ptime | IPython magic for parallel profiling | Performance Testing library

import cv2 #OpenCV is the library that we will be using for image processing
import mediapipe as mp #Mediapipe is the framework that will allow us to get our pose estimation
import time


mpDraw = mp.solutions.drawing_utils
mpPose = mp.solutions.pose

pose = mpPose.Pose()
#pose = mpPose.Pose(static_image_mode = False, upper_body_only = True) #ONLY UPPER_BODY_TRACKING

#cap = cv2.VideoCapture(0)
cap = cv2.VideoCapture('PoseVideos/1_girl_choreography.mp4')

pTime = 0 #previous time

while True:
    success, img = cap.read() #that will give it our image and then we can write the cv2.imshow()
    imgRGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) #convert our image to RGB because Mediapipe use that format
    results = pose.process(imgRGB) #we are simply going to send this image to our model

    #print(enumerate(results.pose_landmarks.landmark)) #

    #so then we will check if it is detected or not
    if results.pose_landmarks:

        mpDraw.draw_landmarks(img, results.pose_landmarks, mpPose.POSE_CONNECTIONS) 

        for id, lm in enumerate(results.pose_landmarks.landmark):

            h, w, c = img.shape #get dimensions(h height, w width) and the c channel of image
            
            print(id)
            print(lm)

            cx, cy = int(lm.x * w), int(lm.y * h)

            cv2.circle(img, (cx, cy), 5, (255, 0, 0), cv2.FILLED)


    cTime = time.time()
    fps = 1 / (cTime - pTime)
    pTime = cTime

    cv2.putText(img, str(int(fps)), (70, 50), cv2.FONT_HERSHEY_PLAIN, 3, (255, 0, 0), 3)

    cv2.imshow("Image", img)
    cv2.waitKey(1)

import cv2
import mediapipe as mp
import time
cap = cv2.VideoCapture(0)
while True:
    _, im0 = cap.read()
    showCrosshair = False
    fromCenter = False
    r = cv2.selectROI("Image", im0, fromCenter, showCrosshair)
    break

mpHands = mp.solutions.hands
hands = mpHands.Hands(static_image_mode=False,
                      max_num_hands=2,
                      min_detection_confidence=0.5,
                      min_tracking_confidence=0.5)
mpDraw = mp.solutions.drawing_utils
pTime = 0
cTime = 0
while True:
    _, img = cap.read()
    img = cv2.rectangle(img,(r[0],r[1]),(r[2],r[3]),(0,255,0),5)
    #imgRGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    results = hands.process(img)
    print(results.multi_hand_landmarks)
    if results.multi_hand_landmarks:
        for handLms in results.multi_hand_landmarks:
            for id, lm in enumerate(handLms.landmark):
                print(id,lm)
                h, w, c = img.shape
                cx, cy = int(lm.x *w), int(lm.y*h)
                cv2.circle(img, (cx,cy), 3, (255,0,255), cv2.FILLED)

            mpDraw.draw_landmarks(img, handLms, mpHands.HAND_CONNECTIONS)

    cTime = time.time()
    fps = 1/(cTime-pTime)
    pTime = cTime
    if cv2.waitKey(1) & 0xFF  == 27:
      break
    cv2.putText(img,str(int(fps)), (10,70), cv2.FONT_HERSHEY_PLAIN, 3, (255,0,255), 3)
    cv2.imshow("ThumbsDown", img)
    cv2.waitKey(1)