From 31fc66b795c0a57b8009d7b03f49f6cd099ceb29 Mon Sep 17 00:00:00 2001
From: natanielruiz <nataniel777@hotmail.com>
Date: 星期六, 23 九月 2017 12:07:48 +0800
Subject: [PATCH] Trying superres
---
code/test_on_video.py | 44 +++++++++++++++++++++++++++++++++++++-------
1 files changed, 37 insertions(+), 7 deletions(-)
diff --git a/code/test_on_video.py b/code/test_on_video.py
index 4fad440..c837775 100644
--- a/code/test_on_video.py
+++ b/code/test_on_video.py
@@ -45,8 +45,10 @@
if not os.path.exists(args.video_path):
sys.exit('Video does not exist')
- # ResNet50 with 3 outputs.
- model = hopenet.Hopenet(torchvision.models.resnet.Bottleneck, [3, 4, 6, 3], 66)
+ # 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)
# ResNet18
# model = hopenet.Hopenet(torchvision.models.resnet.BasicBlock, [2, 2, 2, 2], 66)
@@ -58,7 +60,8 @@
print 'Loading data.'
transformations = transforms.Compose([transforms.Scale(224),
- transforms.RandomCrop(224), transforms.ToTensor()])
+ transforms.CenterCrop(224), transforms.ToTensor(),
+ transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
model.cuda(gpu)
@@ -72,12 +75,24 @@
idx_tensor = torch.FloatTensor(idx_tensor).cuda(gpu)
video = cv2.VideoCapture(video_path)
- width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH)) # float
- height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT)) # float
+
+ # New cv2
+ # width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH)) # float
+ # height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT)) # float
+ #
+ # # Define the codec and create VideoWriter object
+ # fourcc = cv2.VideoWriter_fourcc(*'MJPG')
+ # out = cv2.VideoWriter('output/video/output-%s.avi' % args.output_string, fourcc, 30.0, (width, height))
+
+ # Old cv2
+ width = int(video.get(cv2.cv.CV_CAP_PROP_FRAME_WIDTH)) # float
+ height = int(video.get(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT)) # float
# Define the codec and create VideoWriter object
- fourcc = cv2.VideoWriter_fourcc(*'MJPG')
+ fourcc = cv2.cv.CV_FOURCC(*'MJPG')
out = cv2.VideoWriter('output/video/output-%s.avi' % args.output_string, fourcc, 30.0, (width, height))
+
+ txt_out = open('output/video/output-%s.txt' % args.output_string, 'w')
bbox_file = open(args.bboxes, 'r')
frame_num = 1
@@ -95,6 +110,7 @@
out.release()
video.release()
bbox_file.close()
+ txt_out.close()
sys.exit(0)
# Save all frames as they are if they don't have bbox annotation.
@@ -104,6 +120,7 @@
out.release()
video.release()
bbox_file.close()
+ txt_out.close()
sys.exit(0)
out.write(frame)
frame_num += 1
@@ -113,9 +130,18 @@
out.release()
video.release()
bbox_file.close()
+ txt_out.close()
sys.exit(0)
x_min, y_min, x_max, y_max = int(line[1]), int(line[2]), int(line[3]), int(line[4])
+ x_min -= 150
+ x_max += 150
+ y_min -= 250
+ y_max += 100
+ x_min = max(x_min, 0)
+ y_min = max(y_min, 0)
+ x_max = min(frame.shape[1], x_max)
+ y_max = min(frame.shape[0], y_max)
# Crop image
img = frame[y_min:y_max,x_min:x_max]
img = Image.fromarray(img)
@@ -125,7 +151,7 @@
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 = model(img)
+ yaw, pitch, roll, angles = model(img)
yaw_predicted = F.softmax(yaw)
pitch_predicted = F.softmax(pitch)
@@ -136,7 +162,10 @@
roll_predicted = torch.sum(roll_predicted.data[0] * idx_tensor) * 3 - 99
# Print new frame with cube and TODO: 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 = 200)
+ # Plot expanded bounding box
+ cv2.rectangle(frame, (x_min, y_min), (x_max, y_max), (0,255,0), 3)
out.write(frame)
frame_num += 1
@@ -147,6 +176,7 @@
out.release()
video.release()
bbox_file.close()
+ txt_out.close()
sys.exit(0)
out.write(frame)
frame_num += 1
--
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