распознавание пальцев рук
Co-authored-by: Cursor <cursoragent@cursor.com>
This commit is contained in:
Торов Михаил Дмитриевич
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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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