import cv2
import imutils
import numpy as np
from frigate.config import MotionConfig


class MotionDetector():
    def __init__(self, frame_shape, mask, config: MotionConfig):
        self.config = config
        self.frame_shape = frame_shape
        self.resize_factor = frame_shape[0]/config.frame_height
        self.motion_frame_size = (config.frame_height, config.frame_height*frame_shape[1]//frame_shape[0])
        self.avg_frame = np.zeros(self.motion_frame_size, np.float)
        self.avg_delta = np.zeros(self.motion_frame_size, np.float)
        self.motion_frame_count = 0
        self.frame_counter = 0
        resized_mask = cv2.resize(mask, dsize=(self.motion_frame_size[1], self.motion_frame_size[0]), interpolation=cv2.INTER_LINEAR)
        self.mask = np.where(resized_mask==[0])

    def detect(self, frame):
        motion_boxes = []

        gray = frame[0:self.frame_shape[0], 0:self.frame_shape[1]]

        # resize frame
        resized_frame = cv2.resize(gray, dsize=(self.motion_frame_size[1], self.motion_frame_size[0]), interpolation=cv2.INTER_LINEAR)

        # TODO: can I improve the contrast of the grayscale image here?

        # convert to grayscale
        # resized_frame = cv2.cvtColor(resized_frame, cv2.COLOR_BGR2GRAY)

        # mask frame
        resized_frame[self.mask] = [255]

        # it takes ~30 frames to establish a baseline
        # dont bother looking for motion
        if self.frame_counter < 30:
            self.frame_counter += 1
        else:
            # compare to average
            frameDelta = cv2.absdiff(resized_frame, cv2.convertScaleAbs(self.avg_frame))

            # compute the average delta over the past few frames
            # higher values mean the current frame impacts the delta a lot, and a single raindrop may
            # register as motion, too low and a fast moving person wont be detected as motion
            cv2.accumulateWeighted(frameDelta, self.avg_delta, self.config.delta_alpha)

            # compute the threshold image for the current frame
            # TODO: threshold
            current_thresh = cv2.threshold(frameDelta, self.config.threshold, 255, cv2.THRESH_BINARY)[1]

            # black out everything in the avg_delta where there isnt motion in the current frame
            avg_delta_image = cv2.convertScaleAbs(self.avg_delta)
            avg_delta_image = cv2.bitwise_and(avg_delta_image, current_thresh)

            # then look for deltas above the threshold, but only in areas where there is a delta
            # in the current frame. this prevents deltas from previous frames from being included
            thresh = cv2.threshold(avg_delta_image, self.config.threshold, 255, cv2.THRESH_BINARY)[1]

            # dilate the thresholded image to fill in holes, then find contours
            # on thresholded image
            thresh = cv2.dilate(thresh, None, iterations=2)
            cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
            cnts = imutils.grab_contours(cnts)

            # loop over the contours
            for c in cnts:
                # if the contour is big enough, count it as motion
                contour_area = cv2.contourArea(c)
                if contour_area > self.config.contour_area:
                    x, y, w, h = cv2.boundingRect(c)
                    motion_boxes.append((int(x*self.resize_factor), int(y*self.resize_factor), int((x+w)*self.resize_factor), int((y+h)*self.resize_factor)))
        
        if len(motion_boxes) > 0:
            self.motion_frame_count += 1
            if self.motion_frame_count >= 10:
                # only average in the current frame if the difference persists for a bit
                cv2.accumulateWeighted(resized_frame, self.avg_frame, self.config.frame_alpha)
        else:
            # when no motion, just keep averaging the frames together
            cv2.accumulateWeighted(resized_frame, self.avg_frame, self.config.frame_alpha)
            self.motion_frame_count = 0

        return motion_boxes
