I want to change this code from how to trcak after cars to track after circle in circle - opencv

Question

this is the code but it didn't work with the sign
import cv2
import numpy as np
from object_detection import ObjectDetection
import math
# Initialize Object Detection
od = ObjectDetection()
cap = cv2.VideoCapture("Sign black.mp4")
#Initalize count
count = 0
center_points_prev_frame = []
tracking_objects = {}
track_id = 0
while True:
ret, frame = cap.read()
count += 1
if not ret:
break
# Point current frame
center_points_cur_frame = []
# Detect objects on frame
(class_ids, scores, boxes) = od.detect(frame)
for box in boxes:
(x, y, w, h) = box
cx = int((x + x + w) / 2)
cy = int((y + y + h) / 2)
center_points_cur_frame.append((cx, cy))
#print("FRAME N* ", count, " ", x, y, w, h)
#cv2.circle(frame, (cx,cy), 5, (0, 0, 255), -1)
cv2.rectangle(frame, (x,y), (x + w, y + h), (0, 255, 0), 2)
# Only at the beginning we compare previouse and current frame
if count <= 2:
for pt in center_points_cur_frame:
for pt2 in center_points_prev_frame:
distance = math.hypot(pt2[0] - pt[0], pt2[1] - pt[1])
if distance < 20:
tracking_objects[track_id] = pt
track_id += 1
else:
tracking_objects_copy = tracking_objects.copy()
for object_id, pt2 in tracking_objects_copy.items():
object_exists = False
for pt in center_points_cur_frame:
distance = math.hypot(pt2[0] - pt[0], pt2[1] - pt[1])
# Update object position
if distance < 20:
tracking_objects[object_id] = pt
object_exists = True
continue
# Remove IDs lost
if not object_exists:
tracking_objects.pop(object_id)
for object_id, pt in tracking_objects.items():
cv2.circle(frame, pt, 5, (0, 0, 255), -1)
cv2.putText(frame, str(object_id), (pt[0], pt[1] - 7), 0, 1, (0, 0, 255), 2)
print("Tracking objects")
print(tracking_objects)
# cv2.circle(frame, pt, 5, (0, 0, 255), -1)
print("CUR FRAME LEFT PTS")
print(center_points_cur_frame)
#print("PREV FRAME")
#print(center_points_prev_frame)
cv2.imshow("Frame", frame)
# Make a copy of the points
center_points_prev_frame = center_points_cur_frame.copy()
Key = cv2.waitKey(1)
if Key == 27:
break
cap.release()
cv2.destroyAllWindows()
and this is
import numpy as np
class ObjectDetection:
def __init__(self, weights_path="dnn_model/yolov4.weights", cfg_path="dnn_model/yolov4.cfg"):
print("Loading Object Detection")
print("Running opencv dnn with YOLOv4")
self.nmsThreshold = 0.4
self.confThreshold = 0.5
self.image_size = 608
# Load Network
net = cv2.dnn.readNet(weights_path, cfg_path)
# Enable GPU CUDA
net.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA)
net.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA)
self.model = cv2.dnn_DetectionModel(net)
self.classes = []
self.load_class_names()
self.colors = np.random.uniform(0, 255, size=(80, 3))
self.model.setInputParams(size=(self.image_size, self.image_size), scale=1/255)
def load_class_names(self, classes_path="dnn_model/classes.txt"):
with open(classes_path, "r") as file_object:
for class_name in file_object.readlines():
class_name = class_name.strip()
self.classes.append(class_name)
self.colors = np.random.uniform(0, 255, size=(80, 3))
return self.classes
def detect(self, frame):
return self.model.detect(frame, nmsThreshold=self.nmsThreshold, confThreshold=self.confThreshold)