Question

我编写了一个面部检测代码，但我的目标是获取由haar级联检测到的面部数量，并在每个面部上打印面部数量。我使用face.shape获取人脸的形状，它给出了人脸的数量及其坐标。打印face.shape的第一个参数给出了总的脸数。关键是获取每个面孔的数量。就像1，2，3写在不同的面孔上。我需要一些算法。谢谢。

代替我的帖子，来讨论发生了什么问题，以便我可以改善它。

cap = cv2.VideoCapture(0) # Set the camera to use

while True:
    ret, frame = cap.read()    # For image as an input -> frame = cv2.imread(r'C:\Users\ASUS\Desktop\Novumare Technologies\crowded.mp4')
    if ret is True:
        #start_time = time.time()
        frame = cv2.resize(frame, (640, 360))    # Downscale to improve frame rate
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)    # Haar-cascade classifier needs a grayscale image
    else:
        break

    # Capturing and writing on detected bodies
    bodies = body_cascade.detectMultiScale(gray, 1.2, 5)
    for(x, y, w, h) in bodies:
        cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)
        cv2.putText(frame, 'Human', (x, y), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,255,255), 2)
    # Capturing and writing on detected faces
    faces = face_cascade.detectMultiScale(gray)
    for(ex, ey, ew, eh) in faces:
        cv2.rectangle(frame, (ex, ey), (ex+ew, ey+eh), (0, 0, 255), 2)
        cv2.putText(frame, 'Face #' + str(faces.shape[0]), (ex, ey), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,0), 2)
        print(faces)
        print('------------')
        print(faces.shape)

    # Open here when you save the output -> out.write(frame)
    #end_time = time.time()
    #print("Elapsed time: ", end_time-start_time)
    cv2.putText(frame, "Number of faces detected: " + str(faces.shape[0]), 
    (0,frame.shape[0] -10), cv2.FONT_HERSHEY_TRIPLEX, 0.5,  (0,0,0), 1)
    cv2.imshow('frame', frame)

    if cv2.waitKey(1) & 0xFF == ord('q'):   # Exit condition
        break

# Release everything if job is finished
cap.release()
# Open here when you save the output -> out.release()
cv2.destroyAllWindows()

Answer 1

由于面孔的总数为len（面孔），因此您可以自己计算每个面孔的数量。您可以使用face_count来做到这一点。

face_count = 0 # edit it to 1 if you want the number to start with 1
for(ex, ey, ew, eh) in faces:
    cv2.rectangle(frame, (ex, ey), (ex+ew, ey+eh), (0, 0, 255), 2)
    cv2.putText(frame, 'Face #' + str(face_count), (ex, ey), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,0), 2)
    print(faces)
    print('------------')
    print(faces.shape)
    face_count += 1

获取检测到的面部数量

1 个答案: