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| Author | SHA1 | Date | |
|---|---|---|---|
 Торов Михаил ДмитриевичandCursor
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Торов Михаил ДмитриевичandCursor
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# testCursor
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Разработка ведётся в ветке **dev**.
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```bash
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git checkout dev
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```
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "h2q27gKz1H20"
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},
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"source": [
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"##### Copyright 2023 The MediaPipe Authors. All Rights Reserved."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "TUfAcER1oUS6"
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},
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"outputs": [],
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"source": [
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"#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
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"# you may not use this file except in compliance with the License.\n",
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"# You may obtain a copy of the License at\n",
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"#\n",
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"# https://www.apache.org/licenses/LICENSE-2.0\n",
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"#\n",
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"# Unless required by applicable law or agreed to in writing, software\n",
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"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
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"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
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"# See the License for the specific language governing permissions and\n",
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"# limitations under the License."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "L_cQX8dWu4Dv"
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},
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"source": [
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"# Hand Landmarks Detection with MediaPipe Tasks\n",
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"\n",
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"This notebook shows you how to use MediaPipe Tasks Python API to detect hand landmarks from images."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "O6PN9FvIx614"
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},
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"source": [
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"## Preparation\n",
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"\n",
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"Let's start with installing MediaPipe."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "gxbHBsF-8Y_l"
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},
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"outputs": [],
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"source": [
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"!pip install -q mediapipe"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "a49D7h4TVmru"
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},
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"source": [
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"Then download an off-the-shelf model bundle. Check out the [MediaPipe documentation](https://developers.google.com/mediapipe/solutions/vision/hand_landmarker#models) for more information about this model bundle."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "OMjuVQiDYJKF"
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},
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"outputs": [],
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"source": [
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"!wget -q https://storage.googleapis.com/mediapipe-models/hand_landmarker/hand_landmarker/float16/1/hand_landmarker.task"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "YYKAJ5nDU8-I"
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},
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"source": [
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"## Visualization utilities"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"cellView": "form",
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"id": "s3E6NFV-00Qt"
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},
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"outputs": [],
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"source": [
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"#@markdown We implemented some functions to visualize the hand landmark detection results. <br/> Run the following cell to activate the functions.\n",
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"import mediapipe as mp\n",
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"import numpy as np\n",
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"\n",
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"mp_hands = mp.tasks.vision.HandLandmarksConnections\n",
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"mp_drawing = mp.tasks.vision.drawing_utils\n",
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"mp_drawing_styles = mp.tasks.vision.drawing_styles\n",
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"\n",
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"MARGIN = 10 # pixels\n",
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"FONT_SIZE = 1\n",
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"FONT_THICKNESS = 1\n",
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"HANDEDNESS_TEXT_COLOR = (88, 205, 54) # vibrant green\n",
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"\n",
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"def draw_landmarks_on_image(rgb_image, detection_result):\n",
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" hand_landmarks_list = detection_result.hand_landmarks\n",
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" handedness_list = detection_result.handedness\n",
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" annotated_image = np.copy(rgb_image)\n",
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"\n",
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" # Loop through the detected hands to visualize.\n",
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" for idx in range(len(hand_landmarks_list)):\n",
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" hand_landmarks = hand_landmarks_list[idx]\n",
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" handedness = handedness_list[idx]\n",
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"\n",
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" # Draw the hand landmarks.\n",
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" mp_drawing.draw_landmarks(\n",
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" annotated_image,\n",
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" hand_landmarks,\n",
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" mp_hands.HAND_CONNECTIONS,\n",
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" mp_drawing_styles.get_default_hand_landmarks_style(),\n",
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" mp_drawing_styles.get_default_hand_connections_style())\n",
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"\n",
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" # Get the top left corner of the detected hand's bounding box.\n",
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" height, width, _ = annotated_image.shape\n",
