OpenCV - 顔が検出されたときにテキストを表示する必要があります

Question

私は以下のコード全体を持っていますが、顔が見つかった後にセットテキスト（例： "名前"）を表示します。私はこれを試してみましたが、私はどこにもなっていない午前：それは以下のコードで説明されて

if show_window: 
    cv2.rectangle(image,(x,y),(x+w,y+h),(255,0,0),2) 
    font = cv2.FONT_HERSHEY_SIMPLEX 
    cv2.putText(img,'OpenCV Tuts!',(0,130), font, 1, (200,255,155),  

#!/usr/bin/env python 
# opencv-picam-face.py - Opencv using picamera for face tracking using pan/tilt search and lock 

# Uses pipan.py module from openelectrons.com RPI camera pan/tilt to control 

# Also picamera python module must be installed as well as opencv 

# sudo apt-get install libopencv-dev python-opencv 

# sudo apt-get install python-picamera 
# v4l2 driver is not used since stream is created using picamera module 


print("initializing program. Please wait") 
import io 
import time 
import picamera 
import picamera.array 
import cv2 

# openelectron.com python module and files from the OpenElectron RPI camera pan/tilt 
# make sure pipan.py is in same folder as this script. 
import pipan 

p = pipan.PiPan() 

# Approx Center of Pan/Tilt motion 
pan_x_c = 150 
pan_y_c = 140 

# bounds checking for pan/tilt search. 
limit_y_bottom = 120 
limit_y_top = 170 
limit_y_level = 140 
limit_x_left = 80 
limit_x_right = 200 

# To speed things up, lower the resolution of the camera 
CAMERA_WIDTH = 320 
CAMERA_HEIGHT = 240 
inch_conv = 7.0 
verbose = True # display text messages in console window 
if not verbose: 
print("verbose is set to False so no status messages will be displayed") 
show_window = True # if desktop startx is running then show a window with  image and face rectangle 
if show_window: 
print("You must start this script from a terminal on the startx desktop") 

# Camera center of image 
cam_cx = CAMERA_WIDTH/2 
cam_cy = CAMERA_HEIGHT/2 

# Face detection opencv center of face box 
face_cx = cam_cx 
face_cy = cam_cy 

# Pan/Tilt motion center point 
pan_cx = pan_x_c 
pan_cy = pan_y_c 

# Amount pan/tilt moves when searching 
pan_move_x = 30 
pan_move_y = 20 

# Timer seconds to wait before starting pan/tilt search for face. 
wait_time = 30 

# load a cascade file for detecting faces. This file must be in 
# same folder as this script. Can usually be found as part of opencv 
face_cascade =   cv2.CascadeClassifier('/usr/share/opencv/haarcascades/haarcascade_frontalface_de fault.xml') 

# Saving the picture to an in-program stream rather than a file 
stream = io.BytesIO() 

#--------------------------------------------------------------------------- -------------------- 
def pan_goto(x,y): # Move the pan/tilt to a specific location. has built in limit checks. 
p.do_pan (int(x)) 
p.do_tilt (int(y)) 
if verbose: 
    print("move camera to pan_cx=%i pan_cy=%i" % (x, y)) 

# Start Main Program 
with picamera.PiCamera() as camera: 
camera.resolution = (CAMERA_WIDTH, CAMERA_HEIGHT) 
camera.vflip = True 
time.sleep(2) 

# Put camera in a known good position. 
pan_goto(pan_cx, pan_cy) 
face_found = False 
start_time = time.time() 

loop_cnt = 0 
while(True): 
    with picamera.array.PiRGBArray(camera) as stream: 
     camera.capture(stream, format='bgr') 
     time.sleep(.2) 
     # At this point the image is available as stream.array 
     image = stream.array 

    # Convert to grayscale, which is easier 
    gray = cv2.cvtColor(image, cv2. COLOR_BGR2GRAY) 
    # Look for max two faces in image stream using the loaded cascade file 
    faces = face_cascade.detectMultiScale(gray, 1.2, 2) 

    for (x, y, w, h) in faces: 
     if w > 0 : 
      face_found = True 
      loop_cnt = 0 
      start_time = time.time()   
      face_dist = int((CAMERA_WIDTH/w) * inch_conv) # Calculate distance from camera to Face    
      face_cx = x + w/2 
      face_cy = y + h/2 
      Nav_LR = cam_cx - face_cx 
      Nav_UD = cam_cy - face_cy 
      pan_cx = pan_cx - Nav_LR /5 
      pan_cy = pan_cy - Nav_UD /4 
      pan_goto(pan_cx, pan_cy) 
      # Print Navigation required to center face in image 
      if verbose: 
       print("Found: face_dist=%i inches at pan_cx=%i pan_cy=%i Nav_LR=%i Nav_UD=%i " % (face_dist, pan_cx, pan_cy, Nav_LR, Nav_UD)) 

     if show_window: 
      cv2.rectangle(image,(x,y),(x+w,y+h),(255,0,0),2)  
    font = cv2.FONT_HERSHEY_SIMPLEX 
    cv2.putText(image,'name',(0,130), font, 1, (200,255,155))  

    # start pan/tilt search for face if timer runs out 
    elapsed_time = time.time() - start_time 
    if elapsed_time > wait_time: 
     loop_cnt += 1 
     if loop_cnt > 3: # give camera a few cycles to find a face. 
      loop_cnt = 0 
     else: 
      pan_cx = pan_cx + pan_move_x 
      if pan_cx > limit_x_right: 
       pan_cx = limit_x_left   
       pan_cy = pan_cy + pan_move_y 
       if pan_cy > limit_y_top: 
        pan_cy = limit_y_bottom 
      if verbose: 
       print("Face Search: loop_cnt=%i Timer=%d > %s seconds" % (loop_cnt, elapsed_time, wait_time)) 
      pan_goto (pan_cx, pan_cy) 

    # Use opencv built in window to show the image in startx desktop window 
    # Leave out if your Raspberry Pi isn't set up to display windows 
    if show_window: 
     cv2.imshow('openCV Image', image) 
     if cv2.waitKey(1) & 0xFF == ord('q'): 
     # cursor over opencv window on desktop then press ctrl-q to Close   Window 
      cv2.destroyAllWindows() 
      exit 

Answer 1

：あなたはあなたが作った旗を割り当てることができます

face_found = False #---Initially set the flag to be False 
for (x, y, w, h) in faces: 
    if w > 0 :     #--- Set the flag True if w>0 (i.e, if face is detected) 
     face_found = True 

     cv2.rectangle(image,(x,y),(x+w,y+h),(255,0,0),2) #--- highlight the face 
     font = cv2.FONT_HERSHEY_SIMPLEX 
     cv2.putText(image,'name',(0,130), font, 1, (200,255,155)) #---write the text 
     cv2.imshow('Face having name', image)