From c13dba86b2dbe581353b72602d7fa6e40991964c Mon Sep 17 00:00:00 2001
From: natanielruiz <nataniel777@hotmail.com>
Date: 星期三, 27 九月 2017 04:11:23 +0800
Subject: [PATCH] next
---
practice/.ipynb_checkpoints/load_AFLW-Copy1-checkpoint.ipynb | 44 ++++++++++++++++++++++++++++----------------
1 files changed, 28 insertions(+), 16 deletions(-)
diff --git a/practice/.ipynb_checkpoints/load_AFLW-Copy1-checkpoint.ipynb b/practice/.ipynb_checkpoints/load_AFLW-Copy1-checkpoint.ipynb
index 6c632b6..856fbb4 100644
--- a/practice/.ipynb_checkpoints/load_AFLW-Copy1-checkpoint.ipynb
+++ b/practice/.ipynb_checkpoints/load_AFLW-Copy1-checkpoint.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 1,
"metadata": {
"collapsed": true
},
@@ -23,7 +23,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 2,
"metadata": {
"collapsed": true
},
@@ -36,7 +36,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 4,
"metadata": {
"collapsed": false
},
@@ -100,28 +100,21 @@
" face_h = row[8]\n",
"\n",
" #Error correction\n",
- " k = 0.15\n",
- " x_min = face_x - image_w * k\n",
- " x_max = face_x + image_w * (k+1)\n",
- " y_min = face_y - image_h * k\n",
- " y_max = face_y + image_h * (k+1)\n",
+ " k = 0.35\n",
+ " x_min = face_x - face_w * k * 0.6\n",
+ " x_max = face_x + face_w + face_w * k * 0.6\n",
+ " y_min = face_y - face_h * k * 2\n",
+ " y_max = face_y + face_h + face_h * k * 0.6\n",
" \n",
" x_min = int(max(0, x_min))\n",
" x_max = int(min(image_w, x_max))\n",
" y_min = int(max(0, y_min))\n",
" y_max = int(min(image_h, y_max))\n",
- "\n",
- " if(face_w > image_w): \n",
- " face_w = image_w\n",
- " face_h = image_w\n",
- " if(face_h > image_h): \n",
- " face_h = image_h\n",
- " face_w = image_h\n",
" \n",
" #Crop the face from the image\n",
" image_cropped = np.copy(image[y_min:y_max, x_min:x_max])\n",
" #Uncomment the lines below if you want to rescale the image to a particular size\n",
- " to_size = 260\n",
+ " to_size = 240\n",
" image_cropped = cv2.resize(image_cropped, (to_size,to_size), interpolation = cv2.INTER_AREA)\n",
" #Uncomment the line below if you want to use adaptive histogram normalisation\n",
" #clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(5,5))\n",
@@ -151,6 +144,25 @@
},
{
"cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "test\n"
+ ]
+ }
+ ],
+ "source": [
+ "print 'test'"
+ ]
+ },
+ {
+ "cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
--
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