### lab4-1 (4-1.py) ###

import cv2

ESC = 27

rect = ((220, 20), (370, 190))			# 設定 ROI 的座標範圍
(left, top), (right, bottom) = rect

# 自訂一個取出子畫面的函數
def roiarea(frame):
    return frame[top:bottom, left:right]

# 自訂一個將 ROI 區域的資料貼回原本畫面的函數
def replaceroi(frame, roi):
    frame[top:bottom, left:right] = roi
    return frame

cap = cv2.VideoCapture(0)
ratio = cap.get(cv2.CAP_PROP_FRAME_WIDTH) / cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
WIDTH = 400
HEIGHT = int(WIDTH / ratio)

while True:
    ret, frame = cap.read()
    frame = cv2.resize(frame, (WIDTH, HEIGHT))
    frame = cv2.flip(frame, 1)

    # 取出子畫面
    roi = roiarea(frame)
    roi = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)

    # 將處理完的子畫面貼回到原本畫面中
    frame = replaceroi(frame, roi)

    # 在 ROI 範圍處畫個框
    cv2.rectangle(frame, rect[0], rect[1], (0, 0, 255), 2)

    cv2.imshow('frame', frame)
    
    if cv2.waitKey(1) == ESC:
        break

cap.release()
cv2.destroyAllWindows()



### lab4-2 (4-2.py) ###

import cv2

ESC = 27

cap = cv2.VideoCapture('vtest.avi')			# OpenCV 範例影片 vtest.avi
tracker = cv2.TrackerCSRT_create()
roi = None							# 宣告一個變數 roi 來儲存欲追蹤的座標位置

while True:
    ret, frame = cap.read()
    if roi is None:
        roi = cv2.selectROI('frame', frame)
        if roi != (0, 0, 0, 0):			# 若有框出一個區域，則將矩形區域傳給追蹤演算法的 init() 函數
            tracker.init(frame, roi)
    success, rect = tracker.update(frame)		# 物體移動到新的區域，update() 計算新區域的座標，將新座標交給 rectangle() 畫出一個矩形區域
    
    if success:
        (x, y, w, h) = [int(i) for i in rect]
        cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)

    cv2.imshow('frame', frame)

    if cv2.waitKey(1) == 27:
        break

cap.release()
cv2.destroyAllWindows()



### lab4-3 (4-3.py) ###

import cv2

ESC = 27

# 加入即時影像的設定
cap = cv2.VideoCapture(0)
ratio = cap.get(cv2.CAP_PROP_FRAME_WIDTH) / cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
WIDTH = 400
HEIGHT = int(WIDTH / ratio)

tracker = cv2.TrackerCSRT_create()
roi = None

while True:
    ret, frame = cap.read()					# 讀取即時影像
    frame = cv2.resize(frame, (WIDTH, HEIGHT))
    if roi is None:
        roi = cv2.selectROI('frame', frame)
        if roi != (0, 0, 0, 0):
            tracker.init(frame, roi)
    success, rect = tracker.update(frame)

    if success:
        (x, y, w, h) = [int(i) for i in rect]
        cv2.rectangle(frame, (x,y), (x + w, y + h), (0, 255, 0), 2)

    cv2.imshow('frame', frame)

    if cv2.waitKey(1) == ESC:
        break

cap.release()
cv2.destroyAllWindows()



### lab4-4 ###

# Google MediaPipe 官方網址
https://mediapipe.dev/



### lab4-5 ###

# 從已安裝 opencv 的虛擬環境，複製出一個名為 mediapipe 的 Python 3 虛擬環境
$ cpvirtualenv opencv mediapipe

# 在樹莓派 4 安裝 MediaPipe
(mediapipe) $ pip install mediapipe-rpi4==0.8.4.0

# 在樹莓派 3 安裝 MediaPipe
(mediapipe) $ pip install mediapipe-rpi3==0.8.4.0



### lab4-6 (4-4.py) ###

import cv2
import mediapipe as mp			# 匯入 mediapipe 模組

ESC = 27

mp_face_detection = mp.solutions.face_detection		# 宣告 face_detection 的物件實例 (face_detection 類別用來偵測人臉)
mp_drawing = mp.solutions.drawing_utils			# 宣告 drawing_utils 的物件實例 (drawing_utils 類別用來畫圖)

cap = cv2.VideoCapture(0)
ratio = cap.get(cv2.CAP_PROP_FRAME_WIDTH) / cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
WIDTH = 400
HEIGHT = int(WIDTH / ratio)

face_detection = mp_face_detection.FaceDetection(min_detection_confidence = 0.5)		# 使用 face_detection 物件的 FaceDetection() 來偵測人臉

while cap.isOpened():
    ret, frame = cap.read()
    frame = cv2.resize(frame, (WIDTH, HEIGHT))
    frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)			# 將影像轉為 RGB
    frame.flags.writeable = False						# 設定影像為不能讀寫
    results = face_detection.process(frame)				# 辨識每一張影像
    frame.flags.writeable = True						# 設定影像為讀寫
    frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)			# 將影像轉為 BGR

    if results.detections:							# 如果有人臉被辨識
        for detection in results.detections:					# 走訪每張臉
            mp_drawing.draw_detection(frame, detection)				# 標註 6 個關鍵點

    cv2.imshow('MediaPipe Face Detection', frame)			# 顯示影像

    if cv2.waitKey(1) == ESC:
        break

cap.release()
cv2.destroyAllWindows()