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" x_coordinates = [landmark.x for landmark in hand_landmarks]\n",
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" y_coordinates = [landmark.y for landmark in hand_landmarks]\n",
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" text_x = int(min(x_coordinates) * width)\n",
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" text_y = int(min(y_coordinates) * height) - MARGIN\n",
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"\n",
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" # Draw handedness (left or right hand) on the image.\n",
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" cv2.putText(annotated_image, f\"{handedness[0].category_name}\",\n",
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" (text_x, text_y), cv2.FONT_HERSHEY_DUPLEX,\n",
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" FONT_SIZE, HANDEDNESS_TEXT_COLOR, FONT_THICKNESS, cv2.LINE_AA)\n",
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"\n",
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" return annotated_image"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "83PEJNp9yPBU"
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},
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"source": [
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"## Download test image\n",
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"\n",
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"Let's grab a test image that we'll use later. The image is from [Unsplash](https://unsplash.com/photos/mt2fyrdXxzk)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "tzXuqyIBlXer"
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},
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"outputs": [],
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"source": [
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"!wget -q -O image.jpg https://storage.googleapis.com/mediapipe-tasks/hand_landmarker/woman_hands.jpg\n",
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"\n",
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"import cv2\n",
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"from google.colab.patches import cv2_imshow\n",
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"\n",
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"img = cv2.imread(\"image.jpg\")\n",
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"cv2_imshow(img)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "u-skLwMBmMN_"
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},
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"source": [
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"Optionally, you can upload your own image. If you want to do so, uncomment and run the cell below."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "etBjSdwImQPw"
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},
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"outputs": [],
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"source": [
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"# from google.colab import files\n",
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"# uploaded = files.upload()\n",
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"\n",
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"# for filename in uploaded:\n",
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"# content = uploaded[filename]\n",
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"# with open(filename, 'wb') as f:\n",
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"# f.write(content)\n",
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"\n",
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"# if len(uploaded.keys()):\n",
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"# IMAGE_FILE = next(iter(uploaded))\n",
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"# print('Uploaded file:', IMAGE_FILE)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "Iy4r2_ePylIa"
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},
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"source": [
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"## Running inference and visualizing the results\n",
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"\n",
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"Here are the steps to run hand landmark detection using MediaPipe.\n",
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"\n",
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"Check out the [MediaPipe documentation](https://developers.google.com/mediapipe/solutions/vision/hand_landmarker/python) to learn more about configuration options that this solution supports.\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "_JVO3rvPD4RN"
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},
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"outputs": [],
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"source": [
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"# STEP 1: Import the necessary modules.\n",
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"import mediapipe as mp\n",
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"from mediapipe.tasks import python\n",
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"from mediapipe.tasks.python import vision\n",
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"\n",
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"# STEP 2: Create an HandLandmarker object.\n",
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"base_options = python.BaseOptions(model_asset_path='hand_landmarker.task')\n",
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"options = vision.HandLandmarkerOptions(base_options=base_options,\n",
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" num_hands=2)\n",
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"detector = vision.HandLandmarker.create_from_options(options)\n",
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"\n",
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"# STEP 3: Load the input image.\n",
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"image = mp.Image.create_from_file(\"image.jpg\")\n",
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"\n",
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"# STEP 4: Detect hand landmarks from the input image.\n",
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"detection_result = detector.detect(image)\n",
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"\n",
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"# STEP 5: Process the classification result. In this case, visualize it.\n",
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"annotated_image = draw_landmarks_on_image(image.numpy_view(), detection_result)\n",
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"cv2_imshow(cv2.cvtColor(annotated_image, cv2.COLOR_RGB2BGR))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "SE6_sPCXaX3g"
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},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"colab": {
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"collapsed_sections": [
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"h2q27gKz1H20"
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],
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"provenance": []
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},
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.10"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}
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import cv2
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import mediapipe as mp
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import math
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print("Запуск MediaPipe детектора рук (новый API)...")
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print(f"Версия MediaPipe: {mp.__version__}")
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if not hasattr(mp, 'tasks'):
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print("ОШИБКА: mediapipe.tasks не найден!")
