github
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Jun 13 10:24:03 2021
@author: ledi
"""
import pickle
import numpy as np
import tensorflow as tf
# data_output = open('output_0.pkl','wb')
# pickle.dump(output_0,data_output)
# data_output.close()
'''
if n_a= n_b=13
aa=bb=Out[297]:
<tf.Tensor: shape=(13, 13), dtype=int32, numpy=
array([[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[ 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],
[ 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],
[ 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],
[ 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],
[ 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6],
[ 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7],
[ 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],
[ 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9],
[10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10],
[11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11],
[12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12]], dtype=int32)>
'''
def _meshgrid(n_a, n_b):
aa=tf.reshape(tf.tile(tf.range(n_a), [n_b]), (n_b, n_a))
bb=tf.reshape(tf.repeat(tf.range(n_b), n_a), (n_b, n_a))
return [aa,bb]
def yolo_boxes(pred, anchors, classes):
# pred=output_0
# anchors=anchors[masks[0]]
# pred: (batch_size, grid, grid, anchors, (x, y, w, h, obj, ...classes))
#获取特征的维度
#在yolov3 中有下面三种维度
'''
grid_size= tf.Tensor([13 13], shape=(2,), dtype=int32)
grid_size= tf.Tensor([26 26], shape=(2,), dtype=int32)
grid_size= tf.Tensor([52 52], shape=(2,), dtype=int32)
'''
grid_size = tf.shape(pred)[1:3] #13*13
#将85 维度的向量分割成 2+2+1+classes
box_xy, box_wh, objectness, class_probs = tf.split(
pred, (2, 2, 1, classes), axis=-1)
#中点坐标
box_xy = tf.sigmoid(box_xy)
#置信度
objectness = tf.sigmoid(objectness)
#softmax分类
class_probs = tf.sigmoid(class_probs)
pred_box = tf.concat((box_xy, box_wh), axis=-1) # original xywh for loss
# !!! grid[x][y] == (y, x)
grid = _meshgrid(grid_size[1],grid_size[0])
grid = tf.expand_dims(tf.stack(grid, axis=-1), axis=2) # [gx, gy, 1, 2]
#https://www.shuzhiduo.com/A/qVdeERkndP/
'''
tf.cast(box_xy,tf.float32).shape
TensorShape([5, 13, 13, 3, 2])
tf.cast(grid, tf.float32).shape
TensorShape([13, 13, 1, 2])
'''
# vv=tf.cast(box_xy,tf.float32).shape=TensorShape([5, 13, 13, 3, 2])
# cc=tf.cast(grid, tf.float32).shape= TensorShape([13, 13, 1, 2])
#k=vv[0]+cc
#box_xy_shift[0]==k
#每一个grid 负责一次检测
##grid 为偏移 ，将x,y相对于featuremap尺寸进行了归一化
box_xy_shift= (tf.cast(box_xy,tf.float32) + tf.cast(grid, tf.float32))
box_xy = box_xy_shift / tf.cast(grid_size, tf.float32)
box_wh = tf.exp(box_wh) * anchors/ tf.cast(grid_size, tf.float32)
box_x1y1 = box_xy - box_wh / 2
box_x2y2 = box_xy + box_wh / 2
bbox = tf.concat([box_x1y1, box_x2y2], axis=-1)
return bbox, objectness, class_probs, pred_box
anchors0 = np.array([(10, 13), (16, 30), (33, 23), (30, 61), (62, 45),
(59, 119), (116, 90), (156, 198), (373, 326)],
np.float32) / 416
masks = np.array([[6, 7, 8], [3, 4, 5], [0, 1, 2]])
anchors=anchors0[masks[0]]
# rb 以二进制读取
data_input = open('output_0.pkl','rb')
pred= pickle.load(data_input)
data_input.close()
classes=80
pm=pred.numpy()
pred=pred/pm.max()
cc=yolo_boxes(pred, anchors, classes)
print(cc)