### lab4-7 (4-5.py) ###

import cv2
import mediapipe as mp

ESC = 27

mp_drawing = mp.solutions.drawing_utils
mp_face_mesh = mp.solutions.face_mesh

drawing_spec = mp_drawing.DrawingSpec(thickness = 1, circle_radius = 1)

cap = cv2.VideoCapture(0)
ratio = cap.get(cv2.CAP_PROP_FRAME_WIDTH) / cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
WIDTH = 400
HEIGHT = int(WIDTH / ratio)

face_mesh = mp_face_mesh.FaceMesh(min_detection_confidence = 0.5, min_tracking_confidence = 0.5)

while cap.isOpened():
    ret, frame = cap.read()
    frame = cv2.resize(frame, (WIDTH, HEIGHT))
    frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
    frame.flags.writeable = False
    results = face_mesh.process(frame)
    frame.flags.writeable = True
    frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)

    if results.multi_face_landmarks:						# 如果有人臉被辨識
        for face_landmarks in results.multi_face_landmarks:				# 走訪每張臉
            mp_drawing.draw_landmarks(image = frame,						# 標註 468 個關鍵點
                              landmark_list = face_landmarks,
                              connections = mp_face_mesh.FACEMESH_CONTOURS,
                              landmark_drawing_spec = drawing_spec,
                              connection_drawing_spec = drawing_spec)

    cv2.imshow('MediaPipe FaceMesh', frame)

    if cv2.waitKey(1) == ESC:
        break

cap.release()
cv2.destroyAllWindows()



### lab4-8 (4-6.py) ###

import cv2
import mediapipe as mp

ESC = 27

mp_drawing = mp.solutions.drawing_utils
mp_hands = mp.solutions.hands					# 宣告 hands 的物件實例 (hands 類別用來偵測手勢)

cap = cv2.VideoCapture(0)
ratio = cap.get(cv2.CAP_PROP_FRAME_WIDTH) / cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
WIDTH = 400
HEIGHT = int(WIDTH / ratio)

hands = mp_hands.Hands(min_detection_confidence = 0.5, min_tracking_confidence = 0.5)

while cap.isOpened():
    ret, frame = cap.read()
    frame = cv2.resize(frame, (WIDTH, HEIGHT))
    frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
    frame.flags.writeable = False
    results = hands.process(frame)
    frame.flags.writeable = True
    frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
    
    if results.multi_hand_landmarks:
        for hand_landmarks in results.multi_hand_landmarks:
            mp_drawing.draw_landmarks(frame, hand_landmarks, mp_hands.HAND_CONNECTIONS)

    cv2.imshow('MediaPipe Hands', frame)

    if cv2.waitKey(1) == ESC:
        break
        
cap.release()
cv2.destroyAllWindows()



### lab4-9 (4-7.py) ###

import cv2
import mediapipe as mp

ESC = 27

mp_drawing = mp.solutions.drawing_utils
mp_pose = mp.solutions.pose

cap = cv2.VideoCapture(0)
ratio = cap.get(cv2.CAP_PROP_FRAME_WIDTH) / cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
WIDTH = 400
HEIGHT = int(WIDTH / ratio)

pose = mp_pose.Pose(min_detection_confidence = 0.5, min_tracking_confidence = 0.5)
                    
while cap.isOpened():
    ret, frame = cap.read()
    frame = cv2.resize(frame, (WIDTH, HEIGHT))
    frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
    frame.flags.writeable = False
    results = pose.process(frame)
    frame.flags.writeable = True
    frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
    mp_drawing.draw_landmarks(frame,
                       results.pose_landmarks,
                       mp_pose.POSE_CONNECTIONS)
    cv2.imshow('MediaPipe Pose', frame)

    if cv2.waitKey(1) == ESC:
        break

cap.release()
cv2.destroyAllWindows()



### lab4-10 ###

# CVZone 需要搭配 OpenCV 與 mediapipe，請先複製之前的 mediapipe 虛擬環境：
$ cpvirtualenv mediapipe cvzone

# 繼續安裝 CVZone
(cvzone) $ pip install cvzone==1.5.2



### lab4-11 (4-8.py) ###

from cvzone.FaceDetectionModule import FaceDetector
import cv2

ESC = 27

cap = cv2.VideoCapture(0)
ratio = cap.get(cv2.CAP_PROP_FRAME_WIDTH) / cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
WIDTH = 400
HEIGHT = int(WIDTH / ratio)

detector = FaceDetector()				# 宣告  FaceDetector 的物件實例 (FaceDetector() 用來偵測人臉)

while cap.isOpened():
    success, img = cap.read()
    img = cv2.resize(img, (WIDTH, HEIGHT)) 

    img, bboxs = detector.findFaces(img)

    if bboxs:									# 如果有人臉被辨識
        # bboxInfo - 'id', 'bbox', 'score', 'center'
        center = bboxs[0]['center']							# 設定中心點的座標
        cv2.circle(img, center, 5, (255, 0, 255), cv2.FILLED)		# 以中心點為座標，畫出一個圓

    cv2.imshow('Image', img)
    if cv2.waitKey(1) == ESC:
        break
        
cap.release()
cv2.destroyAllWindows()