Book a Demo!
CoCalc Logo Icon
StoreFeaturesDocsShareSupportNewsAboutPoliciesSign UpSign In
tensorflow
GitHub Repository: tensorflow/docs-l10n
 Path: blob/master/site/pt-br/hub/tutorials/movenet.ipynb
25118 views
Kernel: Python 3

Licensed under the Apache License, Version 2.0 (the "License");

# Copyright 2021 The TensorFlow Hub Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================

MoveNet – Modelo de detecção de poses preciso e ultrarrápido

MoveNet é um modelo preciso e ultrarrápido que detecta 17 pontos-chave do corpo. O modelo é disponibilizado no TF Hub com duas variantes, conhecidas como Lightning e Thunder. A variante Lightning é destinada a aplicações em que a latência é crítica, enquanto a variante Thunder é destinada a aplicações que requerem uma alta exatidão. Os dois modelos são executados mais rápidos que em tempo real (mais de 30 quadros por segundo) na maioria dos computadores, notebooks e celulares modernos, o que é essencial para aplicativos de exercícios, saúde e bem-estar em tempo real.

drawing

*Imagens baixadas de Pexels (https://www.pexels.com/)

Este Colab descreve como carregar o MoveNet e executar a inferência para a imagem e vídeo de entrada abaixo.

Observação: confira a demonstração em tempo real para ver como o modelo funciona!

Estimativa de pose humana usando o MoveNet

Veja as bibliotecas e importações

!pip install -q imageio
!pip install -q opencv-python
!pip install -q git+https://github.com/tensorflow/docs

import tensorflow as tf
import tensorflow_hub as hub
from tensorflow_docs.vis import embed
import numpy as np
import cv2

# Import matplotlib libraries
from matplotlib import pyplot as plt
from matplotlib.collections import LineCollection
import matplotlib.patches as patches

# Some modules to display an animation using imageio.
import imageio
from IPython.display import HTML, display

#@title Helper functions for visualization

# Dictionary that maps from joint names to keypoint indices.
KEYPOINT_DICT = {
    'nose': 0,
    'left_eye': 1,
    'right_eye': 2,
    'left_ear': 3,
    'right_ear': 4,
    'left_shoulder': 5,
    'right_shoulder': 6,
    'left_elbow': 7,
    'right_elbow': 8,
    'left_wrist': 9,
    'right_wrist': 10,
    'left_hip': 11,
    'right_hip': 12,
    'left_knee': 13,
    'right_knee': 14,
    'left_ankle': 15,
    'right_ankle': 16
}

# Maps bones to a matplotlib color name.
KEYPOINT_EDGE_INDS_TO_COLOR = {
    (0, 1): 'm',
    (0, 2): 'c',
    (1, 3): 'm',
    (2, 4): 'c',
    (0, 5): 'm',
    (0, 6): 'c',
    (5, 7): 'm',
    (7, 9): 'm',
    (6, 8): 'c',
    (8, 10): 'c',
    (5, 6): 'y',
    (5, 11): 'm',
    (6, 12): 'c',
    (11, 12): 'y',
    (11, 13): 'm',
    (13, 15): 'm',
    (12, 14): 'c',
    (14, 16): 'c'
}

def _keypoints_and_edges_for_display(keypoints_with_scores,
                                     height,
                                     width,
                                     keypoint_threshold=0.11):
  """Returns high confidence keypoints and edges for visualization.

  Args:
    keypoints_with_scores: A numpy array with shape [1, 1, 17, 3] representing
      the keypoint coordinates and scores returned from the MoveNet model.
    height: height of the image in pixels.
    width: width of the image in pixels.
    keypoint_threshold: minimum confidence score for a keypoint to be
      visualized.

