numpy(image_data)
|
|||
|
|
if self.cuda:
|
|||
|
|
images = images.cuda()
|
|||
|
|
# ---------------------------------------------------------#
|
|||
|
|
# 将图像输入网络当中进行预测！
|
|||
|
|
# ---------------------------------------------------------#
|
|||
|
|
outputs = self.net(images)
|
|||
|
|
|
|||
|
|
plt.imshow(image, alpha=1)
|
|||
|
|
plt.axis('off')
|
|||
|
|
mask = np.zeros((image.size[1], image.size[0]))
|
|||
|
|
for sub_output in outputs:
|
|||
|
|
sub_output = sub_output.cpu().numpy()
|
|||
|
|
b, c, h, w = np.shape(sub_output)
|
|||
|
|
sub_output = np.transpose(np.reshape(sub_output, [b, 3, -1, h, w]), [0, 3, 4, 1, 2])[0]
|
|||
|
|
score = np.max(sigmoid(sub_output[..., 4]), -1)
|
|||
|
|
score = cv2.resize(score, (image.size[0], image.size[1]))
|
|||
|
|
normed_score = (score * 255).astype('uint8')
|
|||
|
|
mask = np.maximum(mask, normed_score)
|
|||
|
|
|
|||
|
|
plt.imshow(mask, alpha=0.5, interpolation='nearest', cmap="jet")
|
|||
|
|
|
|||
|
|
plt.axis('off')
|
|||
|
|
plt.subplots_adjust(top=1, bottom=0, right=1, left=0, hspace=0, wspace=0)
|
|||
|
|
plt.margins(0, 0)
|
|||
|
|
plt.savefig(heatmap_save_path, dpi=200, bbox_inches='tight', pad_inches=-0.1)
|
|||
|
|
print("Save to the " + heatmap_save_path)
|
|||
|
|
plt.show()
|
|||
|
|
|
|||
|
|
def convert_to_onnx(self, simplify, model_path):
|
|||
|
|
import onnx
|
|||
|
|
self.generate(onnx=True)
|
|||
|
|
|
|||
|
|
im = torch.zeros(1, 3, *self.input_shape).to('cpu') # image size(1, 3, 512, 512) BCHW
|
|||
|
|
input_layer_names = ["images"]
|
|||
|
|
output_layer_names = ["output"]
|
|||
|
|
|
|||
|
|
# Export the model
|
|||
|
|
print(f'Starting export with onnx {onnx.__version__}.')
|
|||
|
|
torch.onnx.export(self.net,
|
|||
|
|
im,
|
|||
|
|
f=model_path,
|
|||
|
|
verbose=False,
|
|||
|
|
opset_version=12,
|
|||
|
|
training=torch.onnx.TrainingMode.EVAL,
|
|||
|
|
do_constant_folding=True,
|
|||
|
|
input_names=input_layer_names,
|
|||
|
|
output_names=output_layer_names,
|
|||
|
|
dynamic_axes=None)
|
|||
|
|
|
|||
|
|
# Checks
|
|||
|
|
model_onnx = onnx.load(model_path) # load onnx model
|
|||
|
|
onnx.checker.check_model(model_onnx) # check onnx model
|
|||
|
|
|
|||
|
|
# Simplify onnx
|
|||
|
|
if simplify:
|
|||
|
|
import onnxsim
|
|||
|
|
print(f'Simplifying with onnx-simplifier {onnxsim.__version__}.')
|
|||
|
|
model_onnx, check = onnxsim.simplify(
|
|||
|
|
model_onnx,
|
|||
|
|
dynamic_input_shape=False,
|
|||
|
|
input_shapes=None)
|
|||
|
|
assert check, 'assert check failed'
|
|||
|
|
onnx.save(model_onnx, model_path)
|
|||
|
|
|
|||
|
|
print('Onnx model save as {}'.format(model_path))
|
|||
|
|
|
|||
|
|
def get_map_txt(self, image_id, image, class_names, map_out_path):
|
|||
|
|
f = open(os.path.join(map_out_path, "detection-results/" + image_id + ".txt"), "w")
|
|||
|
|
image_shape = np.array(np.shape(image)[0:2])
|
|||
|
|
# ---------------------------------------------------------#
|
|||
|
|
# 在这里将图像转换成RGB图像，防止灰度图在预测时报错。
|
|||
|
|
# 代码仅仅支持RGB图像的预测，所有其它类型的图像都会转化成RGB
|
|||
|
|
# ---------------------------------------------------------#
|
|||
|
|
image = cvtColor(image)
|
|||
|
|
# ---------------------------------------------------------#
|
|||
|
|
# 给图像增加灰条，实现不失真的resize
|
|||
|
|
# 也可以直接resize进行识别
|
|||
|
|
# ---------------------------------------------------------#
|
|||
|
|
image_data = resize_image(image, (self.input_shape[1], self.input_shape[0]), self.letterbox_image)
|
|||
|
|
# ---------------------------------------------------------#
|
|||
|
|
# 添加上batch_size维度
|
|||
|
|
# ---------------------------------------------------------#
|
|||
|
|
image_data = np.expand_dims(np.transpose(preprocess_input(np.array(image_data, dtype='float32')), (2, 0, 1)), 0)
|
|||
|
|
|
|||
|
|
with torch.no_grad():
|
|||
|
|
images = torch.from_numpy(image_data)
|
|||
|
|
if self.cuda:
|
|||
|
|
images = images.cuda()
|
|||
|
|
# ---------------------------------------------------------#
|
|||
|
|
# 将图像输入网络当中进行预测！
|
|||
|
|
# ---------------------------------------------------------#
|
|||
|
|
outputs = self.net(images)
|
|||
|
|
outputs = self.bbox_util.decode_box(outputs)
|
|||
|
|
# ---------------------------------------------------------#
|
|||
|
|
# 将预测框进行堆叠，然后进行非极大抑制
|
|||
|
|
# ---------------------------------------------------------#
|
|||
|
|
results = self.bbox_util.non_max_suppression(torch.cat(outputs, 1), self.num_classes, self.input_shape,
|
|||
|
|
image_shape, self.letterbox_image, conf_thres=self.confidence,
|
|||
|
|
nms_thres=self.nms_iou)
|
|||
|
|
|
|||
|
|
if results[0] is None:
|
|||
|
|
return
|
|||
|
|
|
|||
|
|
top_label = np.array(results[0][:, 6], dtype='int32')
|
|||
|
|
top_conf = results[0][:, 4] * results[0][:, 5]
|
|||
|
|
top_boxes = results[0][:, :4]
|
|||
|
|
|
|||
|
|
for i, c in list(enumerate(top_label)):
|
|||
|
|
predicted_class = self.class_names[int(c)]
|
|||
|
|
box = top_boxes[i]
|
|||
|
|
score = str(top_conf[i])
|
|||
|
|
|
|||
|
|
top, left, bottom, right = box
|
|||
|
|
if predicted_class not in class_names:
|
|||
|
|
continue
|
|||
|
|
|
|||
|
|
f.write("%s %s %s %s %s %s\n" % (
|
|||
|
|
predicted_class, score[:6], str(int(left)), str(int(top)), str(int(right)), str(int(bottom))))
|
|||
|
|
|
|||
|
|
f.close()
|
|||
|
|
return
|