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object_detect.py
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#!/usr/bin/env python3
# # object detection node
# # persons and cars
# # publish /objects/objects_coords array
# # 1d array pub - [ cls number, object count, min_point, min_p_X, min_p_Y, S_point, S_p_X, S_p_Y, E_point, E_p_X, E_p_Y]
import rospy
import cv2
from ultralytics import YOLO
from cv_bridge import CvBridge
from sensor_msgs.msg import Image
from std_msgs.msg import Float64MultiArray
import numpy as np
shape = (376, 672, 3)
frame_cap = np.zeros(shape)
shape = (376, 672)
depth_cap = np.zeros(shape)
model = YOLO('yolov8n-seg.pt')
frame_success = False
depth_success = False
def image_read(msg: Image):
global frame_cap
global frame_success
bridge = CvBridge()
orig = bridge.imgmsg_to_cv2(msg, "bgr8")
frame_cap = orig
frame_success = True
def depth_read(msg: Image):
global depth_cap
global depth_success
bridge = CvBridge()
orig = bridge.imgmsg_to_cv2(msg, "32FC1")
depth_cap = orig
depth_success = True
def publish_objectImage(img):
global pub_objectImage
bridge = CvBridge()
imgMsg = bridge.cv2_to_imgmsg(img,"bgr8")
pub_objectImage.publish(imgMsg)
if __name__ == '__main__':
rospy.init_node('object_detect_node')
rospy.loginfo("object identify node start")
rate = rospy.Rate(30)
sub_image = rospy.Subscriber("/zed_node/image", Image, callback=image_read)
sub_depth = rospy.Subscriber("/zed_node/depth", Image, callback=depth_read)
pub_coord = rospy.Publisher("/objects/objects_coords", Float64MultiArray, queue_size=50)
pub_objectImage = rospy.Publisher('/objects/objects_image', Image, queue_size=10)
while not rospy.is_shutdown():
if ( frame_success ):
# # convert (376, 672, 3) to (376, 640, 3)
# remove first and last 16 columns
frame = frame_cap[:, +16:-16, :]
# cv2.imshow("upgrade frame", frame)
# # YOLO model load ---------------------------------------------------------------
results = model(frame)
# person, vehicle mask data
object_mask = []
try:
for r in results:
masks = r.masks.cuda()
masks_xy = masks.xy
boxes = r.boxes.cuda()
object_XY = boxes.xyxy
person_count_val = -1
vehicl_count_val = -1
for count, dot_array in enumerate(masks_xy):
detect_mask = []
start_point_x = int(object_XY[count][0])
start_point_y = int(object_XY[count][1])
end_point_x = int(object_XY[count][2])
end_point_y = int(object_XY[count][3])
start_point = ( start_point_x, start_point_y)
end_point = ( end_point_x, end_point_y)
print("detect cls: ", int(r.boxes.cls[count]), " name: ", r.names[int(r.boxes.cls[count])])
if ( int(r.boxes.cls[count]) == 0):
person_count_val = person_count_val +1
# print(masks.data.size())
# print(masks.data[count])
# # person mask bitmap data -----------------------------------------------------
detect_mask.append(int(r.boxes.cls[count]))
detect_mask.append(person_count_val +1)
detect_mask.append(results[0].masks.data[count].cpu().numpy())
detect_mask.append(start_point)
detect_mask.append(end_point)
object_mask.append(detect_mask)
elif ( int(r.boxes.cls[count]) == 2 or int(r.boxes.cls[count]) == 3 or int(r.boxes.cls[count]) == 5 or int(r.boxes.cls[count]) == 7):
vehicl_count_val = vehicl_count_val +1
# # vehicle mask bitmap data -----------------------------------------------------
detect_mask.append(int(r.boxes.cls[count]))
detect_mask.append(vehicl_count_val +1)
detect_mask.append(results[0].masks.data[count].cpu().numpy())
detect_mask.append(start_point)
detect_mask.append(end_point)
object_mask.append(detect_mask)
print("detection count ", len(object_mask))
print("------------------------------------------------------------")
except:
print("model detection error")
