import cv2
import numpy as np
import tflite_runtime.interpreter as tflite

import classify
import detect

def load_labels(filename, encoding='utf-8'):
  with open(filename, 'r', encoding=encoding) as f:
    return {index : line.strip() for (index, line) in enumerate(f.readlines())}

def make_interpreter(model_file):
  model_file, *device = model_file.split('@')
  return tflite.Interpreter(
      model_path=model_file,
      experimental_delegates=[tflite.load_delegate('libedgetpu.so.1',
                              {'device': device[0]} if device else {})])

def make_detector(model, threshold=0.01):
    interpreter = make_interpreter(model)
    interpreter.allocate_tensors()
    def process(frame):
        height, width, _ = frame.shape
        scale = detect.set_input(interpreter, (width, height),
                                 lambda size: cv2.resize(frame, size, fx=0, fy=0, interpolation = cv2.INTER_CUBIC))
        interpreter.invoke()
        return detect.get_output(interpreter, threshold, scale)
    return process

def make_classifier(model, top_k=1, threshold=0.0):
  interpreter = make_interpreter(model)
  interpreter.allocate_tensors()
  size = classify.input_size(interpreter)
  def process(frame):
    classify.set_input(interpreter, cv2.resize(frame, size, fx=0, fy=0, interpolation = cv2.INTER_CUBIC))
    interpreter.invoke()
    return classify.get_output(interpreter, top_k, threshold)
  return process

def Camera(title, size):
  width, height = size
  cap = cv2.VideoCapture(0)
  cap.set(cv2.CAP_PROP_FRAME_WIDTH, width)
  cap.set(cv2.CAP_PROP_FRAME_HEIGHT, height)
  while True:
    ret, frame = cap.read()
    if ret:
      yield frame
    cv2.imshow(title, frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
      break
  cap.release()
  cv2.destroyAllWindows()