From c495a0f6b13b794bab9f6e3423d5038ce645d816 Mon Sep 17 00:00:00 2001
From: natanielruiz <nataniel777@hotmail.com>
Date: 星期三, 13 九月 2017 21:12:59 +0800
Subject: [PATCH] Batch testing and hopenet training complete
---
practice/.ipynb_checkpoints/load_AFLW-Copy1-checkpoint.ipynb | 55 ++++++++++++++++++++++++++++++++++++++++---------------
1 files changed, 40 insertions(+), 15 deletions(-)
diff --git a/practice/.ipynb_checkpoints/load_AFLW-Copy1-checkpoint.ipynb b/practice/.ipynb_checkpoints/load_AFLW-Copy1-checkpoint.ipynb
index edac84d..5c0a9b6 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": 1,
+ "execution_count": 6,
"metadata": {
"collapsed": true
},
@@ -23,7 +23,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 7,
"metadata": {
"collapsed": true
},
@@ -31,16 +31,28 @@
"source": [
"#Change this paths according to your directories\n",
"images_path = \"/Data/nruiz9/data/facial_landmarks/AFLW/aflw/data/flickr/\"\n",
- "storing_path = \"/Data/nruiz9/data/facial_landmarks/AFLW/aflw_cropped/\""
+ "storing_path = \"/Data/nruiz9/data/facial_landmarks/AFLW/aflw_cropped_loose/\""
]
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 8,
"metadata": {
"collapsed": false
},
- "outputs": [],
+ "outputs": [
+ {
+ "ename": "KeyboardInterrupt",
+ "evalue": "",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-8-1f2606c2a679>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 34\u001b[0m \u001b[0;32mif\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misfile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_path\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 35\u001b[0m \u001b[0;31m#image = cv2.imread(input_path, 0) #load in grayscale\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 36\u001b[0;31m \u001b[0mimage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 38\u001b[0m \u001b[0;31m#Image dimensions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+ ]
+ }
+ ],
"source": [
"#Image counter\n",
"counter = 1\n",
@@ -92,17 +104,19 @@
" face_h = row[8]\n",
"\n",
" #Error correction\n",
- " if(face_x < 0): face_x = 0\n",
- " if(face_y < 0): face_y = 0\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",
+ " 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",
" #Crop the face from the image\n",
- " image_cropped = np.copy(image[face_y:face_y+face_h, face_x:face_x+face_w])\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 = 240\n",
" image_cropped = cv2.resize(image_cropped, (to_size,to_size), interpolation = cv2.INTER_AREA)\n",
@@ -136,6 +150,17 @@
"cell_type": "code",
"execution_count": null,
"metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "print 'test'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
"collapsed": true
},
"outputs": [],
--
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