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tensorflow2

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This commit is contained in:
Daniel Saavedra
2020-03-25 18:23:00 -03:00
parent 7010af8a58
commit 7cf0c577a1
25 changed files with 1016 additions and 309 deletions
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31
keras-yolo3-master/utils/utils.py
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@@ -3,6 +3,7 @@ import numpy as np
import os
from .bbox import BoundBox, bbox_iou
from scipy.special import expit
import tensorflow as tf
def _sigmoid(x):
return expit(x)
@@ -166,18 +167,30 @@ def do_nms(boxes, nms_thresh):
if bbox_iou(boxes[index_i], boxes[index_j]) >= nms_thresh:
boxes[index_j].classes[c] = 0
def decode_netout(netout, anchors, obj_thresh, net_h, net_w):
grid_h, grid_w = netout.shape[:2]
def decode_netout(netout_old, anchors, obj_thresh, net_h, net_w):
grid_h, grid_w = netout_old.shape[:2]
nb_box = 3
netout = netout.reshape((grid_h, grid_w, nb_box, -1))
nb_class = netout.shape[-1] - 5
#netout = netout.reshape((grid_h, grid_w, nb_box, -1))
netout_old = tf.reshape(netout_old, (grid_h, grid_w, nb_box, -1))
nb_class = netout_old.shape[-1] - 5
boxes = []
## Tensorflow v.2
#print(tf.shape(netout))
aux_1 = _sigmoid(netout_old[..., :2])
#print(tf.shape(aux_1))
aux_2 = _sigmoid(netout_old[..., 4])
#print(tf.shape(aux_2[..., np.newaxis]))
aux_3 = aux_2[..., np.newaxis] * _softmax(netout_old[..., 5:])
aux_4 = aux_3 * (aux_3 > obj_thresh)
#print(tf.shape(aux_4))
netout = tf.concat([aux_1,netout_old[..., 2:4] ,aux_2[..., np.newaxis], aux_4], 3)
#print(tf.shape(new_netout))
netout[..., :2] = _sigmoid(netout[..., :2])
netout[..., 4] = _sigmoid(netout[..., 4])
netout[..., 5:] = netout[..., 4][..., np.newaxis] * _softmax(netout[..., 5:])
netout[..., 5:] *= netout[..., 5:] > obj_thresh
#netout[..., :2] = _sigmoid(netout[..., :2])
#netout[..., 4] = _sigmoid(netout[..., 4])
#netout[..., 5:] = netout[..., 4][..., np.newaxis] * _softmax(netout[..., 5:])
#netout[..., 5:] *= netout[..., 5:] > obj_thresh
for i in range(grid_h*grid_w):
row = i // grid_w
@@ -198,7 +211,7 @@ def decode_netout(netout, anchors, obj_thresh, net_h, net_w):
h = anchors[2 * b + 1] * np.exp(h) / net_h # unit: image height
# last elements are class probabilities
classes = netout[row,col,b,5:]
classes = np.array(netout[row,col,b,5:])
box = BoundBox(x-w/2, y-h/2, x+w/2, y+h/2, objectness, classes)