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yolo_servo_perception.py
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yolo_servo_perception.py
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import numpy as np
import cv2
from ultralytics import YOLO
import aruco_detector as ad
import aruco_to_fingertips as af
import yaml
from yaml.loader import SafeLoader
import d405_helpers_without_pyrealsense as dh
from copy import deepcopy
def draw_text(image, origin, text_lines):
font = cv2.FONT_HERSHEY_SIMPLEX
font_size = 0.5
location = origin + np.array([0, -55])
location = location.astype(np.int32)
for i, line in enumerate(text_lines):
text_size = cv2.getTextSize(line, font, font_size, 4)
(text_width, text_height), text_baseline = text_size
center = int(text_width / 2)
offset = np.array([-center, i * (1.7*text_height)]).astype(np.int32)
cv2.putText(image, line, location + offset, font, font_size, (0, 0, 0), 4, cv2.LINE_AA)
cv2.putText(image, line, location + offset, font, font_size, (255, 255, 255), 1, cv2.LINE_AA)
class YoloServoPerception():
def __init__(self, model_name=None):
self.camera_info = None
self.depth_scale = None
if model_name is not None:
self.model_name = model_name
else:
self.model_name = 'yolov8n-seg.pt'
print('YoloServoPerception: self.model_name =', self.model_name)
self.masks_available = False
if 'seg' in self.model_name:
self.masks_available = True
self.model = YOLO(self.model_name)
self.example_tennis_ball_diameter = 0.0658
self.marker_info = {}
with open('aruco_marker_info.yaml') as f:
self.marker_info = yaml.load(f, Loader=SafeLoader)
self.aruco_detector = ad.ArucoDetector(marker_info=self.marker_info, show_debug_images=False, use_apriltag_refinement=False, brighten_images=False)
self.fingertip_part = 'cup_top' #'cup_bottom'
self.aruco_to_fingertips = af.ArucoToFingertips(default_height_above_mounting_surface=af.suctioncup_height[self.fingertip_part])
def set_camera_parameters(self, camera_info, depth_scale):
self.camera_info = camera_info
self.depth_scale = depth_scale
def apply(self, color_image, depth_image):
assert ((self.camera_info is not None) and (self.depth_scale is not None)), 'ERROR: YoloServoPerception: set_camera_parameters must be called prior to apply. self.camera_info or self.depth_scale is None'
self.aruco_detector.update(color_image, self.camera_info)
markers = self.aruco_detector.get_detected_marker_dict()
fingertips = self.aruco_to_fingertips.get_fingertips(markers)
display_received_images = False
if display_received_images:
cv2.imshow('Received RGB Image', color_image)
cv2.imshow('Received Depth Image', depth_image)
conf = 0.1 #0.25 is the default threshold
yolo_results = self.model.predict(color_image, conf=conf)[0]
names = yolo_results.names
yolo_output = []
best_ball = None
boxes = yolo_results.boxes.cpu().numpy()
if len(boxes) > 0:
masks = None
if self.masks_available:
masks = yolo_results.masks.xy
for i, box in enumerate(boxes):
class_name = names[box.cls[0]]
if class_name in ['apple', 'sports ball']:
box_min_x, box_min_y, box_max_x, box_max_y = box.xyxy[0]
box_width_x = box_max_x - box_min_x
box_width_y = box_max_y - box_min_y
max_box_side_pix = max(box_width_x, box_width_y)
if masks is not None:
# Find the boundaries of the mask
int_mask = masks[i].astype(np.int32)
min_mask = np.min(int_mask, axis=0)
max_mask = np.max(int_mask, axis=0)
mask_min_x, mask_min_y = min_mask
mask_max_x, mask_max_y = max_mask
mask_width_x = mask_max_x - mask_min_x
mask_width_y = mask_max_y - mask_min_y
display_crop = False
crop_rgb = False
if crop_rgb:
ball_crop = color_image[mask_min_y:mask_max_y, mask_min_x:mask_max_x, :]
if display_crop:
cv2.imshow('Ball RGB Crop', ball_crop)
# Crop the depth image around the ball
ball_depth_crop = depth_image[mask_min_y:mask_max_y, mask_min_x:mask_max_x]
if display_crop:
cv2.imshow('Ball Depth Crop', ball_depth_crop)
# Create segmentation mask for cropped region
crop_polygon = masks[i] - [mask_min_x, mask_min_y]
mask_crop = np.zeros_like(ball_depth_crop, np.uint8)
cv2.fillPoly(mask_crop, [crop_polygon.astype(np.int32)], 255)
if display_crop:
cv2.imshow('Ball Mask', mask_crop)
# Find the estimated depth across the mask
estimated_depth = np.mean(ball_depth_crop[mask_crop > 0])
estimated_z_m = estimated_depth * self.depth_scale
else:
