from imutils.video import VideoStream
import imutils
import time
import cv2
import numpy as np
import statistics
import queue
import math
from PIL import ImageDraw, ImageFont
from PIL import Image


def fall_detect(cnts, defined_min_area, frame, prevX, prevY, xList, yList, centerV, alert):
    for c in cnts:
        # exclusion
        if cv2.contourArea(c) < defined_min_area:
            continue

        # outer bounding box
        (x_b, y_b, w_b, h_b) = cv2.boundingRect(c)
        cv2.rectangle(frame, (x_b, y_b), (x_b + w_b, y_b + h_b), (0, 255, 255), 2)  # 黄色矩形

        # rotating bounding box
        rect = cv2.minAreaRect(c)  # 得到最小外接矩形的（中心(x,y), (宽,高), 旋转角度）
        box = cv2.boxPoints(rect)  # 获取最小外接矩形的4个顶点坐标
        box = np.int0(box)
        cv2.drawContours(frame, [box], 0, (0, 0, 255), 2)

        # averaging line
        rows, cols = frame.shape[:2]
        [vx, vy, x, y] = cv2.fitLine(c, cv2.DIST_L2, 0, 0.01, 0.01)
        lefty = (-x * vy / vx) + y
        righty = ((cols - x) * vy / vx) + y
        cv2.line(frame, (cols - 1, righty), (0, lefty), (255, 0, 0), 2)

        # ellipse
        elps = cv2.fitEllipse(c)
        (x, y), (MA, ma), angle = cv2.fitEllipse(c)
        cv2.ellipse(frame, elps, (255, 0, 0), 3)  # red

        # Aspect Ratio
        AR = MA / ma

        # Center Speed - acceleration
        prevX = 0.0
        prevY = 0.0
        centerSpeed = 0
        if xList.full():
            prevX = statistics.median(list(xList.queue))
            prevY = statistics.median(list(yList.queue))
            xList.get()
            yList.get()

        xList.put(elps[0][0])
        yList.put(elps[0][1])
        X = statistics.median(list(xList.queue))
        Y = statistics.median(list(yList.queue))

        if xList.full():
            dx = abs(prevX - X)
            dy = abs(prevY - Y)
            centerV = math.sqrt(dx ** 2 + dy ** 2)

        # calculate probabilities for the 4 features
        pAngle = (abs(angle - 90) - 50) / 10
        pAngle = 1 / (math.exp(pAngle) + 1)

        pAR = 10 * AR - 5
        pAR = 1 / (math.exp(pAR) + 1)

        ACS = centerV - 9
        try:
            ACS = 1 / (math.exp(ACS) + 1)
        except:
            ACS = 1 / (float('inf') + 1)
        # print("pAngle : ", pAngle)
        # print("pAR : ", pAR)
        # print("ACS : ", ACS)

        # confidence
        P_FALL = pAngle * pAR * ACS + 0.5
        # print("P_FALL1 : ", P_FALL)

        P_FALL = 1 / (math.exp(-(P_FALL - 0.65) * 10) + 1)
        # print("P_FALL2: ", P_FALL)

        # status display
        # cv2.putText(frame, "Status : ", (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)
        # cv2.putText(frame, "Fall Confidence: {0:.2f} ".format(P_FALL), (10, 50), cv2.FONT_HERSHEY_SIMPLEX, 0.5,
        #             (0, 128, 255), 2)
        # cv2.putText(frame, "Angle: {0:.2f}".format(angle), (10, 220),cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 2)
        # cv2.putText(frame, "AR: {0:.2f}".format(AR), (10, 237),cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 2)
        # cv2.putText(frame, "Center Speed: {0:.2f}".format(centerV), (10, 256),cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 2)

        # fall
        if P_FALL > 0.88:
            if alert >3:
                # print("fall")
                font = ImageFont.truetype("simsun.ttc", 30, index=1)
                img_rd = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
                draw = ImageDraw.Draw(img_rd)
                draw.text((10, 10), text="Fall Detected", font=font,
                          fill=(255, 0, 0))
                frame = cv2.cvtColor(np.array(img_rd), cv2.COLOR_RGB2BGR)
                # cv2.imwrite("report.jpg", frame)
                # send_alert.SendMail("report.jpg")
                alert = alert + 1
            else:
                alert = alert + 1

    return frame,alert