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exit()
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BaseOptions = mp.tasks.BaseOptions
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HandLandmarker = mp.tasks.vision.HandLandmarker
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HandLandmarkerOptions = mp.tasks.vision.HandLandmarkerOptions
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VisionRunningMode = mp.tasks.vision.RunningMode
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HAND_CONNECTIONS = [
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(0, 1), (1, 2), (2, 3), (3, 4),
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(0, 5), (5, 6), (6, 7), (7, 8),
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(0, 9), (9, 10), (10, 11), (11, 12),
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(0, 13), (13, 14), (14, 15), (15, 16),
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(0, 17), (17, 18), (18, 19), (19, 20),
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(5, 9), (9, 13), (13, 17)
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]
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options = HandLandmarkerOptions(
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base_options=BaseOptions(model_asset_path='hand_landmarker.task'),
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running_mode=VisionRunningMode.IMAGE,
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num_hands=2,
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min_hand_detection_confidence=0.7,
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min_hand_presence_confidence=0.7,
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min_tracking_confidence=0.5
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)
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hand_landmarker = HandLandmarker.create_from_options(options)
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cap = cv2.VideoCapture(0)
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if not cap.isOpened():
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print("Ошибка: не удалось открыть камеру")
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exit()
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print("Нажмите 'q' для выхода")
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hand_colors = [
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(0, 255, 0),
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(255, 0, 0),
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(0, 255, 255),
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(255, 0, 255),
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]
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def recognize_gesture(hand_landmarks, handedness, fingers_up):
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"""
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Распознаёт жесты на основе положения пальцев.
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hand_landmarks: список из 21 точки (x, y)
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handedness: 'Left' или 'Right'
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fingers_up: список названий поднятых пальцев
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Возвращает: название жеста или None
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"""
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# === Жест "ОК" ===
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# Кончики большого (4) и указательного (8) пальцев соединены
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thumb_tip = hand_landmarks[4]
