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