def __init__(self):
topology = None
with open('/trt_pose/tasks/human_pose/human_pose.json', 'r') as f:
human_pose = json.load(f)
topology = trt_pose.coco.coco_category_to_topology(human_pose)
self._topology = topology
self._MODEL_WEIGHTS = '/trt_pose/tasks/human_pose/resnet18_baseline_att_224x224_A_epoch_249.pth'
self._OPTIMIZED_MODEL = '/trt_pose/tasks/human_pose/resnet18_baseline_att_224x224_A_epoch_249_trt.pth'
self._num_parts = len(human_pose['keypoints'])
self._num_links = len(human_pose['skeleton'])
print('BreathRateDetector: using resnet model')
self._model = trt_pose.models.resnet18_baseline_att(self._num_parts, 2 * self._num_links).cuda().eval()
self._WIDTH = 224
self._HEIGHT = 224
self._data = torch.zeros((1, 3, self._HEIGHT, self._WIDTH)).cuda()
if os.path.exists(self._OPTIMIZED_MODEL) == False:
print('BreathRateDetector: -- Converting TensorRT models. This may takes several minutes...')
self._model.load_state_dict(torch.load(self._MODEL_WEIGHTS))
model_trt = torch2trt.torch2trt(self._model, [self._data], fp16_mode=True, max_workspace_size=1<<25)
torch.save(model_trt.state_dict(), self._OPTIMIZED_MODEL)
print('BreathRateDetector: -- Conversion complete --')
print('BreathRateDetector: loading TRT model.')
self._model_trt = TRTModule()
self._model_trt.load_state_dict(torch.load(self._OPTIMIZED_MODEL))
self._mean = torch.Tensor([0.485, 0.456, 0.406]).cuda()
self._std = torch.Tensor([0.229, 0.224, 0.225]).cuda()
self._device = torch.device('cuda')
self._parse_objects = ParseObjects(topology)
self._draw_objects = DrawObjects(topology)
def main(args):
with open("human_pose.json", "r") as f:
human_pose = json.load(f)
topology = trt_pose.coco.coco_category_to_topology(human_pose)
device = torch.device(
"cuda") if torch.cuda.is_available() else torch.device("cpu")
model = get_model(human_pose, device)
print(f"Running inference on device: {device}")
preprocess = torchvision.transforms.Compose([
torchvision.transforms.ToPILImage(),
torchvision.transforms.Resize((HEIGHT, WIDTH)),
torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225]),
])
parse_objects = ParseObjects(topology)
draw_objects = DrawObjects(topology)
def user_callback(image_data):
start_time = time.monotonic()
tensor_image = preprocess(image_data)
tensor_image = tensor_image.unsqueeze(0)
cmap, paf = model(tensor_image.to(device))
cmap, paf = cmap.detach().cpu(), paf.detach().cpu()
inference_time_ms = (time.monotonic() - start_time) * 1000
print(f"Inference time: {inference_time_ms:.2f}ms")
counts, objects, peaks = parse_objects(
cmap, paf) # , cmap_threshold=0.15, link_threshold=0.15)
draw_objects(image_data, counts, objects, peaks)
return image_data
run_pipeline(
user_callback,
src_frame_rate=args.frame_rate,
src_height=args.source_height,
src_width=args.source_width,
binning_level=args.binning_level,
)
def __init__(self):
self.goal = 0.0 # [angle]
print("Getting Path to package...")
self.follow_people_configfiles_path = rospkg.RosPack().get_path(
'drone_ai') + "/scripts/helpers/trtpose/models"
print("We get the human pose json file that described the human pose")
humanPose_file_path = os.path.join(
rospkg.RosPack().get_path('drone_ai') +
"/scripts/helpers/trtpose/models/", 'human_pose.json')
print("Opening json file")
with open(humanPose_file_path, 'r') as f:
self.human_pose = json.load(f)
print("Creating topology")
self.topology = trt_pose.coco.coco_category_to_topology(
self.human_pose)
#print("Topology====>", self.topology)
self.WIDTH = 640
self.HEIGHT = 480
OPTIMIZED_MODEL = 'resnet18_baseline_att_224x224_A_epoch_249_trt.pth'
#OPTIMIZED_MODEL = 'densenet121_baseline_att_256x256_B_epoch_160_trt.pth'
optimized_model_weights_path = os.path.join(
self.follow_people_configfiles_path, OPTIMIZED_MODEL)
if not os.path.exists(optimized_model_weights_path):
self.__create_optimodel(optimized_model_weights_path)
print("Load the saved model using Torchtrt")
self.model_trt = TRTModule()
self.model_trt.load_state_dict(
torch.load(optimized_model_weights_path))
print("Define the Image processing variables")
self.mean = torch.Tensor([0.485, 0.456, 0.406]).cuda()
self.std = torch.Tensor([0.229, 0.224, 0.225]).cuda()
self.device = torch.device("cuda")
print(
"Classes to parse the object of the NeuralNetwork and draw on the image"
)
self.parse_objects = ParseObjects(self.topology)
self.draw_objects = DrawObjects(self.topology)
def __init__(self, imshow=True):
self.imshow = imshow # display results of pose esimtation
# Torch settings
self.device = torch.device('cuda')
self.mean = torch.Tensor([0.485, 0.456, 0.406]).cuda()
self.std = torch.Tensor([0.229, 0.224, 0.225]).cuda()
self.load_model()
# Setup camera and visuals
self.parse_objects = ParseObjects(self.topology)
self.draw_objects = DrawObjects(self.topology)
self.setup_camera()
# Visualization
self.im = plt.imshow(self.execute({'new': self.camera.value}))
self.ani = FuncAnimation(plt.gcf(), self.update, interval=200)
self.cid = plt.gcf().canvas.mpl_connect("key_press_event", self.close)
self.running = True
plt.show()
def __init__(self):
with open('modules/ai_module/techniques/pose/trt_pose/human_pose.json',
'r') as f:
human_pose = json.load(f)
self.topology = trt_pose.coco.coco_category_to_topology(human_pose)
self.parse_objects = ParseObjects(self.topology)
self.draw_objects = DrawObjects(self.topology)
OPTIMIZED_MODEL = 'modules/ai_module/techniques/pose/trt_pose/resnet18_baseline_att_224x224_A_epoch_249_trt.pth'
self.model_trt = TRTModule()
self.model_trt.load_state_dict(torch.load(OPTIMIZED_MODEL))
log = "POSE Model loaded"
logging.info(log)
print("[", colored("INFO", 'green', attrs=['bold']), " ] " + log)
self.mean = torch.Tensor([0.485, 0.456, 0.406]).cuda()
self.std = torch.Tensor([0.229, 0.224, 0.225]).cuda()
self.device = torch.device('cuda')
log = "Setting CUDA backend"
logging.info(log)
print("[", colored("INFO", 'green', attrs=['bold']), " ] " + log)
def run(self):
fourcc = cv2.VideoWriter_fourcc('m', 'p', '4', 'v')
out_video = cv2.VideoWriter('/tmp/output.mp4', fourcc, self.cap.get(cv2.CAP_PROP_FPS), (640, 480))
count = 0
while self.cap.isOpened() and count < 500:
t = time.time()
ret_val, dst = self.cap.read()
parse_objects = ParseObjects(topology)
draw_objects = DrawObjects(topology)
if ret_val == False:
print("Camera read Error")
break
img = cv2.resize(dst, dsize=(WIDTH, HEIGHT), interpolation=cv2.INTER_AREA)
img = PE.execute(img, dst, t)
cv2.imshow("result", img)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
count += 1
cv2.destroyAllWindows()
out_video.release()
cap.release()
def __init__(self, net, image_dimensions, network_dimensions,
weights_filename, optimize):
with open('models/human_pose.json', 'r') as f:
self.human_pose = json.load(f)
self.topology = coco_category_to_topology(self.human_pose)
self.parse_objects = ParseObjects(self.topology)
self.draw_objects = DrawObjects(self.topology)
self.num_parts = len(self.human_pose['keypoints'])
self.num_links = len(self.human_pose['skeleton'])
self.weights_filename = weights_filename
self.weights_filename_opt = '{0}.trt'.format(weights_filename)
self.model = net(self.num_parts, 2 * self.num_links).cuda().eval()
self.image_dimensions = image_dimensions
self.network_dimensions = network_dimensions
# data = torch.zeros((1, 3, self.height, self.width)).cuda()
if optimize or os.path.exists(self.weights_filename_opt) == False:
logger.info("Optimizing network for trt")
