def update(self):
# TODO: add core code here
global frame_no, id_for_new_vehicle, tracked_vehicles_info, tracked_vehicle_ids, current_tracked_id,tracked_vehicles,vehicle
flag = 0
if self.vid is None:
return self.window.after(self.delay, self.update)
ret, frame = self.vid.get_frame()
# if frame_no % 1 != 0:
# frame_no += 1
# frame = cv2.resize(frame, (1080, 700))
# frame = cv2.cvtColor(frame, cv2.COLOR_BGRA2RGBA)
# self.photo = PIL.ImageTk.PhotoImage(image=PIL.Image.fromarray(frame))
# self.canvas.create_image(0, 0, image=self.photo, anchor=tk.NW)
# return self.window.after(self.delay, self.update)
if ret:
vehicle_ids_this_frame = []
ocr_outputs_this_frame = []
# perform object detection
vehicle_imgs, vbbox, bbox = yolo.get_vehicle_imgs(frame, 750, 700)
if len(vehicle_imgs) == 0 or len(vbbox) == 0 or len(bbox) == 0:
frame_no +=1
frame = cv2.resize(frame, (1080, 700))
frame = cv2.cvtColor(frame, cv2.COLOR_BGRA2RGBA)
self.photo = PIL.ImageTk.PhotoImage(image=PIL.Image.fromarray(frame))
self.canvas.create_image(0, 0, image=self.photo, anchor=tk.NW)
return self.window.after(self.delay,self.update)
# perform object tracking
tracked_vehicles_info, id_for_new_vehicle = obj_tracker.track_vehicle(
obj_tracker.get_bbox_without_class(bbox))
# create or update the vehicles tracked till now
for key in tracked_vehicles_info.keys():
_, centroid_x, centroid_y = tracked_vehicles_info[key]
try:
v_index = list(map(lambda obj: obj.id, tracked_vehicles)).index(key)
# vehicle is present in the current list of vehicles
vehicle = tracked_vehicles[v_index]
except ValueError as e: # vehicle is not found
# if vehicle is not None: # here vehicle refers to the last detected vehicle
vehicle = Vehicle(key)
tracked_vehicles.append(vehicle)
vehicle_ids_this_frame.append(vehicle.id)
vehicle.current_bounding_box_centroid = (centroid_x, centroid_y)
# find the vehicle's image in the current frame
for v_img, v_bbox in zip(vehicle_imgs, vbbox):
# vehicle_imgs and vbbox have one-to-one direct correspondence
if obj_tracker.check_exact_match(vehicle.current_bounding_box_centroid[0],
vehicle.current_bounding_box_centroid[1],
v_bbox):
vehicle.img_current = v_img
vehicle.bbox_current = v_bbox
vehicle.bboxes.append(v_bbox)
vehicle.vehicle_imgs.append(v_img)
# if vehicle has no corresponding image, no point to process it further
if vehicle.img_current is None or vehicle.bbox_current is None:
tracked_vehicles.remove(vehicle)
if self.is_vehicle_out_of_frame(vehicle) is None:
pass
elif self.is_vehicle_out_of_frame(vehicle) and (not vehicle.is_completely_processed):
vehicle.aggregate_ocr()
print(vehicle.license_number)
if vehicle.license_number != '' and self.is_vehicle_unique(vehicle):
self._create_new_line(vehicle.id)
self.vehicle_nos[-1].insert(0, vehicle.id)
self.vehicle_lps[-1].insert(0, vehicle.license_number)
self.vehicle_nos[-1].config(state='readonly')
self.vehicle_lps[-1].config(state='readonly')
vehicle.is_completely_processed = True
if len(tracked_vehicles) == 0: # if no vehicle was detected, just skip
frame_no += 1
frame = cv2.resize(frame, (1080, 700))
frame = cv2.cvtColor(frame, cv2.COLOR_BGRA2RGBA)
self.photo = PIL.ImageTk.PhotoImage(image=PIL.Image.fromarray(frame))
self.canvas.create_image(0, 0, image=self.photo, anchor=tk.NW)
return self.window.after(self.delay,self.update)
for v in tracked_vehicles:
# perform lp localization
if v.id in vehicle_ids_this_frame and (not v.is_completely_processed):
lp_imgs, lp_bboxes, frame = localizer.predict_license_plate([v.img_current], frame,
[v.bbox_current])
lp_img = lp_imgs[0]
v.lp_imgs.append(lp_img)
lp_img = cv2.cvtColor(lp_img, cv2.COLOR_RGB2GRAY)
ocr_output = ocr.predict_ocr(lp_img)
v.license_number_predictions.append(ocr_output if ocr_output is not None else '')
ocr_outputs_this_frame.append(ocr_output)
# processing done, time for outputs
# print(frame_no, ocr_outputs_this_frame)
# create the frame to display
frame = yolo.draw_bbox(frame, bbox)
for v in tracked_vehicles:
if v.id in vehicle_ids_this_frame:
cx = int(v.current_bounding_box_centroid[0])
cy = int(v.current_bounding_box_centroid[1])
frame = cv2.putText(frame, f'{v.id}', (cx, cy), cv2.FONT_HERSHEY_COMPLEX, 4, (0, 255, 0), 4)
# resize the frame for display
frame = cv2.resize(frame, (1080, 700))
# cv2.imshow("prediction", frame)
frame = cv2.cvtColor(frame, cv2.COLOR_BGRA2RGBA)
self.photo = PIL.ImageTk.PhotoImage(image=PIL.Image.fromarray(frame))
self.canvas.create_image(0, 0, image=self.photo, anchor=tk.NW)
frame_no += 1
self.window.after(self.delay,self.update)
