Face Detection

File path: /home/pi/Yahboom_Project/1.OpenCV course/04 advanced tutorial/face_detaction.ipynb

The test code is as follows:

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#bgr8 to jpeg format
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import enum
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import cv2
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def bgr8_to_jpeg(value, quality=75):
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    return bytes(cv2.imencode('.jpg', value)[1])
#Camera display component
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import cv2
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import ipywidgets.widgets as widgets
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import threading
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import time
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import sys
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image_widget = widgets.Image(format='jpeg', width=320, height=240)
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display(image_widget)
image = cv2.VideoCapture(0)
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image.set(3,320)
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image.set(4,240)
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ret, frame = image.read()
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image_widget.value = bgr8_to_jpeg(frame)
# Thread function operation library
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import inspect
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import ctypes
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def _async_raise(tid, exctype):
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    """raises the exception, performs cleanup if needed"""
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    tid = ctypes.c_long(tid)
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    if not inspect.isclass(exctype):
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        exctype = type(exctype)
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    res = ctypes.pythonapi.PyThreadState_SetAsyncExc(tid, ctypes.py_object(exctype))
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    if res == 0:
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        raise ValueError("invalid thread id")
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    elif res != 1:
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        # """if it returns a number greater than one, you're in trouble,
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        # and you should call it again with exc=NULL to revert the effect"""
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        ctypes.pythonapi.PyThreadState_SetAsyncExc(tid, None)
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def stop_thread(thread):
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    _async_raise(thread.ident, SystemExit)
# body_haar = cv2.CascadeClassifier("haarcascade_upperbody.xml")
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# face_haar = cv2.CascadeClassifier("haarcascade_profileface.xml")
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face_haar = cv2.CascadeClassifier("haarcascade_fullbody.xml")
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#eye_haar = cv2.CascadeClassifier("haarcascade_eye.xml")  
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eye_haar = cv2.CascadeClassifier("haarcascade_eye_tree_eyeglasses.xml")  
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def Camera_display():
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    while 1:
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        ret, frame = image.read()
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        # Convert the image to a grayscale image
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        gray_img = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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#         # Detect all pedestrians in the image
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#      bodies = body_haar.detectMultiScale(gray_img, 1.3, 5)
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#      for body_x,body_y,body_w,body_h in bodies:
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#      cv2.rectangle(frame, (body_x, body_y), (body_x+body_w, body_y+body_h), (0,255,0), 2)
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        faces = face_haar.detectMultiScale(gray_img, 1.1, 3)
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        for face_x,face_y,face_w,face_h in faces:
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            cv2.rectangle(frame, (face_x, face_y), (face_x+face_w, face_y+face_h), (0,255,0), 2)
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        eyes = eye_haar.detectMultiScale(gray_img, 1.1, 3)
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        for eye_x,eye_y,eye_w,eye_h in eyes:
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            cv2.rectangle(frame, (eye_x,eye_y), (eye_x+eye_w, eye_y+eye_h), (255,0,0), 2)
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#         eyes = eye_haar.detectMultiScale(gray_img, 1.3, 5)
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#         for eye_x,eye_y,eye_w,eye_h in eyes:
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#             cv2.rectangle(frame, (eye_x,eye_y), (eye_x+eye_w, eye_y+eye_h), (255,0,0), 2)
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        image_widget.value = bgr8_to_jpeg(frame)
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        time.sleep(0.010)
#Start the thread
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thread1 = threading.Thread(target=Camera_display)
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thread1.setDaemon(True)
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thread1.start()
#End the process, release the camera, execute when it needs to end
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stop_thread(thread1)
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image.release()

After running, we can see the camera display frame below the camera component, with the person's face and eyes outlined. Note that lighting may affect the experimental results.

For the CSI camera, code path: /home/pi/Yahboom_Project/1.OpenCV course/04 advanced tutorial/face_detection_CSI.ipynb

Note: For the CSI camera, if you want to re-run the program, you need to do the following: