import math from random import shuffle import cv2 import numpy as np from PIL import Image from tensorflow import keras from tensorflow.keras.applications.imagenet_utils import preprocess_input from utils.utils import cvtColor class SSDDatasets(keras.utils.Sequence): def __init__(self, annotation_lines, input_shape, anchors, batch_size, num_classes, train, overlap_threshold = 0.5): self.annotation_lines = annotation_lines self.length = len(self.annotation_lines) self.input_shape = input_shape self.anchors = anchors self.num_anchors = len(anchors) self.batch_size = batch_size self.num_classes = num_classes self.train = train self.overlap_threshold = overlap_threshold def __len__(self): return math.ceil(len(self.annotation_lines) / float(self.batch_size)) def __getitem__(self, index): image_data = [] box_data = [] for i in range(index * self.batch_size, (index + 1) * self.batch_size): i = i % self.length #---------------------------------------------------# # 训练时进行数据的随机增强 # 验证时不进行数据的随机增强 #---------------------------------------------------# image, box = self.get_random_data(self.annotation_lines[i], self.input_shape, random = self.train) if len(box)!=0: boxes = np.array(box[:,:4] , dtype=np.float32) boxes[:, [0, 2]] = boxes[:,[0, 2]] / self.input_shape[1] boxes[:, [1, 3]] = boxes[:,[1, 3]] / self.input_shape[0] one_hot_label = np.eye(self.num_classes - 1)[np.array(box[:,4], np.int32)] box = np.concatenate([boxes, one_hot_label], axis=-1) box = self.assign_boxes(box) image_data.append(image) box_data.append(box) #print(preprocess_input(np.array(image_data)), np.array(box_data)) break return preprocess_input(np.array(image_data)), np.array(box_data) def generate(self): i = 0 while True: image_data = [] box_data = [] for b in range(self.batch_size): if i==0: np.random.shuffle(self.annotation_lines) #---------------------------------------------------# # 训练时进行数据的随机增强 # 验证时不进行数据的随机增强 #---------------------------------------------------# image, box = self.get_random_data(self.annotation_lines[i], self.input_shape, random = self.train) i = (i+1) % self.length if len(box)!=0: boxes = np.array(box[:,:4] , dtype=np.float32) boxes[:, [0, 2]] = boxes[:,[0, 2]] / self.input_shape[1] boxes[:, [1, 3]] = boxes[:,[1, 3]] / self.input_shape[0] one_hot_label = np.eye(self.num_classes - 1)[np.array(box[:,4], np.int32)] box = np.concatenate([boxes, one_hot_label], axis=-1) box = self.assign_boxes(box) image_data.append(image) box_data.append(box) yield preprocess_input(np.array(image_data)), np.array(box_data) def rand(self, a=0, b=1): return np.random.rand()*(b-a) + a def get_random_data(self, annotation_line, input_shape, jitter=.3, hue=.1, sat=1.5, val=1.5, random=True): line = annotation_line.split() #------------------------------# # 读取图像并转换成RGB图像 #------------------------------# image = Image.open(line[0]) image = cvtColor(image) #------------------------------# # 获得图像的高宽与目标高宽 #------------------------------# iw, ih = image.size h, w = input_shape #------------------------------# # 获得预测框 #------------------------------# box = np.array([np.array(list(map(int,box.split(',')))) for box in line[1:]]) if not random: scale = min(w/iw, h/ih) nw = int(iw*scale) nh = int(ih*scale) dx = (w-nw)//2 dy = (h-nh)//2 #---------------------------------# # 将图像多余的部分加上灰条 #---------------------------------# image = image.resize((nw,nh), Image.BICUBIC) new_image = Image.new('RGB', (w,h), (128,128,128)) new_image.paste(image, (dx, dy)) image_data = np.array(new_image, np.float32) #---------------------------------# # 对真实框进行调整 #---------------------------------# if