Python cv2 模块，FONT_HERSHEY_TRIPLEX 实例源码

def input_control(self, count_defects, img_src):
        # update position difference with previous frame (for move mouse)
        d_x, d_y = 0, 0
        if self.preCX is not None:
            d_x = self.ROIx - self.preCX
            d_y = self.ROIy - self.preCY

        # checking current command, and filter out unstable hand gesture
        cur_cmd = 0
        if self.cmd_switch:
            if self.last_cmds.count(count_defects) >= self.last_cmds.n_maj:
                cur_cmd = count_defects
                #print 'major command is ', cur_cmd
            else:
                cur_cmd = 0     # self.last_cmds.major()
        else:
            cur_cmd = count_defects

        # send mouse input event depend on hand gesture
        if cur_cmd == 1:
            str1 = '2, move mouse dx,dy = ' + str(d_x*3) + ', ' + str(d_y*3)
            cv2.putText(img_src, str1, (50, 50), cv2.FONT_HERSHEY_TRIPLEX, 2, (0, 0, 255), 2)
            if self.cmd_switch:
                pyautogui.moveRel(d_x*3, d_y*3)
                self.last_cmds.push(count_defects)
                #pyautogui.mouseDown(button='left')
                #pyautogui.moveRel(d_x, d_y)
            #else:
            #    pyautogui.mouseUp(button='left')
        elif cur_cmd == 2:
            cv2.putText(img_src, '3 Left (rotate)', (50, 50), cv2.FONT_HERSHEY_TRIPLEX, 2, (0, 0, 255), 2)
            if self.cmd_switch:
                pyautogui.dragRel(d_x, d_y, button='left')
                self.last_cmds.push(count_defects)
                #pyautogui.scroll(d_y,pause=0.2) 
        elif cur_cmd == 3:
            cv2.putText(img_src, '4 middle (zoom)', (50, 50), cv2.FONT_HERSHEY_TRIPLEX, 2, (0, 0, 255), 2)
            if self.cmd_switch:
                pyautogui.dragRel(d_x, d_y, button='middle')
                self.last_cmds.push(count_defects)
        elif cur_cmd == 4:
            cv2.putText(img_src, '5 right (pan)', (50, 50), cv2.FONT_HERSHEY_TRIPLEX, 2, (0, 0, 255), 2)
            if self.cmd_switch:
                pyautogui.dragRel(d_x, d_y, button='right')
                self.last_cmds.push(count_defects)
        elif cur_cmd == 5:
            cv2.putText(img_src, '1 fingertip show up', (50, 50), cv2.FONT_HERSHEY_TRIPLEX, 2, (0, 0, 255), 2)
            if self.cmd_switch:
                self.last_cmds.push(count_defects)
        else:
            cv2.putText(img_src, 'No finger detect!', (50, 50), cv2.FONT_HERSHEY_TRIPLEX, 2, (0, 0, 255), 2)
            if self.cmd_switch:
                self.last_cmds.push(count_defects)  # no finger detect or wrong gesture


# testing pyautogui

def main(args):
    infos = _get_classifier_model_info(args.model_version)
    with tf.Graph().as_default():
        sess = tf.Session(config=tf.ConfigProto(log_device_placement=False))
        with sess.as_default():
            pnet, rnet, onet = mtcnn.create_mtcnn(sess, args.caffe_model_dir)
    with tf.Graph().as_default():
        sess = tf.Session(config=tf.ConfigProto(log_device_placement=False))
        with sess.as_default():
            recognize = csair_classifier.create_classifier(sess, model_def=infos['model_def'],
                                                           image_size=int(infos['image_size']),
                                                           embedding_size=int(infos['embedding_size']),
                                                           nrof_classes=int(infos['nrof_classes']),
                                                           ckpt_dir=args.ckpt_dir)
    conn = db_utils.open_connection()
    cap = cv2.VideoCapture(0)
    while True:
        ret, frame = cap.read()

        bounding_boxes, points = mtcnn.detect_face(frame, 20, pnet, rnet, onet, args.threshold, args.factor)
        if len(bounding_boxes) > 0:
            for i in range(len(bounding_boxes)):
                box = bounding_boxes[i].astype(int)
                # mark = np.reshape(points[:, i].astype(int), (2, 5)).T
                crop = cv2.rectangle(frame, (box[0], box[1]), (box[2], box[3]), (0, 0, 255), 2)
                crop = cv2.resize(crop, (160, 160), interpolation=cv2.INTER_CUBIC)
                crop = np.expand_dims(crop, 0)
                value, index = csair_classifier.classify(crop, recognize)

                font = cv2.FONT_HERSHEY_TRIPLEX
                name = db_utils.get_candidate_info(conn, int(index[0][0]))[0]
                text = 'person: ' + name + ' probability: ' + str(value[0][0])
                # print('text: ', text)
                cv2.putText(frame, text, (box[0], box[1]), font, 0.42, (255, 255, 0))
                # for p in mark:
                #     cv2.circle(frame, (p[0], p[1]), 3, (0, 0, 255))

        cv2.imshow('frame', frame)
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

    cap.release()
    cv2.destroyAllWindows()
    db_utils.close_connection(conn)