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from ultralytics import YOLO
import numpy as np
from transformers import TrOCRProcessor, VisionEncoderDecoderModel
car_detection = YOLO("models/yolov8n.pt")
lp_detection = YOLO("models/yolov8n_lp_det.pt")
processor = TrOCRProcessor.from_pretrained('models/processor')
model = VisionEncoderDecoderModel.from_pretrained('models/model')
# char_dect = YOLO("models/yolov8n_lpchar_det.pt")
# char_rec = torch.load("models/charrec.pt", map_location="cpu")
# function to detect cars in the given image
def detect_cars(inputs):
cars = []
# running the cars detection model with 50% confidence threshold
car_results = car_detection.predict(source=inputs, classes=[2], conf=0.5, verbose=False)
# iterating through each output (num of outputs will be same as num of inputs)
for car_result in car_results:
# finding the bounding boxes of the cars detected
boxes = car_result.boxes.xyxy.tolist()
# iterating through each car detected
for box in boxes:
# cropping car image from the input image
car = car_result.orig_img[int(box[1]):int(box[3]), int(box[0]):int(box[2])]
cars.append(car)
return cars
# function to detect licence plates in the given car images
def detect_lp(inputs):
lps = []
# running the license plate detection model with 50% confidence threshold
lp_results = lp_detection.predict(source=inputs, conf=0.5, verbose=False)
# iterating through each output (num of outputs will be same as num of inputs)
for lp_result in lp_results:
# finding the bounding boxes of the license plate detected
lp_boxes = lp_result.boxes.xyxy.tolist()
# iterating through each license plate detected
for lp_box in lp_boxes:
# cropping license plate image from the car image
lp = lp_result.orig_img[int(lp_box[1]):int(lp_box[3]), int(lp_box[0]):int(lp_box[2])]
lps.append(lp)
# breaking as we only want to detect one licence plate per car
break
# if no licence plate is detected then we are adding a black image
if len(lp_boxes) == 0:
lps.append(np.zeros((100,100,3), np.uint8))
return lps
# function to detect licence plate number in the given licence plate images
def detect_lp_text(inputs):
plate_number = []
# iterating through each licence plate
for input in inputs:
# finding the number/text in licence plate
pixel_values = processor(input, return_tensors="pt").pixel_values
generated_ids = model.generate(pixel_values)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
# if no text is found in the licence plate, then adding a default text not found
if len(generated_text) == 0:
plate_number.append("not found")
else:
# adding the licence plate number to a list
plate_number.append(generated_text)
return plate_number
def run(inputs):
# for future, to handle multiple inputs
# currently using just one input
inputs = inputs[0]
# detecting cars, this function returns all detected car images
cars = detect_cars(inputs)
# if no car is detected black images are returned
if len(cars) == 0:
return [np.zeros((100,100,3), np.uint8)], [np.zeros((100,100,3), np.uint8)], "not found"
# detecting licence plates from the car images
# returns licence plate images, if it cant find a license plate a black image is returned
lps = detect_lp(cars)
# detecting licence plate number from licence plate images
# returns text from the licence plate images, if none is detected "not found" text is returned
lp_text = detect_lp_text(lps)
return cars, lps, lp_text
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