# Use the bounding box to estimate the
# range instead of a segmentation
# mask.
box_min_x_int, box_min_y_int, box_max_x_int, box_max_y_int = box.xyxy[0].astype(np.int32)
display_crop = False
crop_rgb = False
if crop_rgb:
ball_crop = color_image[box_min_y_int:box_max_y_int, box_min_x_int:box_max_x_int, :]
if display_crop:
cv2.imshow('Ball RGB Crop', ball_crop)
# Crop the depth image around the ball
ball_depth_crop = depth_image[box_min_y_int:box_max_y_int, box_min_x_int:box_max_x_int]
if display_crop:
cv2.imshow('Ball Depth Crop', ball_depth_crop)
# Find the estimated depth across the mask
estimated_depth = np.percentile(ball_depth_crop, 50)
estimated_z_m = estimated_depth * self.depth_scale
# Compute the 3D grasp point
center_pix = np.array([(box_max_x + box_min_x)/2.0, (box_max_y + box_min_y)/2.0])
left_side_pix = np.array([box_min_x, (box_max_y + box_min_y)/2.0])
right_side_pix = np.array([box_max_x, (box_max_y + box_min_y)/2.0])
width_pix = box_max_x - box_min_x
center_xyz = dh.pixel_to_3d(center_pix, estimated_z_m , self.camera_info)
center_ray = center_xyz / np.linalg.norm(center_xyz)
left_side_xyz = dh.pixel_to_3d(left_side_pix, estimated_z_m, self.camera_info)
right_side_xyz = dh.pixel_to_3d(right_side_pix, estimated_z_m, self.camera_info)
width_m = np.linalg.norm(right_side_xyz - left_side_xyz)
grasp_depth = width_m / 2.0
grasp_center_xyz = center_xyz + (grasp_depth * center_ray)
if (best_ball is None) or (best_ball['max_box_side_pix'] < max_box_side_pix):
best_ball = {
'name': class_name,
'max_box_side_pix' : max_box_side_pix,
'confidence': box.conf[0],
'width_m': width_m,
'width_pix': width_pix,
'estimated_z_m': estimated_z_m,
'grasp_center_xyz': grasp_center_xyz,
'left_side_xyz': left_side_xyz,
'left_side_pix': left_side_pix,
'right_side_xyz': right_side_xyz,
'right_side_pix': right_side_pix,
'box': {'min_x': box_min_x,
'min_y': box_min_y,
'max_x': box_max_x,
'max_y': box_max_y}
}
if masks is not None:
best_ball['mask'] = masks[i]
display_yolo_results_image = False
if display_yolo_results_image:
results_image = yolo_results.plot()
cv2.imshow('YOLOv8 result', results_image)
if best_ball is not None:
# minimize the object before sending
minimal_ball = deepcopy(best_ball)
# Pixel information is not currently useful for
# visual servoing using remote visual processing,
# since the local visual servoing process does not
# have access to the D405.
del minimal_ball['max_box_side_pix']
if self.masks_available:
del minimal_ball['mask']
del minimal_ball['width_pix']
del minimal_ball['left_side_pix']
del minimal_ball['right_side_pix']
del minimal_ball['box']
yolo_output.append(minimal_ball)
send_dict = {
'fingertips': fingertips,
'yolo': yolo_output
}
display_task_relevant_image = True
if display_task_relevant_image:
task_relevant_image = np.copy(color_image)
self.aruco_to_fingertips.draw_fingertip_frames(send_dict['fingertips'],
task_relevant_image,
self.camera_info,
axis_length_in_m=0.02,
draw_origins=True,
write_coordinates=True)
ball = best_ball
if ball is not None:
if self.masks_available:
mask = ball['mask']
brighten_mask = np.ones(task_relevant_image.shape[:2], np.float32)
#cv2.polylines(task_relevant_image, [mask.astype(np.int32)], True, (0,0,255), 3, lineType=cv2.LINE_AA)
cv2.polylines(brighten_mask, [mask.astype(np.int32)], True, 4.0, 3, lineType=cv2.LINE_AA)
cv2.fillPoly(brighten_mask, [mask.astype(np.int32)], 2.0, lineType=cv2.LINE_AA)
task_relevant_image[:,:,0] = np.minimum(brighten_mask * task_relevant_image[:,:,0], 255).astype(np.uint8)
task_relevant_image[:,:,1] = np.minimum(brighten_mask * task_relevant_image[:,:,1], 255).astype(np.uint8)
task_relevant_image[:,:,2] = np.minimum(brighten_mask * task_relevant_image[:,:,2], 255).astype(np.uint8)
display_bounding_box = False
if display_bounding_box:
box = ball['box']
x0 = box['min_x']
x1 = box['max_x']
y0 = box['min_y']
y1 = box['max_y']
lines = np.array([[x0,y0], [x1,y0], [x1,y1], [x0,y1]]).astype(np.int32)
cv2.polylines(task_relevant_image, [lines], True, (255,0,0), 2)
middle = np.round(np.array([(x1+x0)/2.0, (y1+y0)/2.0])).astype(np.int32)
display_sides = False
if display_sides:
left_side_color = (0,255,0)
right_side_color = (255, 0, 0)
side_thickness = 2
side_length = ball['width_pix']
left_side_pix = ball['left_side_pix']
left_side_start = np.copy(left_side_pix)
left_side_start[1] = left_side_start[1] + side_length/2.0
left_side_end = np.copy(left_side_pix)
left_side_end[1] = left_side_end[1] - side_length/2.0
cv2.line(task_relevant_image,
left_side_start.astype(np.int32),
left_side_end.astype(np.int32),
left_side_color, side_thickness)
right_side_pix = ball['right_side_pix']
right_side_start = np.copy(right_side_pix)
right_side_start[1] = right_side_start[1] + side_length/2.0
right_side_end = np.copy(right_side_pix)
right_side_end[1] = right_side_end[1] - side_length/2.0
cv2.line(task_relevant_image,
right_side_start.astype(np.int32),
right_side_end.astype(np.int32),
right_side_color, side_thickness)
grasp_center_xy = dh.pixel_from_3d(ball['grasp_center_xyz'], self.camera_info)
grasp_point = grasp_center_xy.astype(np.int32)
radius = 6
cv2.circle(task_relevant_image, grasp_point, radius, (255, 0, 0), -1, lineType=cv2.LINE_AA)
x,y,z = ball['grasp_center_xyz'] * 100.0
text_lines = [
"{:.1f} cm wide".format(ball['width_m']*100.0),
"{:.1f}, {:.1f}, {:.1f} cm".format(x,y,z)
]
draw_text(task_relevant_image, grasp_point, text_lines)
cv2.imshow('Task Relevant Results', task_relevant_image)
return send_dict