From 2c764e41e2fde6244b87da58d12c40d09a14fcb4 Mon Sep 17 00:00:00 2001
From: natanielruiz <nruiz9@gatech.edu>
Date: 星期一, 30 十月 2017 06:49:01 +0800
Subject: [PATCH] Next
---
code/test_alexnet.py | 21 ++++------
code/test_on_video.py | 24 +++++-------
code/test_on_video_noconf.py | 29 ++++++--------
3 files changed, 32 insertions(+), 42 deletions(-)
diff --git a/code/test_alexnet.py b/code/test_alexnet.py
index 7a3989a..529d566 100644
--- a/code/test_alexnet.py
+++ b/code/test_alexnet.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
@@ -8,15 +13,7 @@
import torchvision
import torch.nn.functional as F
-import cv2
-import matplotlib.pyplot as plt
-import sys
-import os
-import argparse
-
-import datasets
-import hopenet
-import utils
+import datasets, hopenet, utils
def parse_args():
"""Parse input arguments."""
@@ -134,8 +131,7 @@
pitch_error += torch.sum(torch.abs(pitch_predicted - label_pitch))
roll_error += torch.sum(torch.abs(roll_predicted - label_roll))
- # Save images with pose cube.
- # TODO: fix for larger batch size
+ # Save first image in batch with pose cube or axis.
if args.save_viz:
name = name[0]
if args.dataset == 'BIWI':
@@ -145,7 +141,8 @@
if args.batch_size == 1:
error_string = 'y %.2f, p %.2f, r %.2f' % (torch.sum(torch.abs(yaw_predicted - label_yaw)), torch.sum(torch.abs(pitch_predicted - label_pitch)), torch.sum(torch.abs(roll_predicted - label_roll)))
cv2.putText(cv2_img, error_string, (30, cv2_img.shape[0]- 30), fontFace=1, fontScale=1, color=(0,0,255), thickness=1)
- utils.plot_pose_cube(cv2_img, yaw_predicted[0], pitch_predicted[0], roll_predicted[0])
+ # utils.plot_pose_cube(cv2_img, yaw_predicted[0], pitch_predicted[0], roll_predicted[0], size=100)
+ utils.draw_axis(cv2_img, yaw_predicted[0], pitch_predicted[0], roll_predicted[0], tdx = 200, tdy= 200, size=100)
cv2.imwrite(os.path.join('output/images', name + '.jpg'), cv2_img)
print('Test error in degrees of the model on the ' + str(total) +
diff --git a/code/test_on_video.py b/code/test_on_video.py
index 38fe798..c4172da 100644
--- a/code/test_on_video.py
+++ b/code/test_on_video.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
@@ -46,10 +47,8 @@
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)
+ model = hopenet.Hopenet(torchvision.models.resnet.Bottleneck, [3, 4, 6, 3], 66)
print 'Loading snapshot.'
# Load snapshot
@@ -154,15 +153,12 @@
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_predicted = F.softmax(yaw)
- pitch_predicted = F.softmax(pitch)
- roll_predicted = F.softmax(roll)
- # Get continuous predictions in degrees.
- 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
+ yaw_predicted = angles[:,0].data[0].cpu()
+ pitch_predicted = angles[:,1].data[0].cpu()
+ roll_predicted = angles[:,2].data[0].cpu()
# 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)
diff --git a/code/test_on_video_noconf.py b/code/test_on_video_noconf.py
index e040de7..b6a8d2c 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)
@@ -154,15 +153,13 @@
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_predicted = F.softmax(yaw)
- pitch_predicted = F.softmax(pitch)
- roll_predicted = F.softmax(roll)
- # Get continuous predictions in degrees.
- 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
+ yaw_predicted = angles[:,0].data[0].cpu()
+ pitch_predicted = angles[:,1].data[0].cpu()
+ roll_predicted = angles[:,2].data[0].cpu()
+
# 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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