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index_tip = hand_landmarks[8]
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distance_thumb_index = math.sqrt(
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(thumb_tip[0] - index_tip[0])**2 +
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(thumb_tip[1] - index_tip[1])**2
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)
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# Порог расстояния (в пикселях) - зависит от размера руки в кадре
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pinch_threshold = 50
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if distance_thumb_index < pinch_threshold:
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# Проверяем, что остальные пальцы не обязательно подняты
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return "OK"
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||||||
|
|
||||||
# === Жест "Большой палец вверх" ===
|
|
||||||
# Только большой палец поднят, остальные согнуты
|
|
||||||
if fingers_up == ['Большой']:
|
|
||||||
# Дополнительно проверяем, что большой палец направлен вверх
|
|
||||||
thumb_tip = hand_landmarks[4]
|
|
||||||
thumb_ip = hand_landmarks[3]
|
|
||||||
if thumb_tip[1] > thumb_ip[1]: # Кончик выше сустава
|
|
||||||
return "THUMBS_UP"
|
|
||||||
|
|
||||||
# === Жест "Победа/Мир" (V sign) ===
|
|
||||||
# Указательный и средний подняты, остальные согнуты
|
|
||||||
if set(fingers_up) == {'Указательный', 'Средний'}:
|
|
||||||
return "PEACE"
|
|
||||||
|
|
||||||
# === Жест "Стоп/Пять" ===
|
|
||||||
# Все 5 пальцев подняты
|
|
||||||
if len(fingers_up) == 5:
|
|
||||||
return "STOP"
|
|
||||||
|
|
||||||
# === Жест "Рок/Коза" ===
|
|
||||||
# Указательный и мизинец подняты, остальные согнуты
|
|
||||||
if set(fingers_up) == {'Указательный', 'Мизинец'}:
|
|
||||||
return "ROCK"
|
|
||||||
|
|
||||||
# === Жест "Указание" ===
|
|
||||||
# Только указательный палец поднят
|
|
||||||
if fingers_up == ['Указательный']:
|
|
||||||
return "POINT"
|
|
||||||
|
|
||||||
# === Жест "Кулак" ===
|
|
||||||
# Ни один палец не поднят
|
|
||||||
if len(fingers_up) == 0:
|
|
||||||
return "FIST"
|
|
||||||
|
|
||||||
# === Жест "Три" ===
|
|
||||||
# Указательный, средний и безымянный подняты
|
|
||||||
if set(fingers_up) == {'Указательный', 'Средний', 'Безымянный'}:
|
|
||||||
return "THREE"
|
|
||||||
|
|
||||||
# === Жест "Четыре" ===
|
|
||||||
# Четыре пальца подняты (все кроме большого)
|
|
||||||
if set(fingers_up) == {'Указательный', 'Средний', 'Безымянный', 'Мизинец'}:
|
|
||||||
return "FOUR"
|
|
||||||
|
|
||||||
# Жест не распознан
|
|
||||||
return None
|
|
||||||
|
|
||||||
def count_fingers(hand_landmarks, handedness):
|
|
||||||
"""Подсчитывает количество поднятых пальцев"""
|
|
||||||
fingers_up = []
|
|
||||||
|
|
||||||
# Большой палец
|
|
||||||
thumb_tip = hand_landmarks[4]
|
|
||||||
thumb_ip = hand_landmarks[3]
|
|
||||||
if handedness == 'Right':
|
|
||||||
if thumb_ip[0] > thumb_tip[0] :
|
|
||||||
fingers_up.append('Большой')
|
|
||||||
else:
|
|
||||||
if thumb_ip[0] > thumb_tip[0] :
|
|
||||||
fingers_up.append('Большой')
|
|
||||||
|
|
||||||
# Указательный
|
|
||||||
if hand_landmarks[8][1] < hand_landmarks[6][1]:
|
|
||||||
fingers_up.append('Указательный')
|
|
||||||
|
|
||||||
# Средний
|
|
||||||
if hand_landmarks[12][1] < hand_landmarks[10][1]:
|
|
||||||
fingers_up.append('Средний')
|
|
||||||
|
|
||||||
# Безымянный
|
|
||||||
if hand_landmarks[16][1] < hand_landmarks[14][1]:
|
|
||||||
fingers_up.append('Безымянный')
|
|
||||||
|
|
||||||
# Мизинец
|