# # YOLO detection end ----------------------------------------------------------------
# # depth data processing ------------------------------------------------------------
try:
publish_coord = []
object_coord = []
for detect_mask in object_mask:
frame = cv2.rectangle(frame, detect_mask[3], detect_mask[4], (255,0,0), 1)
frame = cv2.putText( frame, str(r.names[detect_mask[0]]), detect_mask[3], cv2.FONT_HERSHEY_SIMPLEX, 1, (50,50,255), 1, cv2.LINE_AA)
# # mask boundary coords (image_mask X depth_matrix) ---------------------------
# get detect person and remove first 8 rows to 376x640
object_frame = detect_mask[2][ +4:-4, :]
person_depth_array = np.array(object_frame) * np.array(depth_cap[:,+16:-16])
# print("person_depth_array", person_depth_array.shape)
# # minimum point of mask -----------------------------------------------
min_array= np.empty((0, 2), dtype=float)
person_x_min=0
person_y_min=0
for row in person_depth_array:
# print(type(row))
row[ row == 0 ] = np.nan
# print("max", np.nanmax(row) , "min", np.nanmin(row))
if np.any(~np.isnan(row)):
person_x_min = np.nanargmin(np.abs(row))
min_array = np.vstack((min_array, np.array([np.nanmin(np.abs(row)) , person_x_min])))
else:
min_array = np.vstack((min_array, np.array([np.nan , np.nan])))
# print(min_array.shape)
# # minimum value select
minimum_value = np.nanmin(min_array[:,0])
person_y_min = np.nanargmin(min_array[:,0])
person_x_min = min_array[person_y_min,1]
# print('minimun', minimum_value, "at:", person_x_min, person_y_min)
frame = cv2.circle( frame , (int(person_x_min),int(person_y_min)), 1, (255,0,0), 5)
frame = cv2.putText( frame, str(int(minimum_value)), [int(person_x_min),int(person_y_min)], cv2.FONT_HERSHEY_SIMPLEX, 1, (50,50,255), 2, cv2.LINE_AA)
# # boundary point values -----------------------------------------------------
start_point_x = detect_mask[3][0]
start_point_y = detect_mask[3][1]
end_point_x = detect_mask[4][0]
end_point_y = detect_mask[4][1]
# # box1 = [start_point_x,start_point_y], [start_point_x + 40, end_point_y]
# # box2 = [end_point_x - 40,start_point_y] , [end_point_x, end_point_y]
S_point_min = np.nanmin(np.abs(person_depth_array[ int(start_point_y):int(end_point_y), int(start_point_x):int(start_point_x+40) ]))
E_point_min = np.nanmin(np.abs(person_depth_array[ int(start_point_y):int(end_point_y), int(end_point_x-40):int(end_point_x) ]))
# print("start point mini :", S_point_min)
# print("end point min :", E_point_min)
frame = cv2.putText( frame, str(int(S_point_min)), ( start_point_x, end_point_y), cv2.FONT_HERSHEY_SIMPLEX, 1, (50,50,255), 2, cv2.LINE_AA)
frame = cv2.putText( frame, str(int(E_point_min)), ( end_point_x, end_point_y), cv2.FONT_HERSHEY_SIMPLEX, 1, (50,50,255), 2, cv2.LINE_AA)
# # publish depth data -------------------------------------------------------------
object_coord.append(detect_mask[0])
object_coord.append(detect_mask[1])
object_coord.append(minimum_value) # min point
object_coord.append(person_x_min)
object_coord.append(person_y_min)
object_coord.append(S_point_min) # start point
object_coord.append(start_point_x)
object_coord.append(start_point_y)
object_coord.append(E_point_min) # end point
object_coord.append(end_point_x)
object_coord.append(end_point_y)
# print(object_coord)
# # depth frame display
# frame_name = "object_depth" + str(detect_mask[1])
# cv2.imshow(frame_name, person_depth_array/5000)
print(object_coord)
publish_coord = Float64MultiArray()
publish_coord.data = object_coord
pub_coord.publish(publish_coord)
# # frame with masks
publish_objectImage(frame)
cv2.imshow("frame data", frame)
print("------------------------------------------------------------")
except:
print("depth processing error")
if cv2.waitKey(1) & 0XFF == ord("q"):
break
frame_success = False
rate.sleep()
rospy.spin()