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_on_video_dlib.py | 20 ++++++++++++--------
1 files changed, 12 insertions(+), 8 deletions(-)
diff --git a/code/test_on_video_dlib.py b/code/test_on_video_dlib.py
index 7f651f7..8ae74fa 100644
--- a/code/test_on_video_dlib.py
+++ b/code/test_on_video_dlib.py
@@ -58,12 +58,12 @@
# Dlib face detection model
cnn_face_detector = dlib.cnn_face_detection_model_v1(args.face_model)
- 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(),
@@ -71,13 +71,13 @@
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).
total = 0
- idx_tensor = [idx for idx in xrange(66)]
+ idx_tensor = [idx for idx in range(66)]
idx_tensor = torch.FloatTensor(idx_tensor).cuda(gpu)
video = cv2.VideoCapture(video_path)
@@ -103,18 +103,22 @@
frame_num = 1
while frame_num <= args.n_frames:
- print frame_num
+ print(frame_num)
ret,frame = video.read()
+
if ret == False:
break
-
+
+ frame = cv2.resize(frame, (960, 540), interpolation=cv2.INTER_LINEAR)
+ print(frame.shape)
cv2_frame = cv2.cvtColor(frame,cv2.COLOR_BGR2RGB)
# Dlib detect
dets = cnn_face_detector(cv2_frame, 1)
for idx, det in enumerate(dets):
+ print('**********************yes****************************yes********************************yes************************************')
# Get x_min, y_min, x_max, y_max, conf
x_min = det.rect.left()
y_min = det.rect.top()
@@ -132,7 +136,7 @@
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 = cv2_frame[y_min:y_max,x_min:x_max]
+ img = cv2_frame[int(y_min):int(y_max),int(x_min):int(x_max)]
img = Image.fromarray(img)
# Transform
@@ -156,7 +160,7 @@
# utils.plot_pose_cube(frame, yaw_predicted, pitch_predicted, roll_predicted, (x_min + x_max) / 2, (y_min + y_max) / 2, size = bbox_width)
utils.draw_axis(frame, yaw_predicted, pitch_predicted, roll_predicted, tdx = (x_min + x_max) / 2, tdy= (y_min + y_max) / 2, size = bbox_height/2)
# Plot expanded bounding box
- cv2.rectangle(frame, (x_min, y_min), (x_max, y_max), (0,255,0), 1)
+ # cv2.rectangle(frame, (x_min, y_min), (x_max, y_max), (0,255,0), 1)
out.write(frame)
frame_num += 1
--
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