From f111cb002b9c6065fdf6bb274ce5857a9e875e8c Mon Sep 17 00:00:00 2001
From: chenshijun <csj_sky@126.com>
Date: 星期三, 05 六月 2019 15:38:49 +0800
Subject: [PATCH] face rectangle
---
code/test_alexnet.py | 45 ++++++++++++++++++++++-----------------------
1 files changed, 22 insertions(+), 23 deletions(-)
diff --git a/code/test_alexnet.py b/code/test_alexnet.py
index d9cc0a3..45ad25f 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."""
@@ -48,33 +45,35 @@
model = hopenet.AlexNet(66)
- print 'Loading snapshot.'
+ print('Loading snapshot.')
# Load snapshot
saved_state_dict = torch.load(snapshot_path)
model.load_state_dict(saved_state_dict)
- print 'Loading data.'
+ print('Loading data.')
transformations = transforms.Compose([transforms.Scale(224),
transforms.CenterCrop(224), transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
- if args.dataset == 'AFLW2000':
- pose_dataset = datasets.AFLW2000(args.data_dir, args.filename_list,
- transformations)
+ if args.dataset == 'Pose_300W_LP':
+ pose_dataset = datasets.Pose_300W_LP(args.data_dir, args.filename_list, transformations)
+ elif args.dataset == 'Pose_300W_LP_random_ds':
+ pose_dataset = datasets.Pose_300W_LP_random_ds(args.data_dir, args.filename_list, transformations)
+ elif args.dataset == 'AFLW2000':
+ pose_dataset = datasets.AFLW2000(args.data_dir, args.filename_list, transformations)
elif args.dataset == 'AFLW2000_ds':
- pose_dataset = datasets.AFLW2000_ds(args.data_dir, args.filename_list,
- transformations)
+ pose_dataset = datasets.AFLW2000_ds(args.data_dir, args.filename_list, transformations)
elif args.dataset == 'BIWI':
pose_dataset = datasets.BIWI(args.data_dir, args.filename_list, transformations)
elif args.dataset == 'AFLW':
pose_dataset = datasets.AFLW(args.data_dir, args.filename_list, transformations)
- elif args.dataset == 'Pose_300W_LP':
- pose_dataset = datasets.Pose_300W_LP(args.data_dir, args.filename_list, transformations)
+ elif args.dataset == 'AFLW_aug':
+ pose_dataset = datasets.AFLW_aug(args.data_dir, args.filename_list, transformations)
elif args.dataset == 'AFW':
pose_dataset = datasets.AFW(args.data_dir, args.filename_list, transformations)
else:
- print 'Error: not a valid dataset name'
+ print('Error: not a valid dataset name')
sys.exit()
test_loader = torch.utils.data.DataLoader(dataset=pose_dataset,
batch_size=args.batch_size,
@@ -82,7 +81,7 @@
model.cuda(gpu)
- print 'Ready to test network.'
+ print ('Ready to test network.')
# Test the Model
model.eval() # Change model to 'eval' mode (BN uses moving mean/var).
@@ -125,8 +124,7 @@
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':
@@ -136,7 +134,8 @@
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) +
--
Gitblit v1.8.0