「摸鱼」神器来了，Python 实现人脸监测制作神器

facial_features = [        'chin',        'left_eyebrow',        'right_eyebrow',        'nose_bridge',        'nose_tip',        'left_eye',        'right_eye',        'top_lip',        'bottom_lip'     ]video_capture = cv2.VideoCapture(0)label="flase"num=0try:    os.mkdir("img/"+label)except:    passwhile True:    ret,frame=video_capture.read()    face_locations = face_recognition.face_locations(frame)    face_landmarks_list = face_recognition.face_landmarks(frame)    for face_location in face_locations:        top, right, bottom, left = face_location        if len(face_landmarks_list)==1:            num+=1            face_image = frame[top:bottom, left:right]            cv2.imwrite("img/"+label+"/"+str(num)+".jpg",face_image)            print("保存第"+str(num)+"张人脸")            cv2.imshow("test",face_image)            cv2.waitKey(1)        else:            print("未能检测到人脸，或人脸数目不止一个，请保证只有一个人脸")    if num == 1000:        break        cv2.destroyAllWindows()

def train(train_dir, model_save_path=None, n_neighbors=None, knn_algo='ball_tree', verbose=False):    X = []    y = []    for class_dir in os.listdir(train_dir):        if not os.path.isdir(os.path.join(train_dir, class_dir)):            continue        for img_path in image_files_in_folder(os.path.join(train_dir, class_dir)):            image = face_recognition.load_image_file(img_path)            face_bounding_boxes = face_recognition.face_locations(image)            if len(face_bounding_boxes) != 1:                if verbose:                    print("Image {} not suitable for training: {}".format(img_path,                                                                          "Didn't find a face" if len(                                                                              face_bounding_boxes) < 1 else "Found more than one face"))            else:                X.append(face_recognition.face_encodings(image, known_face_locations=face_bounding_boxes)[0])                y.append(class_dir)    if n_neighbors is None:        n_neighbors = int(round(math.sqrt(len(X))))        if verbose:            print("Chose n_neighbors automatically:", n_neighbors)    knn_clf = neighbors.KNeighborsClassifier(n_neighbors=n_neighbors, algorithm=knn_algo, weights='distance')    knn_clf.fit(X, y)    if model_save_path is not None:        with open(model_save_path, 'wb') as f:            pickle.dump(knn_clf, f)    return knn_clfdef predict(X_img_path, knn_clf=None, model_path=None, distance_threshold=0.5):    if knn_clf is None and model_path is None:        raise Exception("Must supply knn classifier either thourgh knn_clf or model_path")    if knn_clf is None:        with open(model_path, 'rb') as f:            knn_clf = pickle.load(f)    X_img = X_img_path    X_face_locations = face_recognition.face_locations(X_img)    if len(X_face_locations) == 0:        return []    faces_encodings = face_recognition.face_encodings(X_img, known_face_locations=X_face_locations)    closest_distances = knn_clf.kneighbors(faces_encodings, n_neighbors=1)    are_matches = [closest_distances[0][i][0] <= distance_threshold for i in range(len(X_face_locations))]    return [(pred, loc) if rec else ("unknown", loc) for pred, loc, rec in            zip(knn_clf.predict(faces_encodings), X_face_locations, are_matches)]

def get_window_positon(width, height):    window_x_position = (window.winfo_screenwidth() - width) // 2    window_y_position = (window.winfo_screenheight() - height) // 2    return window_x_position, window_y_positionpos = get_window_positon(tk_width, tk_height) window.geometry(f'+{pos[0]}+{pos[1]}')def closewindow():    messagebox.showinfo(title="警告",message="请点击确定")    returndef t():    try:        os.remove("ok.txt")    except:        pass    window.destroy()window.protocol("WM_DELETE_WINDOW",closewindow)bnt=Button(window,text="确定",width=15,height=2,command=t)bnt.pack()window.mainloop()if temp>num:        if os.path.exists("ok.txt"):            pass        else:            t2 = threading.Thread(target=test2)            t2.start()            os.system("1.jpg")            f = open("ok.txt", "w")            f.close()            t1 = threading.Thread(target=test1)            t1.start()    for name, (top, right, bottom, left) in predictions:        draw.rectangle(((left, top), (right, bottom)), outline=(0, 0, 255))        text_width, text_height = draw.textsize(name)        draw.rectangle(((left, bottom - text_height - 10), (right, bottom)), fill=(0, 0, 255), outline=(0, 0, 255))        draw.text((int((left + right)/2), bottom - text_height - 10), name,font=myfont, fill=(0,0,0))    del draw    pil_image = np.array(pil_image)    temp = numelse:    pil_image=img_pathdef test2():    os.system('1.caj')def test1():    os.system('test.exe')