# Opencv Deep learning Tutorial

# https://github.com/opencv/opencv/wiki/Deep-Learning-in-OpenCV

# Caffe Model Zoo : github.com/BVLC/caffe

## 모델 파일 : dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel

## 설정 파일 : github.com/BVLC/caffe/blob/master/models/bvlc_googlenet/deploy.prototxt

# ONNX Model Zoo : github.com/onnx/models

# 모델파일: https://github.com/onnx/models/tree/master/vision/classification/inception_and_googlenet/googlenet

# 클래스 이름 파일 : github.com/opencv/opencv/blob/4.1.0/samples/data/dnn/

# readNet(model, config)

# model, config, dataset

# https://cocodataset.org/#home

# 실행순서

# cv2.dnn.readNet(model, config)-> ret, 객체생성

# blobFromImage(image, scalefactor, size, mean, swapRB, crop) -> retval

# scalefactor: Multiply by factor

# image has BGR ordering and swapRB is true.

##############################################################################################

import numpy as np

import cv2

import sys

import os

import glob

img =  cv2.imread('./opencv_face_detector/fig/sunglass.png')

## Tensoflow

model='./face_detector/opencv_face_detector_uint8.pb'

config = './face_detector/opencv_face_detector.pbtxt.txt'

# cap = cv2.VideoCapture(0) #카메라일때 

cap = cv2.VideoCapture('/Users/winviva/Downloads/movie/인질/인질 Hostage Missing Celebrity.2021.1080p.FHDRip.H264.AAC-NonDRM.mp4')

if not cap.isOpened():

    print('vid faile')

    sys.exit()

face_net = cv2.dnn.readNet(model, config)

if face_net.empty():

    print('net fail')

    sys.exit()

while True:

    ret, img = cap.read()

    if not ret:

        print('vid fail')

        break

    ##################

    blob = cv2.dnn.blobFromImage(img,1,(300,300),(104, 117, 123),swapRB=False)

    face_net.setInput(blob)

    out = face_net.forward()

    # print(out.shape)

    detect = out[0,0,:,:]

    # print(detect.shape)

    h, w = img.shape[:2]

    threshold = 0.5

    for i in range(detect.shape[0]):

        confidence = detect[i,2]

        if confidence > 0.5:

            x1 = int(detect[i,3]*w)

            y1 = int(detect[i,4]*h)

            x2 = int(detect[i,5]*w)

            y2 = int(detect[i,6]*h)

            cv2.rectangle(img, (x1,y1),(x2,y2),(0,0,255),2)

            # text = f'Face: {confidence*100:4.2f}%'

            text = 'Face: {}%'.format(round(confidence*100,2))

            cv2.putText(img, text, (x1,y1-3), cv2.FONT_HERSHEY_COMPLEX,1,(255,255,0), 1, cv2.LINE_AA)

    cv2.imshow('image', img)

    if cv2.waitKey(20)== 27:

        break

cap.release()

cv2.waitKey(1)

cv2.destroyAllWindows()

cv2.waitKey(1)