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vision_grasping.py
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vision_grasping.py
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import argparse
import sys
import signal
import os
import time
import yaml
import jetson.inference
import jetson.utils
import numpy as np
import cv2 as cv
import gripper_control as gripper
from datetime import datetime
from realsense_wapper import realsense
from franka.FrankaController import FrankaController
def read_cfg(path):
with open(path, 'r') as stream:
out = yaml.safe_load(stream)
return out
def load_cam_T_base_matrix(file_path):
H = np.loadtxt(file_path, delimiter = ',')
cam_T_base_R = H[:3, :3]
cam_T_base_t = H[:3, 3:].squeeze(1)
return cam_T_base_R, cam_T_base_t
if __name__ == '__main__':
# parse the command line
parser = argparse.ArgumentParser(description="Locate objects in a live camera stream using an object detection DNN.",
formatter_class=argparse.RawTextHelpFormatter, epilog=jetson.inference.detectNet.Usage() +
jetson.utils.videoSource.Usage() + jetson.utils.videoOutput.Usage() + jetson.utils.logUsage())
parser.add_argument("--network", type=str, default="coco-bottle", help="pre-trained model to load (see below for options)")
parser.add_argument("--overlay", type=str, default="box,labels,conf", help="detection overlay flags (e.g. --overlay=box,labels,conf)\nvalid combinations are: 'box', 'labels', 'conf', 'none'")
parser.add_argument("--threshold", type=float, default=0.5, help="minimum detection threshold to use")
is_headless = ["--headless"] if sys.argv[0].find('console.py') != -1 else [""]
try:
opt = parser.parse_known_args()[0]
except:
print("")
parser.print_help()
sys.exit(0)
ROOT = os.path.dirname(os.path.abspath(__file__))
sys.path.append(ROOT)
cfg = read_cfg('config/grasping.yaml')
arm = FrankaController(ROOT + '/config/franka.yaml')
cam = realsense(frame_width = cfg['width'], frame_height = cfg['height'], fps = cfg['fps'])
net = jetson.inference.detectNet(opt.network, sys.argv, opt.threshold) # load the object detection network
time_evaluate = cfg['time_evaluate']
initial_pose = cfg['initial_position']
drop_position = cfg['drop_position']
conf_threshold = cfg['conf_threshold']
is_logging = cfg['log']
R, t = load_cam_T_base_matrix(cfg['matrix_path'])
print("Load R, t from file:\nR:\n", R, "\nt:\n", t)
grasp_pre_offset = cfg['grasp_prepare_offset']
effector_offset = cfg['effector_offset']
detection_turncation = cfg['detection_turncation']
grasp_width = cfg['grasp_width']
# display = jetson.utils.glDisplay()
print("Moving to initial position...")
arm.move_p(initial_pose)
print("Moving to initial position... Done")
if(is_logging):
current_log_dir = ROOT + '/log/' + str(datetime.now()).replace(' ', '-')
os.mkdir(current_log_dir)
print("Set log dir to " + current_log_dir)
while(True):
if(time_evaluate):
t0 = time.time()
# Get img from realsense in Numpy array format
depth_img, color_img = cam.get_frame_cv()
# Numpy array can only be accessed by cpu
# Copy color img to GPU for network inference
color_img_cuda = jetson.utils.cudaFromNumpy(color_img)
# allocate gpu memory for network input image as rgba32f, with the same width/height as the color frame
network_input_img = jetson.utils.cudaAllocMapped(width = cam.color_frame_width, height = cam.color_frame_height, format='rgba32f')
# convert from rgb8 (default format for realsense color frame in this program) to rgba32f
jetson.utils.cudaConvertColor(color_img_cuda, network_input_img)
if(time_evaluate):
print("Time to convert from numpy array to cuda: ", time.time() - t0)
# detect objects in the image (with overlay)
detections = net.Detect(network_input_img, cam.color_frame_width, cam.color_frame_height, opt.overlay)
visual_img = cv.cvtColor(jetson.utils.cudaToNumpy(network_input_img), cv.COLOR_RGBA2BGR)
visual_img = visual_img.astype(np.uint8)
# print(visual_img)
cv.imshow("Result", visual_img)
cv.waitKey(100)
# print the detections
print("detected {:d} objects in image".format(len(detections)))
#for detection in detections:
#print(detection)
# render the image
# Turn off due to X11 error with opengl
# display.RenderOnce(network_input_img, cam.color_frame_width, cam.color_frame_height)
# update the title bar
# display.SetTitle("{:s} | Network {:.0f} FPS".format(opt.network, net.GetNetworkFPS()))
# print out performance info
if(time_evaluate):
net.PrintProfilerTimes()
if(len(detections) != 0):
for detection in detections:
if detection.Confidence > conf_threshold:
print(detection)
obj_center_row = int(detection.Center[1])
obj_center_col = int(detection.Center[0])
if(obj_center_col < detection_turncation):
print("Discard detection result: in turncation area")
continue
if(is_logging):
log_img = cv.cvtColor(color_img, cv.COLOR_RGB2BGR)
cv.circle(log_img, (obj_center_col, obj_center_row), 20, (0, 0, 255), thickness = 3)
cv.imwrite(current_log_dir + '/' + str(time.time()) + '.jpg', log_img)
# compute target coordinate in camera frame
target_in_cam_z = depth_img[obj_center_row, obj_center_col] * cam.depth_scale
target_in_cam_x = np.multiply(obj_center_col - cam.intrinsics['cx'], target_in_cam_z / cam.intrinsics['fx'])
target_in_cam_y = np.multiply(obj_center_row - cam.intrinsics['cy'], target_in_cam_z / cam.intrinsics['fy'])
print("Target in camera frame:\n", [target_in_cam_x, target_in_cam_y, target_in_cam_z])
target_in_cam = np.array([target_in_cam_x, target_in_cam_y, target_in_cam_z])
target_in_base = R.dot(target_in_cam) + t
print("Target in base frame:\n", target_in_base)
prepare_pos = [target_in_base[0], target_in_base[1], target_in_base[2] + grasp_pre_offset + effector_offset, 3.14, 0, 0]
arm.move_p(prepare_pos)
gripper.gripper_open()
arm.move_p([target_in_base[0], target_in_base[1], target_in_base[2] + effector_offset, 3.14, 0, 0])
gripper.gripper_close()
# Move to drop position and drop object
arm.move_p(initial_pose)
arm.move_p(drop_position)
gripper.gripper_open()
# Back to initial position
arm.move_p(initial_pose)
# pause for a bit
# time.sleep(3)