|
||||||
if hand_landmarks[20][1] < hand_landmarks[18][1]:
|
|
||||||
fingers_up.append('Мизинец')
|
|
||||||
|
|
||||||
return len(fingers_up), fingers_up
|
|
||||||
|
|
||||||
|
|
||||||
def detect_palm_orientation(hand_landmarks, handedness):
|
|
||||||
"""
|
|
||||||
Определяет ориентацию ладони: к камере или тыльной стороной.
|
|
||||||
|
|
||||||
Логика метода:
|
|
||||||
- Берём большой палец (точка 4) и мизинец (точка 17)
|
|
||||||
- Сравниваем их x-координаты
|
|
||||||
- Для ПРАВОЙ руки: если большой палец ЛЕВЕЕ мизинца (x меньше) → ладонь к камере
|
|
||||||
- Для ЛЕВОЙ руки: если большой палец ПРАВЕЕ мизинца (x больше) → ладонь к камере
|
|
||||||
|
|
||||||
Почему так:
|
|
||||||
- Когда правая рука показывает ладонью к камере, большой палец оказывается слева
|
|
||||||
- Когда правая рука показывает тыльной стороной, большой палец оказывается справа
|
|
||||||
- Для левой руки всё зеркально
|
|
||||||
|
|
||||||
Возвращает: 'palm' (ладонь к камере) или 'back' (тыльная сторона)
|
|
||||||
"""
|
|
||||||
thumb_tip = hand_landmarks[4] # Кончик большого пальца
|
|
||||||
pinky_mcp = hand_landmarks[17] # Основание мизинца
|
|
||||||
|
|
||||||
thumb_x = thumb_tip[0]
|
|
||||||
pinky_x = pinky_mcp[0]
|
|
||||||
|
|
||||||
if handedness == 'Right':
|
|
||||||
# Правая рука: большой палец слева от мизинца → ладонь к камере
|
|
||||||
if thumb_x < pinky_x:
|
|
||||||
return 'back'
|
|
||||||
else:
|
|
||||||
return 'palm'
|
|
||||||
else:
|
|
||||||
# Левая рука: большой палец справа от мизинца → ладонь к камере
|
|
||||||
if thumb_x > pinky_x:
|
|
||||||
return 'back'
|
|
||||||
else:
|
|
||||||
return 'palm'
|
|
||||||
|
|
||||||
|
|
||||||
frame_counter = 0
|
|
||||||
|
|
||||||
while True:
|
|
||||||
ret, frame = cap.read()
|
|
||||||
if not ret:
|
|
||||||
break
|
|
||||||
|
|
||||||
rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
|
|
||||||
mp_image = mp.Image(image_format=mp.ImageFormat.SRGB, data=rgb_frame)
|
|
||||||
|
|
||||||
result = hand_landmarker.detect(mp_image)
|
|
||||||
|
|
||||||
h, w, _ = frame.shape
|
|
||||||
total_fingers = 0
|
|
||||||
|
|
||||||
if result.hand_landmarks:
|
|
||||||
for hand_idx, hand_landmarks in enumerate(result.hand_landmarks):
|
|
||||||
color = hand_colors[hand_idx % len(hand_colors)]
|
|
||||||
handedness = result.handedness[hand_idx][0].category_name
|
|
||||||
|
|
||||||
points = []
|
|
||||||
for lm in hand_landmarks:
|
|
||||||
px = int(lm.x * w)
|
|
||||||
py = int(lm.y * h)
|
|
||||||
points.append((px, py))
|
|
||||||
|
|
||||||
# Подсчёт пальцев
|
|
||||||
fingers_count, fingers_names = count_fingers(points, handedness)
|
|
||||||
total_fingers += fingers_count
|
|
||||||
|
|
||||||
# Определение ориентации ладони
|
|
||||||
orientation = detect_palm_orientation(points, handedness)
|
|
||||||
|
|
||||||
# === РАСПОЗНАВАНИЕ ЖЕСТА ===
|
|
||||||
gesture = recognize_gesture(points, handedness, fingers_names)
|
|
||||||
|
|
||||||
# Рисуем соединения
|
|
||||||
for connection in HAND_CONNECTIONS:
|
|
||||||
start_idx, end_idx = connection
|
|
||||||
if start_idx < len(points) and end_idx < len(points):
|
|
||||||
pt1 = points[start_idx]
|
|
||||||
pt2 = points[end_idx]
|
|
||||||
# Если ладонь к камере - рисуем жирнее и ярче
|
|
||||||
thickness = 3 if orientation == 'palm' else 2
|
|
||||||
cv2.line(frame, pt1, pt2, color, thickness)
|
|
||||||
|
|
||||||
# Рисуем точки
|
|
||||||
for i, (px, py) in enumerate(points):
|
|
||||||
if i == 0:
|
|
||||||
cv2.circle(frame, (px, py), 8, color, -1)
|
|
||||||
cv2.circle(frame, (px, py), 8, (255, 255, 255), 2)
|
|
||||||
elif i in [4, 8, 12, 16, 20]:
|
|