self.model.load_state_dict(torch.load(self.weights_filename))
self.model_trt = torch2trt.torch2trt(
self.model,
[torch.zeros((1, 3, *self.network_dimensions)).cuda()],
fp16_mode=True)
torch.save(self.model_trt.state_dict(), self.weights_filename_opt)
else:
self.model_trt = TRTModule()
self.model_trt.load_state_dict(
torch.load(self.weights_filename_opt))
self.mean = torch.Tensor([0.485, 0.456, 0.406]).cuda()
self.std = torch.Tensor([0.229, 0.224, 0.225]).cuda()
self.device = torch.device('cuda')
self.preprocess = transforms.Compose([
transforms.Resize(self.network_dimensions),
# transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
self.model_trt
])
def init():
import torch2trt
from torch2trt import TRTModule
with open('./models/human_pose.json', 'r') as f:
human_pose = json.load(f)
global topology
topology = coco_category_to_topology(human_pose)
global WIDTH
WIDTH = 256
global HEIGHT
HEIGHT = 256
#data = torch.zeros((1, 3, HEIGHT, WIDTH)).cuda()
OPTIMIZED_MODEL = Path('./models/densenet121_baseline_att_256x256_B_epoch_160_trt.pth')
global model_trt
model_trt = TRTModule()
model_trt.load_state_dict(torch.load(OPTIMIZED_MODEL))
print('loaded model')
global mean
mean = torch.Tensor([0.485, 0.456, 0.406]).cuda()
global std
std = torch.Tensor([0.229, 0.224, 0.225]).cuda()
global device
device = torch.device('cuda')
global parse_objects
parse_objects = ParseObjects(topology)
global draw_objects
draw_objects = DrawObjects(topology)
def __init__(self):
print('------ model = resnet--------')
self.MODEL_WEIGHTS = 'resnet18_baseline_att_224x224_A_epoch_249.pth'
self.OPTIMIZED_MODEL = 'resnet18_baseline_att_224x224_A_epoch_249_trt.pth'
with open('human_pose.json', 'r') as f:
human_pose = json.load(f)
num_parts = len(human_pose['keypoints'])
num_links = len(human_pose['skeleton'])
self.model = trt_pose.models.resnet18_baseline_att(
num_parts, 2 * num_links).cuda().eval()
self.WIDTH = 224
self.HEIGHT = 224
data = torch.zeros((1, 3, self.HEIGHT, self.WIDTH)).cuda()
if os.path.exists(self.OPTIMIZED_MODEL) == False:
self.model.load_state_dict(torch.load(self.MODEL_WEIGHTS))
self.model_trt = torch2trt.torch2trt(self.model, [data],
fp16_mode=True,
max_workspace_size=1 << 25)
torch.save(self.model_trt.state_dict(), self.OPTIMIZED_MODEL)
self.topology = trt_pose.coco.coco_category_to_topology(human_pose)
self.model_trt = TRTModule()
self.model_trt.load_state_dict(torch.load(self.OPTIMIZED_MODEL))
self.parse_objects = ParseObjects(self.topology)
self.draw_objects = DrawObjects(self.topology)
model.load_state_dict(torch.load(MODEL_WEIGHTS))
# Optimization with tensorRT
# NOTE: optimization is device specific
# data = torch.zeros((1, 3, HEIGHT, WIDTH)).cuda()
# model_trt = torch2trt.torch2trt(model, [data], fp16_mode=True, max_workspace_size=1<<25)
# torch.save(model_trt.state_dict(), OPTIMIZED_MODEL)
# Load optimized model
print("Loading optimized model")
model_trt = TRTModule()
model_trt.load_state_dict(torch.load(OPTIMIZED_MODEL))
# Setup camera and visuals
parse_objects = ParseObjects(topology)
draw_objects = DrawObjects(topology)
camera = USBCamera(width=WIDTH, height=HEIGHT, capture_device=1)
camera.running = True
# Attach oberver to act on each new frame received
# camera.observe(execute, names='value')
im = plt.imshow(execute({'new': camera.value}))
ani = FuncAnimation(plt.gcf(), update, interval=200)
cid = plt.gcf().canvas.mpl_connect("key_press_event", close)
plt.show()
def main():
liveDemo = True
if liveDemo:
# Set up USB camera (assuming device 0)
cam = USBCamera(width=1920, height=1080, capture_device=0)
# Set up model for yolov5s
yolo = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)
device = torch.device('cuda')
yolo.to(device)
# Set up topology, model, and classes for ResNet
with open('human_pose.json', 'r') as f:
human_pose = json.load(f)
topology = trt_pose.coco.coco_category_to_topology(human_pose)
resnet = TRTModule()
resnet.load_state_dict(torch.load('resnet_trt.pth'))
parseObjects = ParseObjects(topology)
drawObjects = DrawObjects(topology)
# Basic analytics
imageCount = 0
t = time()
# Live demo on webcam
if liveDemo:
# Continue until interrupt
try:
while True:
# Grab a frame
img = cam.read()[:, :, ::-1] # Convert BGR to RGB
print(f'got frame {imageCount}')
# Process with yolo and resnet
result, empty = processFrame(img, yolo, resnet, parseObjects,
drawObjects)
# Save file
cv2.imwrite(f'imgs/{imageCount:04}.jpg', result)
imageCount += 1
except KeyboardInterrupt:
print('Keyboard interrupt!')
