From 4b67b5c8ed5566ec3030d537536282e830d87e40 Mon Sep 17 00:00:00 2001
From: natanielruiz <nruiz9@gatech.edu>
Date: 星期一, 30 十月 2017 07:15:49 +0800
Subject: [PATCH] next
---
code/test_on_video_noconf.py | 21 +++++++++++----------
1 files changed, 11 insertions(+), 10 deletions(-)
diff --git a/code/test_on_video_noconf.py b/code/test_on_video_noconf.py
index e040de7..a76a396 100644
--- a/code/test_on_video_noconf.py
+++ b/code/test_on_video_noconf.py
@@ -1,4 +1,9 @@
+import sys, os, argparse
+
import numpy as np
+import cv2
+import matplotlib.pyplot as plt
+
import torch
import torch.nn as nn
from torch.autograd import Variable
@@ -8,10 +13,6 @@
import torchvision
import torch.nn.functional as F
from PIL import Image
-
-import cv2
-import matplotlib.pyplot as plt
-import sys, os, argparse
import datasets, hopenet, utils
@@ -47,10 +48,8 @@
if not os.path.exists(args.video_path):
sys.exit('Video does not exist')
- # ResNet101 with 3 outputs.
- # model = hopenet.Hopenet(torchvision.models.resnet.Bottleneck, [3, 4, 23, 3], 66)
- # ResNet50
- model = hopenet.Hopenet(torchvision.models.resnet.Bottleneck, [3, 4, 6, 3], 66, 0)
+ # ResNet50 structure
+ model = hopenet.Hopenet(torchvision.models.resnet.Bottleneck, [3, 4, 6, 3], 66)
print 'Loading snapshot.'
# Load snapshot
@@ -145,7 +144,7 @@
y_min = max(y_min, 0)
x_max = min(frame.shape[1], x_max)
y_max = min(frame.shape[0], y_max)
- # Crop image
+ # Crop face loosely
img = frame[y_min:y_max,x_min:x_max]
img = Image.fromarray(img)
@@ -154,7 +153,8 @@
img_shape = img.size()
img = img.view(1, img_shape[0], img_shape[1], img_shape[2])
img = Variable(img).cuda(gpu)
- yaw, pitch, roll, angles = model(img)
+
+ yaw, pitch, roll = model(img)
yaw_predicted = F.softmax(yaw)
pitch_predicted = F.softmax(pitch)
@@ -163,6 +163,7 @@
yaw_predicted = torch.sum(yaw_predicted.data[0] * idx_tensor) * 3 - 99
pitch_predicted = torch.sum(pitch_predicted.data[0] * idx_tensor) * 3 - 99
roll_predicted = torch.sum(roll_predicted.data[0] * idx_tensor) * 3 - 99
+
# Print new frame with cube and axis
txt_out.write(str(frame_num) + ' %f %f %f\n' % (yaw_predicted, pitch_predicted, roll_predicted))
# utils.plot_pose_cube(frame, yaw_predicted, pitch_predicted, roll_predicted, (x_min + x_max) / 2, (y_min + y_max) / 2, size = bbox_width)
--
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