||||||
cv2.circle(frame, (px, py), 7, color, -1)
|
|
||||||
cv2.circle(frame, (px, py), 7, (255, 255, 255), 2)
|
|
||||||
else:
|
|
||||||
cv2.circle(frame, (px, py), 5, color, -1)
|
|
||||||
|
|
||||||
# Подписи
|
|
||||||
if points:
|
|
||||||
wrist_x, wrist_y = points[0]
|
|
||||||
|
|
||||||
# Название руки
|
|
||||||
cv2.putText(frame, f"Hand {hand_idx+1}: {handedness}",
|
|
||||||
(wrist_x - 50, wrist_y - 40),
|
|
||||||
cv2.FONT_HERSHEY_SIMPLEX, 0.7, color, 2)
|
|
||||||
|
|
||||||
# Ориентация ладони (с цветовой индикацией)
|
|
||||||
if orientation == 'palm':
|
|
||||||
orient_text = "LADON (palm)"
|
|
||||||
orient_color = (0, 255, 0) # Зелёный
|
|
||||||
else:
|
|
||||||
orient_text = "TYLNAYA (back)"
|
|
||||||
orient_color = (0, 0, 255) # Красный
|
|
||||||
|
|
||||||
cv2.putText(frame, orient_text,
|
|
||||||
(wrist_x - 50, wrist_y - 10),
|
|
||||||
cv2.FONT_HERSHEY_SIMPLEX, 0.6, orient_color, 2)
|
|
||||||
|
|
||||||
# === ОТОБРАЖЕНИЕ ЖЕСТА ===
|
|
||||||
if gesture:
|
|
||||||
gesture_color = (0, 255, 255) # Жёлтый для распознанного жеста
|
|
||||||
cv2.putText(frame, f"GESTURE: {gesture}",
|
|
||||||
(wrist_x - 50, wrist_y),
|
|
||||||
cv2.FONT_HERSHEY_SIMPLEX, 0.9, gesture_color, 3)
|
|
||||||
else:
|
|
||||||
cv2.putText(frame, "No gesture",
|
|
||||||
(wrist_x - 50, wrist_y),
|
|
||||||
cv2.FONT_HERSHEY_SIMPLEX, 0.6, (128, 128, 128), 2)
|
|
||||||
|
|
||||||
# Количество пальцев
|
|
||||||
cv2.putText(frame, f"Fingers: {fingers_count}",
|
|
||||||
(wrist_x - 50, wrist_y + 25),
|
|
||||||
cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 255), 3)
|
|
||||||
|
|
||||||
# Список пальцев
|
|
||||||
if fingers_names:
|
|
||||||
fingers_text = ', '.join(fingers_names)
|
|
||||||
cv2.putText(frame, fingers_text,
|
|
||||||
(wrist_x - 50, wrist_y + 55),
|
|
||||||
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 0), 2)
|
|
||||||
|
|
||||||
# Большая панель с общей информацией
|
|
||||||
cv2.rectangle(frame, (10, 10), (350, 120), (0, 0, 0), -1)
|
|
||||||
cv2.putText(frame, f"Total fingers: {total_fingers}",
|
|
||||||
(20, 50), cv2.FONT_HERSHEY_SIMPLEX, 1.5, (0, 255, 255), 3)
|
|
||||||
|
|
||||||
hands_count = len(result.hand_landmarks) if result.hand_landmarks else 0
|
|
||||||
cv2.putText(frame, f"Hands detected: {hands_count}",
|
|
||||||
(20, 90), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 255, 255), 2)
|
|
||||||
|
|
||||||
# Вывод в консоль (раз в 30 кадров)
|
|
||||||
frame_counter += 1
|
|
||||||
if result.hand_landmarks and frame_counter % 30 == 0:
|
|
||||||
print(f"\n=== Кадр {frame_counter} ===")
|
|
||||||
print(f"Рук обнаружено: {hands_count}, всего пальцев: {total_fingers}")
|
|
||||||
for hand_idx, hand_landmarks in enumerate(result.hand_landmarks):
|
|
||||||
handedness = result.handedness[hand_idx][0].category_name
|
|
||||||
points = [(int(lm.x * w), int(lm.y * h)) for lm in hand_landmarks]
|
|
||||||
fingers_count, fingers_names = count_fingers(points, handedness)
|
|
||||||
orientation = detect_palm_orientation(points, handedness)
|
|
||||||
orient_str = "ЛАДОНЬ" if orientation == 'palm' else "ТЫЛЬНАЯ"
|
|
||||||
fingers_str = ', '.join(fingers_names) if fingers_names else 'нет'
|
|
||||||
print(f" Рука {hand_idx+1} ({handedness}): ориентация={orient_str}, "
|
|
||||||
f"пальцев={fingers_count} [{fingers_str}]")
|
|
||||||
|
|
||||||
cv2.imshow("Hand Detection - Palm Orientation", frame)
|
|
||||||
|
|
||||||
if cv2.waitKey(1) & 0xFF == ord('q'):
|
|
||||||
break
|
|
||||||
|
|
||||||
cap.release()
|
|
||||||
cv2.destroyAllWindows()
|
|
||||||
print("Завершено.")
|
|
||||||
Reference in New Issue
Block a user