From b74b9c54247177c82493f180617d7551de8e2bb1 Mon Sep 17 00:00:00 2001
From: natanielruiz <nataniel777@hotmail.com>
Date: 星期二, 26 九月 2017 03:47:06 +0800
Subject: [PATCH] Before SR experiments
---
practice/.ipynb_checkpoints/smoothing_ypr-checkpoint.ipynb | 50 +++++++++++++++++++++++++++++---------------------
1 files changed, 29 insertions(+), 21 deletions(-)
diff --git a/practice/.ipynb_checkpoints/smoothing_ypr-checkpoint.ipynb b/practice/.ipynb_checkpoints/smoothing_ypr-checkpoint.ipynb
index a411c30..cd62e38 100644
--- a/practice/.ipynb_checkpoints/smoothing_ypr-checkpoint.ipynb
+++ b/practice/.ipynb_checkpoints/smoothing_ypr-checkpoint.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 156,
+ "execution_count": 1,
"metadata": {
"collapsed": false
},
@@ -17,22 +17,22 @@
},
{
"cell_type": "code",
- "execution_count": 157,
+ "execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
- "video_path = '../data/video/SGT036_2016_07_25_pivothead_AVI.avi'\n",
- "bbox_path = '../data/video/annotations/SGT036_childface.txt'\n",
+ "video_path = '../data/video/SWC016_2016_04_08_pivothead_AVI.avi'\n",
+ "bbox_path = '../data/video/annotations/SWC016_childface.txt'\n",
"\n",
- "annot_path = '../output/video/output-SGT036_resnet18_cr_epoch_1.txt'\n",
- "output_string = 'SGT036_resnet18_cr_epoch_1_flat_smoothed'"
+ "annot_path = '../output/video/output-SWC016_normal_1e-5_epoch_20.txt'\n",
+ "output_string = 'SWC016_normal_1e-5_epoch_20_smooth'"
]
},
{
"cell_type": "code",
- "execution_count": 158,
+ "execution_count": 3,
"metadata": {
"collapsed": false
},
@@ -41,9 +41,9 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "[-6.069214 -0.831665 0.53318 ..., -3.836042 -3.868275 -2.377155]\n",
- "(8508,)\n",
- "(53464,)\n"
+ "[ -1.15884 -14.885593 -16.727406 ..., -57.590378 -56.963156 -58.641377]\n",
+ "(9322,)\n",
+ "(63691,)\n"
]
}
],
@@ -93,7 +93,7 @@
},
{
"cell_type": "code",
- "execution_count": 159,
+ "execution_count": 4,
"metadata": {
"collapsed": false
},
@@ -107,31 +107,39 @@
}
],
"source": [
- "window_len = 6\n",
+ "window_len = 7\n",
"pad = window_len / 2\n",
"window = 'flat'\n",
+ "window_2 = 'flat'\n",
+ "window_len_2 = 7\n",
+ "pad_2 = window_len_2 / 2\n",
"\n",
"s = np.r_[y[window_len-1:0:-1],y,y[-2:-window_len-1:-1]]\n",
"t = np.r_[p[window_len-1:0:-1],p,p[-2:-window_len-1:-1]]\n",
"u = np.r_[r[window_len-1:0:-1],r,r[-2:-window_len-1:-1]]\n",
"\n",
- "xa = np.r_[x_min_arr[window_len-1:0:-1],x_min_arr,x_min_arr[-2:-window_len-1:-1]]\n",
- "xb = np.r_[x_max_arr[window_len-1:0:-1],x_max_arr,x_max_arr[-2:-window_len-1:-1]]\n",
- "ya = np.r_[y_min_arr[window_len-1:0:-1],y_min_arr,y_min_arr[-2:-window_len-1:-1]]\n",
- "yb = np.r_[y_max_arr[window_len-1:0:-1],y_max_arr,y_max_arr[-2:-window_len-1:-1]]\n",
+ "xa = np.r_[x_min_arr[window_len_2-1:0:-1],x_min_arr,x_min_arr[-2:-window_len_2-1:-1]]\n",
+ "xb = np.r_[x_max_arr[window_len_2-1:0:-1],x_max_arr,x_max_arr[-2:-window_len_2-1:-1]]\n",
+ "ya = np.r_[y_min_arr[window_len_2-1:0:-1],y_min_arr,y_min_arr[-2:-window_len_2-1:-1]]\n",
+ "yb = np.r_[y_max_arr[window_len_2-1:0:-1],y_max_arr,y_max_arr[-2:-window_len_2-1:-1]]\n",
"\n",
"if window == 'flat':\n",
" w=np.ones(window_len, 'd')\n",
"else:\n",
" w=eval('np.' + window + '(window_len)')\n",
+ " \n",
+ "if window_2 == 'flat':\n",
+ " w_2=np.ones(window_len_2, 'd')\n",
+ "else:\n",
+ " w_2=eval('np.' + window_2 + '(window_len_2)') \n",
"\n",
"y = np.convolve(w / w.sum(), s, mode='valid')[pad:-pad]\n",
"p = np.convolve(w / w.sum(), t, mode='valid')[pad:-pad]\n",
"r = np.convolve(w / w.sum(), u, mode='valid')[pad:-pad]\n",
- "x_min_arr = np.convolve(w / w.sum(), xa, mode='valid')[pad:-pad]\n",
- "x_max_arr = np.convolve(w / w.sum(), xb, mode='valid')[pad:-pad]\n",
- "y_min_arr = np.convolve(w / w.sum(), ya, mode='valid')[pad:-pad]\n",
- "y_max_arr = np.convolve(w / w.sum(), yb, mode='valid')[pad:-pad]\n",
+ "x_min_arr = np.convolve(w_2 / w_2.sum(), xa, mode='valid')[pad_2:-pad_2]\n",
+ "x_max_arr = np.convolve(w_2 / w_2.sum(), xb, mode='valid')[pad_2:-pad_2]\n",
+ "y_min_arr = np.convolve(w_2 / w_2.sum(), ya, mode='valid')[pad_2:-pad_2]\n",
+ "y_max_arr = np.convolve(w_2 / w_2.sum(), yb, mode='valid')[pad_2:-pad_2]\n",
"\n",
"pose_dict = {}\n",
"bbox_dict = {}\n",
@@ -151,7 +159,7 @@
},
{
"cell_type": "code",
- "execution_count": 160,
+ "execution_count": 7,
"metadata": {
"collapsed": false
},
--
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