ValueError: could not broadcast input array from shape (320,320,3) into shape (640,640,3)

Question

I've been trying to run a detection model on a raspberry pi but when I try I get the error that:

could not broadcast input array from shape (320,320,3) into shape (640,640,3)

when I run this

import re
import cv2
from tflite_runtime.interpreter import Interpreter
import numpy as np

CAMERA_WIDTH = 640
CAMERA_HEIGHT = 480

def load_labels(path='labels.txt'):
  """Loads the labels file. Supports files with or without index numbers."""
  with open(path, 'r', encoding='utf-8') as f:
    lines = f.readlines()
    labels = {}
    for row_number, content in enumerate(lines):
      pair = re.split(r'[:\s]+', content.strip(), maxsplit=1)
      if len(pair) == 2 and pair[0].strip().isdigit():
        labels[int(pair[0])] = pair[1].strip()
      else:
        labels[row_number] = pair[0].strip()
  return labels

def set_input_tensor(interpreter, image):
  """Sets the input tensor."""
  tensor_index = interpreter.get_input_details()[0]['index']
  input_tensor = interpreter.tensor(tensor_index)()[0]
  input_tensor[:, :] = np.expand_dims((image-255)/255, axis=0)


def get_output_tensor(interpreter, index):
  """Returns the output tensor at the given index."""
  output_details = interpreter.get_output_details()[index]
  tensor = np.squeeze(interpreter.get_tensor(output_details['index']))
  return tensor


def detect_objects(interpreter, image, threshold):
  """Returns a list of detection results, each a dictionary of object info."""
  set_input_tensor(interpreter, image)
  interpreter.invoke()
  # Get all output details
  boxes = get_output_tensor(interpreter, 0)
  classes = get_output_tensor(interpreter, 1)
  scores = get_output_tensor(interpreter, 2)
  count = int(get_output_tensor(interpreter, 3))

  results = []
  for i in range(count):
    if scores[i] >= threshold:
      result = {
          'bounding_box': boxes[i],
          'class_id': classes[i],
          'score': scores[i]
      }
      results.append(result)
  return results

def main():
    labels = load_labels()
    interpreter = Interpreter('detect.tflite')
    interpreter.allocate_tensors()
    _, input_height, input_width, _ = interpreter.get_input_details()[0]['shape']

    cap = cv2.VideoCapture(0)
    while cap.isOpened():
        ret, frame = cap.read()
        img = cv2.resize(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB), (320,320))
        res = detect_objects(interpreter, img, 0.8)
        print(res)

        for result in res:
            ymin, xmin, ymax, xmax = result['bounding_box']
            xmin = int(max(1,xmin * CAMERA_WIDTH))
            xmax = int(min(CAMERA_WIDTH, xmax * CAMERA_WIDTH))
            ymin = int(max(1, ymin * CAMERA_HEIGHT))
            ymax = int(min(CAMERA_HEIGHT, ymax * CAMERA_HEIGHT))
            
            cv2.rectangle(frame,(xmin, ymin),(xmax, ymax),(0,255,0),3)
            cv2.putText(frame,labels[int(result['class_id'])],(xmin, min(ymax, CAMERA_HEIGHT-20)), cv2.FONT_HERSHEY_SIMPLEX, 0.5,(255,255,255),2,cv2.LINE_AA) 

        cv2.imshow('Pi Feed', frame)

        if cv2.waitKey(10) & 0xFF ==ord('q'):
            cap.release()
            cv2.destroyAllWindows()

if __name__ == "__main__":
    main()

the model is an SSD Mobilenet 640x640 and the images for the model were taken on the raspberry pi as 1028x720 but were downscaled during model training. But I still get this error and I'm not sure how to fix it.