finally:
t = time() - t
# Recorded video
else:
cap = cv2.VideoCapture('example_video.mpg')
# Grab a frame
ret, frame = cap.read()
# Continue until video is done
while ret:
# Process and save image
img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
result, empty = processFrame(img, yolo, resnet, parseObjects,
drawObjects)
cv2.imwrite(f'imgs/{imageCount:04}.jpg', result)
# Try to grab next frame
ret, frame = cap.read()
imageCount += 1
t = time() - t
cap.release()
print(
f'Ending. Processed {imageCount} images in {t}s, average FPS of {imageCount/t}'
)
def __init__(self, root):
# Context variable declarations and loading
self.running = False
self.WIDTH = 224
self.HEIGHT = 224
self.thresh = 127
self.round = 0
self.minimum_joints = 4
self.path = './images/'
self.mdelay_sec = 10
self.mtick = self.mdelay_sec
self.mask = None
self.calibrate = True # Flag to show calibration pose over camera feed
self.calibration_pose = cv2.imread('./images/cal_pose.jpg', cv2.IMREAD_COLOR)
#Loading model and model data
with open('./tasks/human_pose/human_pose.json', 'r') as f:
human_pose = json.load(f)
self.topology = trt_pose.coco.coco_category_to_topology(human_pose)
self.num_parts = len(human_pose['keypoints'])
self.num_links = len(human_pose['skeleton'])
self.data = torch.zeros((1, 3, self.HEIGHT, self.WIDTH)).cuda()
self.OPTIMIZED_MODEL = './tasks/human_pose/resnet18_baseline_att_224x224_A_epoch_249_trt.pth'
self.model_trt = TRTModule()
self.model_trt.load_state_dict(torch.load(self.OPTIMIZED_MODEL))
self.mean = torch.Tensor([0.485, 0.456, 0.406]).cuda()
self.std = torch.Tensor([0.229, 0.224, 0.225]).cuda()
self.device = torch.device('cuda')
self.parse_objects = ParseObjects(self.topology)
self.draw_objects = DrawObjects(self.topology)
# Start camera
if USBCam:
self.camera = USBCamera(width=self.WIDTH, height=self.HEIGHT, capture_fps=30)
else:
self.camera = CSICamera(width=self.WIDTH, height=self.HEIGHT, capture_fps=30)
self.frame=Tk.Frame(root)
self.root=root
# Create editable title
self.titleVar = Tk.StringVar()
self.title= Tk.Label(root, textvariable=self.titleVar, font="Verdana 36")
self.titleVar.set("Pose Estimation Game")
self.title.pack(side=Tk.TOP)
self.frame.pack(side=Tk.LEFT, fill=Tk.BOTH, expand=1)
# Create image capture figure
# Set as Frame with three possible images (live feed, mask/pose to make, image captured)
# Image row
self.im_row = Tk.Frame(self.frame)
self.feed_label = Tk.Label(self.im_row)
self.feed_label.pack(side=Tk.LEFT)
self.mask_label = Tk.Label(self.im_row)
self.pose_label = Tk.Label(self.im_row)
# Create editable description label
self.desTextVar = "Please select an option from the right"
self.desText = Tk.Label(self.frame, text=self.desTextVar, font="Verdana 12")
#Create Combobox for selection (Steps are currently in comments)
#Grab maps from repository
#Parse map names to develop choices
#group map names into array
self.levels = []
choices = ["Easy", "Medium", "Hard"]
#Put map names in combo box
self.ddVar = Tk.StringVar()
self.ddVar.set('Select a Choice')
self.dropDown = Tk.OptionMenu(self.frame, self.ddVar, *choices)
# This function binds a function that loads all images for level upon the selection of
# an option in the dropdown menu
self.ddVar.trace('w', self.levels_select)
# Create inital button panel
self.buttonPanel = ButtonPanel(root)
self.im_row.pack()
self.desText.pack()
self.buttonPanel.pack()
self.root.after(10, self.camera_loop)
MainGUI.updateToTitle(self)
