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yolo_predict.py
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yolo_predict.py
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import os
import config
import random
import colorsys
import numpy as np
import tensorflow as tf
from model.yolo3_model import yolo
class yolo_predictor:
def __init__(self, obj_threshold, nms_threshold, classes_file, anchors_file):
"""
Introduction
------------
初始化函数
Parameters
----------
obj_threshold: 目标检测为物体的阈值
nms_threshold: nms阈值
"""
self.obj_threshold = obj_threshold
self.nms_threshold = nms_threshold
self.classes_path = classes_file
self.anchors_path = anchors_file
self.class_names = self._get_class()
self.anchors = self._get_anchors()
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))
random.seed(10101)
random.shuffle(self.colors)
random.seed(None)
def _get_class(self):
"""
Introduction
------------
读取类别名称
"""
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
def _get_anchors(self):
"""
Introduction
------------
读取anchors数据
"""
anchors_path = os.path.expanduser(self.anchors_path)
with open(anchors_path) as f:
anchors = f.readline()
anchors = [float(x) for x in anchors.split(',')]
anchors = np.array(anchors).reshape(-1, 2)
return anchors
def eval(self, yolo_outputs, image_shape, max_boxes = 20):
"""
Introduction
------------
根据Yolo模型的输出进行非极大值抑制，获取最后的物体检测框和物体检测类别
Parameters
----------
yolo_outputs: yolo模型输出
image_shape: 图片的大小
max_boxes: 最大box数量
Returns
-------
boxes_: 物体框的位置
scores_: 物体类别的概率
classes_: 物体类别
"""
anchor_mask = [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
boxes = []
box_scores = []
input_shape = tf.shape(yolo_outputs[0])[1 : 3] * 32
# 对三个尺度的输出获取每个预测box坐标和box的分数，score计算为置信度x类别概率
for i in range(len(yolo_outputs)):
_boxes, _box_scores = self.boxes_and_scores(yolo_outputs[i], self.anchors[anchor_mask[i]], len(self.class_names), input_shape, image_shape)
boxes.append(_boxes)
box_scores.append(_box_scores)
boxes = tf.concat(boxes, axis = 0)
box_scores = tf.concat(box_scores, axis = 0)
mask = box_scores >= self.obj_threshold
max_boxes_tensor = tf.constant(max_boxes, dtype = tf.int32)
boxes_ = []
scores_ = []
classes_ = []
for c in range(len(self.class_names)):
class_boxes = tf.boolean_mask(boxes, mask[:, c])
class_box_scores = tf.boolean_mask(box_scores[:, c], mask[:, c])
nms_index = tf.image.non_max_suppression(class_boxes, class_box_scores, max_boxes_tensor, iou_threshold = self.nms_threshold)
class_boxes = tf.gather(class_boxes, nms_index)
class_box_scores = tf.gather(class_box_scores, nms_index)
classes = tf.ones_like(class_box_scores, 'int32') * c
boxes_.append(class_boxes)
scores_.append(class_box_scores)
classes_.append(classes)
boxes_ = tf.concat(boxes_, axis = 0)
scores_ = tf.concat(scores_, axis = 0)
classes_ = tf.concat(classes_, axis = 0)
return boxes_, scores_, classes_
def boxes_and_scores(self, feats, anchors, classes_num, input_shape, image_shape):
"""
Introduction
------------
将预测出的box坐标转换为对应原图的坐标，然后计算每个box的分数
Parameters
----------
feats: yolo输出的feature map
anchors: anchor的位置
class_num: 类别数目
input_shape: 输入大小
image_shape: 图片大小
Returns
-------
boxes: 物体框的位置
boxes_scores: 物体框的分数，为置信度和类别概率的乘积
"""
box_xy, box_wh, box_confidence, box_class_probs = self._get_feats(feats, anchors, classes_num, input_shape)
boxes = self.correct_boxes(box_xy, box_wh, input_shape, image_shape)
boxes = tf.reshape(boxes, [-1, 4])
box_scores = box_confidence * box_class_probs
box_scores = tf.reshape(box_scores, [-1, classes_num])
return boxes, box_scores
def correct_boxes(self, box_xy, box_wh, input_shape, image_shape):
"""
Introduction
------------
计算物体框预测坐标在原图中的位置坐标
Parameters
----------
box_xy: 物体框左上角坐标
box_wh: 物体框的宽高
input_shape: 输入的大小
image_shape: 图片的大小
Returns
-------
boxes: 物体框的位置
"""
box_yx = box_xy[..., ::-1]
box_hw = box_wh[..., ::-1]
input_shape = tf.cast(input_shape, dtype = tf.float32)
image_shape = tf.cast(image_shape, dtype = tf.float32)
new_shape = tf.round(image_shape * tf.reduce_min(input_shape / image_shape))
offset = (input_shape - new_shape) / 2. / input_shape
scale = input_shape / new_shape
box_yx = (box_yx - offset) * scale
box_hw *= scale
box_mins = box_yx - (box_hw / 2.)
box_maxes = box_yx + (box_hw / 2.)
boxes = tf.concat([
box_mins[..., 0:1],
box_mins[..., 1:2],
box_maxes[..., 0:1],
box_maxes[..., 1:2]
], axis = -1)
boxes *= tf.concat([image_shape, image_shape], axis = -1)
return boxes
def _get_feats(self, feats, anchors, num_classes, input_shape):
"""
Introduction
------------
根据yolo最后一层的输出确定bounding box
Parameters
----------
feats: yolo模型最后一层输出
anchors: anchors的位置
num_classes: 类别数量
input_shape: 输入大小
Returns
-------
box_xy, box_wh, box_confidence, box_class_probs
"""
num_anchors = len(anchors)
anchors_tensor = tf.reshape(tf.constant(anchors, dtype=tf.float32), [1, 1, 1, num_anchors, 2])
grid_size = tf.shape(feats)[1:3]
predictions = tf.reshape(feats, [-1, grid_size[0], grid_size[1], num_anchors, num_classes + 5])
# 这里构建13*13*1*2的矩阵，对应每个格子加上对应的坐标
grid_y = tf.tile(tf.reshape(tf.range(grid_size[0]), [-1, 1, 1, 1]), [1, grid_size[1], 1, 1])
grid_x = tf.tile(tf.reshape(tf.range(grid_size[1]), [1, -1, 1, 1]), [grid_size[0], 1, 1, 1])
grid = tf.concat([grid_x, grid_y], axis = -1)
grid = tf.cast(grid, tf.float32)
# 将x,y坐标归一化为占416的比例
box_xy = (tf.sigmoid(predictions[..., :2]) + grid) / tf.cast(grid_size[::-1], tf.float32)
# 将w,h也归一化为占416的比例
box_wh = tf.exp(predictions[..., 2:4]) * anchors_tensor / tf.cast(input_shape[::-1], tf.float32)
box_confidence = tf.sigmoid(predictions[..., 4:5])
box_class_probs = tf.sigmoid(predictions[..., 5:])
return box_xy, box_wh, box_confidence, box_class_probs
def predict(self, inputs, image_shape):
"""
Introduction
------------
构建预测模型
Parameters
----------
inputs: 处理之后的输入图片
image_shape: 图像原始大小
Returns
-------
boxes: 物体框坐标
scores: 物体概率值
classes: 物体类别
"""
model = yolo(config.norm_epsilon, config.norm_decay, self.anchors_path, self.classes_path, pre_train = False)
output = model.yolo_inference(inputs, config.num_anchors // 3, config.num_classes, training = False)
boxes, scores, classes = self.eval(output, image_shape, max_boxes = 20)
return boxes, scores, classes