def test():
# cpu or gpu?
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print('using device {}'.format(device))
# load net
net = Net()
net.to(device)
net.load_state_dict(torch.load('trained_net.pt'))
net.train(False)
# sample data
val_idx = np.load('val_idx.npy')
val_sampler = SubsetRandomSampler(val_idx)
pose_dataset = PoseDataset('panoptic_dataset.pickle')
val_loader = DataLoader(dataset=pose_dataset,
batch_size=1,
sampler=val_sampler)
while True:
data_iter = iter(val_loader)
skel_2d, skel_z = next(data_iter)
print(skel_2d)
# inference
skel_2d = skel_2d.to(device)
z_out = net(skel_2d)
# show
skel_2d = skel_2d.cpu().numpy()
skel_2d = skel_2d.reshape((2, -1), order='F') # [(x,y) x n_joint]
z_out = z_out.detach().cpu().numpy()
z_out = z_out.reshape(-1)
z_gt = skel_z.numpy().reshape(-1)
show_skeletons(skel_2d, z_out, z_gt)
def show_camera(args):
# To flip the image, modify the flip_method parameter (0 and 2 are the most common)
print(gstreamer_pipeline(flip_method=0))
use_cuda = torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")
model = Net()
model.load_state_dict(torch.load(args.model_path))
model = model.to(device)
font = cv2.FONT_HERSHEY_SIMPLEX
fontScale = 1
org = (30, 50)
color = (0, 0, 255)
thickness = 2
cap = cv2.VideoCapture(gstreamer_pipeline(flip_method=0),
cv2.CAP_GSTREAMER)
if cap.isOpened():
window_handle = cv2.namedWindow('Camera', cv2.WINDOW_AUTOSIZE)
cv2.namedWindow('image', cv2.WINDOW_AUTOSIZE)
cv2.namedWindow('inputs', cv2.WINDOW_AUTOSIZE)
# Window
while cv2.getWindowProperty('Camera', 0) >= 0:
ret_val, img = cap.read()
# Transform inputs
image0, image, inputs = transform_inputs(img, device)
# Run Model Evaluation
output = model(inputs)
index = output.data.cpu().numpy().argmax()
cv2.putText(img, str(index), org, font, fontScale, color,
thickness, cv2.LINE_AA)
cv2.imshow('Camera', img)
cv2.imshow("image", cv2.resize(image0, (200, 200)))
cv2.imshow("inputs", cv2.resize(image, (200, 200)))
# This also acts as
keyCode = cv2.waitKey(30) & 0xff
# Stop the program on the ESC key
if keyCode == 27:
break
cap.release()
cv2.destroyAllWindows()
else:
print('Unable to open camera')
test_path = 'D:\\DeapLearn Project\\ License plate recognition\\single_num\\resize\\'
test_data = MyDataSet(test_path)
new_test_loader = DataLoader(test_data,
batch_size=32,
shuffle=False,
pin_memory=True,
num_workers=0)
# 创造一个一模一样的模型
model = Net()
# 加载预训练模型的参数
model.load_state_dict(torch.load('Model.pth'))
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model.to(device)
def test():
correct = 0
total = 0
with torch.no_grad():
for _, data in enumerate(new_test_loader, 0):
inputs, _ = data[0], data[1]
inputs = inputs.to(device)
outputs = model(inputs)
# print(outputs.shape)
_, prediction = torch.max(outputs.data, dim=1)
print('-' * 40)
# print(target)
# print(prediction)
def show_camera(args):
# To flip the image, modify the flip_method parameter (0 and 2 are the most common)
print(gstreamer_pipeline(flip_method=0))
use_cuda = torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")
model = Net()
model.load_state_dict(torch.load(args.model_path))
model = model.to(device)
font = cv2.FONT_HERSHEY_SIMPLEX
fontScale = 1
org = (30, 50)
color = (0, 0, 255)
thickness = 2
cap = cv2.VideoCapture(gstreamer_pipeline(flip_method=0), cv2.CAP_GSTREAMER)
if cap.isOpened():
window_handle = cv2.namedWindow('Camera', cv2.WINDOW_AUTOSIZE)
# Window
while cv2.getWindowProperty('Camera',0) >= 0:
ret_val, img = cap.read()
# Convert to grayscale and apply Gaussian filtering
im_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
im_gray = cv2.GaussianBlur(im_gray, (5, 5), 0)
# Threshold the image
ret, im_th = cv2.threshold(im_gray, 90, 255, cv2.THRESH_BINARY_INV)
# Find contours in the image
im2, ctrs, hier = cv2.findContours(im_th, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# Get rectangles contains each contour
rects = [cv2.boundingRect(ctr) for ctr in ctrs]
