import colorsys import os import time import numpy as np import tensorflow as tf from tensorflow.keras import backend as K from tensorflow.keras.applications.imagenet_utils import preprocess_input from PIL import ImageDraw, ImageFont from nets.ssd import SSD300 from utils.utils_bbox import BBoxUtility from utils.utils import get_classes, resize_image, cvtColor from utils.anchors import get_anchors ''' 训练自己的数据集必看！ ''' class SSD(object): _defaults = { #--------------------------------------------------------------------------# # 使用自己训练好的模型进行预测一定要修改model_path和classes_path！ # model_path指向logs文件夹下的权值文件，classes_path指向model_data下的txt # # 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。 # 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。 # 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 #--------------------------------------------------------------------------# "model_path" : 'model_data/ssd_weights.h5', "classes_path" : 'model_data/voc_classes.txt', #---------------------------------------------------------------------# # 用于预测的图像大小，和train时使用同一个即可 #---------------------------------------------------------------------# "input_shape" : [300, 300], #---------------------------------------------------------------------# # 只有得分大于置信度的预测框会被保留下来 #---------------------------------------------------------------------# "confidence" : 0.5, #---------------------------------------------------------------------# # 非极大抑制所用到的nms_iou大小 #---------------------------------------------------------------------# "nms_iou" : 0.45, #---------------------------------------------------------------------# # 用于指定先验框的大小 #---------------------------------------------------------------------# 'anchors_size' : [30, 60, 111, 162, 213, 264, 315], #---------------------------------------------------------------------# # 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， # 在多次测试后，发现关闭letterbox_image直接resize的效果更好 #---------------------------------------------------------------------# "letterbox_image" : False, } @classmethod def get_defaults(cls, n): if n in cls._defaults: return cls._defaults[n] else: return "Unrecognized attribute name '" + n + "'" #---------------------------------------------------# # 初始化ssd #---------------------------------------------------# def __init__(self, **kwargs): self.__dict__.update(self._defaults) for name, value in kwargs.items(): setattr(self, name, value) #---------------------------------------------------# # 计算总的类的数量 #---------------------------------------------------# self.class_names, self.num_classes = get_classes(self.classes_path) self.anchors = get_anchors(self.input_shape, self.anchors_size) self.num_classes = self.num_classes + 1 #---------------------------------------------------# # 画框设置不同的颜色 #---------------------------------------------------# 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.bbox_util = BBoxUtility(self.num_classes, nms_thresh=self.nms_iou) self.generate() #---------------------------------------------------# # 载入模型 #---------------------------------------------------# def generate(self): model_path = os.path.expanduser(self.model_path) assert model_path.endswith('.h5'), 'Keras model or weights must be a .h5 file.' #-------------------------------# # 载入模型与权值 #-------------------------------# self.ssd = SSD300([self.input_shape[0], self.input_shape[1], 3], self.num_classes) self.ssd.load_weights(self.model_path, by_name=True) print('{} model, anchors, and classes loaded.'.format(model_path)) @tf.function def get_pred(self, photo): preds = self.ssd(photo, training=False) return preds #---------------------------------------------------# # 检测图片 #---------------------------------------------------# def detect_image(self, image, crop = False): 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，图片预处理，归一化。 #---------------------------------------------------------# image_data = preprocess_input(np.expand_dims(np.array(image_data, dtype='float32'), 0)) preds = self.get_pred(image_data).numpy() #-----------------------------------------------------------# # 将预测结果进行解码 #-----------------------------------------------------------# results = self.bbox_util.decode_box(preds, self.anchors, image_shape, self.input_shape, self.letterbox_image, confidence=self.confidence) #--------------------------------------# # 如果没有检测到物体，则返回原图 #--------------------------------------# if len(results[0])<=0: return image top_label = np.array(results[0][:, 4], dtype = 'int32') top_conf = results[0][:, 5] top_boxes = results[0][:, :4] #---------------------------------------------------------# # 设置字体与边框厚度 #---------------------------------------------------------# font = ImageFont.truetype(font='model_data/simhei.ttf', size=np.floor(3e-2 * np.shape(image)[1] + 0.5).astype('int32')) thickness = max((np.shape(image)[0] + np.shape(image)[1]) // self.input_shape[0], 1) #---------------------------------------------------------# # 是否进行目标的裁剪 #---------------------------------------------------------# if crop: for i, c in list(enumerate(top_boxes)): top, left, bottom, right = top_boxes[i] top = max(0, np.floor(top).astype('int32')) left = max(0, np.floor(left).astype('int32')) bottom = min(image.size[1], np.floor(bottom).astype('int32')) right = min(image.size[0], np.floor(right).astype('int32')) dir_save_path = "img_crop" if not os.path.exists(dir_save_path): os.makedirs(dir_save_path) crop_image = image.crop([left, top, right, bottom]) crop_image.save(os.path.join(dir_save_path, "crop_" + str(i) + ".png"), quality=95, subsampling=0) print("save crop_" + str(i) + ".png to " + dir_save_path) #---------------------------------------------------------# # 图像绘制 #---------------------------------------------------------# for i, c in list(enumerate(top_label)): predicted_class = self.class_names[int(c)] box = top_boxes[i] score = top_conf[i] top, left, bottom, right = box top = max(0, np.floor(top).astype('int32')) left = max(0, np.floor(left).astype('int32')) bottom = min(image.size[1], np.floor(bottom).astype('int32')) right = min(image.size[0], np.floor(right).astype('int32')) label = '{} {:.2f}'.format(predicted_class, score) draw = ImageDraw.Draw(image) label_size = draw.textsize(label, font) label = label.encode('utf-8') print(label, top, left, bottom, right) 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[c]) draw.rectangle([tuple(text_origin), tuple(text_origin + label_size)], fill=self.colors[c]) draw.text(text_origin, str(label,'UTF-8'), fill=(0, 0, 0), font=font) del draw return image def get_FPS(self, image, test_interval): 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，图片预处理，归一化。 #---------------------------------------------------------# image_data = preprocess_input(np.expand_dims(np.array(image_data, dtype='float32'), 0)) preds = self.get_pred(image_data).numpy() #-----------------------------------------------------------# # 将预测结果进行解码 #-----------------------------------------------------------# results = self.bbox_util.decode_box(preds, self.anchors, image_shape, self.input_shape, self.letterbox_image, confidence=self.confidence) t1 = time.time() for _ in range(test_interval): preds = self.get_pred(image_data).numpy() #-----------------------------------------------------------# # 将预测结果进行解码 #-----------------------------------------------------------# results = self.bbox_util.decode_box(preds, self.anchors, image_shape, self.input_shape, self.letterbox_image, confidence=self.confidence) t2 = time.time() tact_time = (t2 - t1) / test_interval return tact_time 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，图片预处理，归一化。 #---------------------------------------------------------# image_data = preprocess_input(np.expand_dims(np.array(image_data, dtype='float32'), 0)) preds = self.get_pred(image_data).numpy() #-----------------------------------------------------------# # 将预测结果进行解码 #-----------------------------------------------------------# results = self.bbox_util.decode_box(preds, self.anchors, image_shape, self.input_shape, self.letterbox_image, confidence=self.confidence) #--------------------------------------# # 如果没有检测到物体，则返回原图 #--------------------------------------# if len(results[0])<=0: return top_label = results[0][:, 4] top_conf = 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