len(box)>0: np.random.shuffle(box) box[:, [0,2]] = box[:, [0,2]]*nw/iw + dx box[:, [1,3]] = box[:, [1,3]]*nh/ih + dy box[:, 0:2][box[:, 0:2]<0] = 0 box[:, 2][box[:, 2]>w] = w box[:, 3][box[:, 3]>h] = h box_w = box[:, 2] - box[:, 0] box_h = box[:, 3] - box[:, 1] box = box[np.logical_and(box_w>1, box_h>1)] # discard invalid box return image_data, box #------------------------------------------# # 对图像进行缩放并且进行长和宽的扭曲 #------------------------------------------# new_ar = w/h * self.rand(1-jitter,1+jitter) / self.rand(1-jitter,1+jitter) scale = self.rand(.25, 2) if new_ar < 1: nh = int(scale*h) nw = int(nh*new_ar) else: nw = int(scale*w) nh = int(nw/new_ar) image = image.resize((nw,nh), Image.BICUBIC) #------------------------------------------# # 将图像多余的部分加上灰条 #------------------------------------------# dx = int(self.rand(0, w-nw)) dy = int(self.rand(0, h-nh)) new_image = Image.new('RGB', (w,h), (128,128,128)) new_image.paste(image, (dx, dy)) image = new_image #------------------------------------------# # 翻转图像 #------------------------------------------# flip = self.rand()<.5 if flip: image = image.transpose(Image.FLIP_LEFT_RIGHT) #------------------------------------------# # 色域扭曲 #------------------------------------------# hue = self.rand(-hue, hue) sat = self.rand(1, sat) if self.rand()<.5 else 1/self.rand(1, sat) val = self.rand(1, val) if self.rand()<.5 else 1/self.rand(1, val) x = cv2.cvtColor(np.array(image,np.float32)/255, cv2.COLOR_RGB2HSV) x[..., 0] += hue*360 x[..., 0][x[..., 0]>1] -= 1 x[..., 0][x[..., 0]<0] += 1 x[..., 1] *= sat x[..., 2] *= val x[x[:,:, 0]>360, 0] = 360 x[:, :, 1:][x[:, :, 1:]>1] = 1 x[x<0] = 0 image_data = cv2.cvtColor(x, cv2.COLOR_HSV2RGB)*255 # numpy array, 0 to 1 #---------------------------------# # 对真实框进行调整 #---------------------------------# if len(box)>0: np.random.shuffle(box) box[:, [0,2]] = box[:, [0,2]]*nw/iw + dx box[:, [1,3]] = box[:, [1,3]]*nh/ih + dy if flip: box[:, [0,2]] = w - box[:, [2,0]] box[:, 0:2][box[:, 0:2]<0] = 0 box[:, 2][box[:, 2]>w] = w box[:, 3][box[:, 3]>h] = h box_w = box[:, 2] - box[:, 0] box_h = box[:, 3] - box[:, 1] box = box[np.logical_and(box_w>1, box_h>1)] return image_data, box def on_epoch_begin(self): shuffle(self.annotation_lines) def iou(self, box): #---------------------------------------------# # 计算出每个真实框与所有的先验框的iou # 判断真实框与先验框的重合情况 #---------------------------------------------# inter_upleft = np.maximum(self.anchors[:, :2], box[:2]) inter_botright = np.minimum(self.anchors[:, 2:4], box[2:]) inter_wh = inter_botright - inter_upleft inter_wh = np.maximum(inter_wh, 0) inter = inter_wh[:, 0] * inter_wh[:, 1] #---------------------------------------------# # 真实框的面积 #---------------------------------------------# area_true = (box[2] - box[0]) * (box[3] - box[1]) #---------------------------------------------# # 先验框的面积 #---------------------------------------------# area_gt = (self.anchors[:, 2] - self.anchors[:, 0])*(self.anchors[:, 3] - self.anchors[:, 1]) #---------------------------------------------# # 计算iou #---------------------------------------------# union = area_true + area_gt - inter iou = inter / union return iou def encode_box(self, box, return_iou=True, variances = [0.1, 0.1, 0.2, 0.2]): #---------------------------------------------# # 计算当前真实框和先验框的重合情况 # iou [self.num_anchors] # encoded_box [self.num_anchors, 5] #---------------------------------------------# iou = self.iou(box) encoded_box = np.zeros((self.num_anchors, 4 + return_iou)) #---------------------------------------------# # 找到每一个真实框，重合程度较高的先验框 # 真实框可以由这个先验框来负责预测 #---------------------------------------------# assign_mask = iou > self.overlap_threshold #---------------------------------------------# # 如果没有一个先验框重合度大于self.overlap_threshold # 则选择重合度最大的为正样本 #---------------------------------------------# if not assign_mask.any(): assign_mask[iou.argmax()] = True #---------------------------------------------# # 利用iou进行赋值 #---------------------------------------------# if return_iou: encoded_box[:, -1][assign_mask] = iou[assign_mask] #---------------------------------------------# # 找到对应的先验框 #---------------------------------------------# assigned_anchors = self.anchors[assign_mask] #---------------------------------------------# # 逆向编码，将真实框转化为ssd预测结果的格式 # 先计算真实框的中心与长宽 #---------------------------------------------# box_center = 0.5 * (box[:2] + box[2:]) box_wh = box[2:] - box[:2] #---------------------------------------------# # 再计算重合度较高的先验框的中心与长宽 #---------------------------------------------# assigned_anchors_center = (assigned_anchors[:, 0:2] + assigned_anchors[:, 2:4]) * 0.5 assigned_anchors_wh = (assigned_anchors[:, 2:4] - assigned_anchors[:, 0:2]) #------------------------------------------------# # 逆向求取ssd应该有的预测结果 # 先求取中心的预测结果，再求取宽高的预测结果 # 存在改变数量级的参数，默认为[0.1,0.1,0.2,0.2] #------------------------------------------------# encoded_box[:, :2][assign_mask] = box_center - assigned_anchors_center encoded_box[:, :2][assign_mask] /= assigned_anchors_wh encoded_box[:, :2][assign_mask] /= np.array(variances)[:2] encoded_box[:, 2:4][assign_mask] = np.log(box_wh / assigned_anchors_wh) encoded_box[:, 2:4][assign_mask] /= np.array(variances)[2:4] return encoded_box.ravel() def assign_boxes(self, boxes): #---------------------------------------------------# # assignment分为3个部分 # :4 的内容为网络应该有的回归预测结果 # 4:-1 的内容为先验框所对应的种类，默认为背景 # -1 的内容为当前先验框是否包含目标 #---------------------------------------------------# assignment = np.zeros((self.num_anchors, 4 + self.num_classes + 1)) assignment[:, 4] = 1.0 if len(boxes) == 0: return assignment # 对每一个真实框都进行iou计算 encoded_boxes = np.apply_along_axis(self.encode_box, 1, boxes[:, :4]) #---------------------------------------------------# # 在reshape后，获得的encoded_boxes的shape为： # [num_true_box, num_anchors, 4 + 1] # 4是编码后的结果，1为iou #---------------------------------------------------# encoded_boxes = encoded_boxes.reshape(-1, self.num_anchors, 5) #---------------------------------------------------# # [num_anchors]求取每一个先验框重合度最大的真实框 #---------------------------------------------------# best_iou = encoded_boxes[:, :, -1].max(axis=0) best_iou_idx = encoded_boxes[:, :, -1].argmax(axis=0) best_iou_mask = best_iou > 0 best_iou_idx = best_iou_idx[best_iou_mask] #---------------------------------------------------# # 计算一共有多少先验框满足需求 #---------------------------------------------------# assign_num = len(best_iou_idx) # 将编码后的真实框取出 encoded_boxes = encoded_boxes[:, best_iou_mask, :] #---------------------------------------------------# # 编码后的真实框的赋值 #---------------------------------------------------# assignment[:, :4][best_iou_mask] = encoded_boxes[best_iou_idx,np.arange(assign_num),:4] #----------------------------------------------------------# # 4代表为背景的概率，设定为0，因为这些先验框有对应的物体 #----------------------------------------------------------# assignment[:, 4][best_iou_mask] = 0 assignment[:, 5:-1][best_iou_mask] = boxes[best_iou_idx, 4:] #----------------------------------------------------------# # -1表示先验框是否有对应的物体 #----------------------------------------------------------# assignment[:, -1][best_iou_mask] = 1 # 通过assign_boxes我们就获得了，输入进来的这张图片，应该有的预测结果是什么样子的 return assignment