# For each rectangular region, calculate HOG features and predict
# the digit using Linear SVM.
for rect in rects:
# Draw the rectangles
cv2.rectangle(img, (rect[0], rect[1]), (rect[0] + rect[2], rect[1] + rect[3]), (0, 255, 0), 3)
# Make the rectangular region around the digit
leng = int(rect[3] * 1.6)
pt1 = int(rect[1] + rect[3] // 2 - leng // 2)
pt2 = int(rect[0] + rect[2] // 2 - leng // 2)
roi = im_gray[pt1:pt1+leng, pt2:pt2+leng]
# Resize the image
h, w = roi.shape
if h > 10 and w > 10:
# Transform inputs
inputs = transform_inputs(roi, device)
# Run Model Evaluation
output = model(inputs)
result = output.data.cpu().numpy().argmax()
cv2.putText(img, str(result), (rect[0], rect[1]),cv2.FONT_HERSHEY_DUPLEX, 2, (0, 255, 255), 3)
cv2.imshow("Camera", img)
# This also acts as
keyCode = cv2.waitKey(30) & 0xff
# Stop the program on the ESC key
if keyCode == 27:
break
cap.release()
cv2.destroyAllWindows()
else:
print('Unable to open camera')
class Inferencing():
def __init__(self, network_path, dataset_path, validation_set):
self.network_path = network_path
self.dataset_path = dataset_path
self.validation_set = validation_set
self.device =\
torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
self.net = Net()
self.net.to(self.device)
self.net.load_state_dict(
torch.load(self.network_path, map_location='cpu'))
self.net.train(False)
def get3Dcoordinates(self, skel_2d):
"""Returns Z(3D) coordinates of a 2D pose"""
skel_2d = skel_2d.to(self.device)
z_out = self.net(skel_2d)
z_out = z_out.detach().cpu().numpy()
z_out = z_out.reshape(-1)
return z_out
def testSample(self):
"""Inferences on 5 random samples."""
val_idx = np.load(self.validation_set)
val_sampler = SubsetRandomSampler(val_idx)
pose_dataset = PoseDataset(self.dataset_path)
val_loader = DataLoader(dataset=pose_dataset, batch_size=1,\
sampler=val_sampler)
for i in range(5):
data_iter = iter(val_loader)
skel_2d, skel_z = next(data_iter)
# inference
skel_2d = skel_2d.to(self.device)
z_out = self.net(skel_2d)
# show
skel_2d = skel_2d.cpu().numpy()
skel_2d = skel_2d.reshape((2, -1), order='F') # [(x,y) x n_joint]
z_out = z_out.detach().cpu().numpy()
z_out = z_out.reshape(-1)
z_gt = skel_z.numpy().reshape(-1)
self.show_skeletons(skel_2d, z_out, z_gt)
def show_skeletons(self, skel_2d, z_out, z_gt=None):
"""Show skeleton in 2D and 3D, includes full upper body and headself.
Keyword Arguments:
skel_2d - skeleton with x,y coordinates
z_out - predicted z coordinates (for 3d)
z_gt - ground truth z coordinates
"""
fig = plt.figure(figsize=(20, 20))
ax1 = fig.add_subplot(1, 2, 1)
ax2 = fig.add_subplot(1, 2, 2, projection='3d')
edges = np.array([[1, 0], [0, 2], [2, 3], [3, 4], [0, 5], [5, 6],
[6, 7]])
ax_2d = ax1
ax_3d = ax2
# draw 3d
for edge in edges:
ax_3d.plot(skel_2d[0, edge],
z_out[edge],
skel_2d[1, edge],
color='r')
if z_gt is not None:
ax_3d.plot(skel_2d[0, edge],
z_gt[edge],
skel_2d[1, edge],
color='g')
ax_3d.set_aspect('equal')
ax_3d.set_xlabel("x"), ax_3d.set_ylabel("z"), ax_3d.set_zlabel("y")
ax_3d.set_xlim3d([-2,
2]), ax_3d.set_ylim3d([2, -2
]), ax_3d.set_zlim3d([2, -2])
ax_3d.view_init(elev=10, azim=-45)
# draw 2d
for edge in edges:
ax_2d.plot(skel_2d[0, edge], skel_2d[1, edge], color='r')
ax_2d.set_aspect('equal')
ax_2d.set_xlabel("x"), ax_2d.set_ylabel("y")
ax_2d.set_xlim([-2, 2]), ax_2d.set_ylim([2, -2])
plt.show()