  Returns:
    A (keypoints_xy, edges_xy, edge_colors) containing:
      * the coordinates of all keypoints of all detected entities;
      * the coordinates of all skeleton edges of all detected entities;
      * the colors in which the edges should be plotted.
  """
  keypoints_all = []
  keypoint_edges_all = []
  edge_colors = []
  num_instances, _, _, _ = keypoints_with_scores.shape
  for idx in range(num_instances):
    kpts_x = keypoints_with_scores[0, idx, :, 1]
    kpts_y = keypoints_with_scores[0, idx, :, 0]
    kpts_scores = keypoints_with_scores[0, idx, :, 2]
    kpts_absolute_xy = np.stack(
        [width * np.array(kpts_x), height * np.array(kpts_y)], axis=-1)
    kpts_above_thresh_absolute = kpts_absolute_xy[
        kpts_scores > keypoint_threshold, :]
    keypoints_all.append(kpts_above_thresh_absolute)

    for edge_pair, color in KEYPOINT_EDGE_INDS_TO_COLOR.items():
      if (kpts_scores[edge_pair[0]] > keypoint_threshold and
          kpts_scores[edge_pair[1]] > keypoint_threshold):
        x_start = kpts_absolute_xy[edge_pair[0], 0]
        y_start = kpts_absolute_xy[edge_pair[0], 1]
        x_end = kpts_absolute_xy[edge_pair[1], 0]
        y_end = kpts_absolute_xy[edge_pair[1], 1]
        line_seg = np.array([[x_start, y_start], [x_end, y_end]])
        keypoint_edges_all.append(line_seg)
        edge_colors.append(color)
  if keypoints_all:
    keypoints_xy = np.concatenate(keypoints_all, axis=0)
  else:
    keypoints_xy = np.zeros((0, 17, 2))

  if keypoint_edges_all:
    edges_xy = np.stack(keypoint_edges_all, axis=0)
  else:
    edges_xy = np.zeros((0, 2, 2))
  return keypoints_xy, edges_xy, edge_colors


def draw_prediction_on_image(
    image, keypoints_with_scores, crop_region=None, close_figure=False,
    output_image_height=None):
  """Draws the keypoint predictions on image.

  Args:
    image: A numpy array with shape [height, width, channel] representing the
      pixel values of the input image.
    keypoints_with_scores: A numpy array with shape [1, 1, 17, 3] representing
      the keypoint coordinates and scores returned from the MoveNet model.
    crop_region: A dictionary that defines the coordinates of the bounding box
      of the crop region in normalized coordinates (see the init_crop_region
      function below for more detail). If provided, this function will also
      draw the bounding box on the image.
    output_image_height: An integer indicating the height of the output image.
      Note that the image aspect ratio will be the same as the input image.

  Returns:
    A numpy array with shape [out_height, out_width, channel] representing the
    image overlaid with keypoint predictions.
  """
  height, width, channel = image.shape
  aspect_ratio = float(width) / height
  fig, ax = plt.subplots(figsize=(12 * aspect_ratio, 12))
  # To remove the huge white borders
  fig.tight_layout(pad=0)
  ax.margins(0)
  ax.set_yticklabels([])
  ax.set_xticklabels([])
  plt.axis('off')

  im = ax.imshow(image)
  line_segments = LineCollection([], linewidths=(4), linestyle='solid')
  ax.add_collection(line_segments)
  # Turn off tick labels
  scat = ax.scatter([], [], s=60, color='#FF1493', zorder=3)

  (keypoint_locs, keypoint_edges,
   edge_colors) = _keypoints_and_edges_for_display(
       keypoints_with_scores, height, width)

  line_segments.set_segments(keypoint_edges)
  line_segments.set_color(edge_colors)
  if keypoint_edges.shape[0]:
    line_segments.set_segments(keypoint_edges)
    line_segments.set_color(edge_colors)
  if keypoint_locs.shape[0]:
    scat.set_offsets(keypoint_locs)

  if crop_region is not None:
    xmin = max(crop_region['x_min'] * width, 0.0)
    ymin = max(crop_region['y_min'] * height, 0.0)
    rec_width = min(crop_region['x_max'], 0.99) * width - xmin
    rec_height = min(crop_region['y_max'], 0.99) * height - ymin
    rect = patches.Rectangle(
        (xmin,ymin),rec_width,rec_height,
        linewidth=1,edgecolor='b',facecolor='none')
    ax.add_patch(rect)

  fig.canvas.draw()
  image_from_plot = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8)
  image_from_plot = image_from_plot.reshape(
      fig.canvas.get_width_height()[::-1] + (3,))
  plt.close(fig)
  if output_image_height is not None:
    output_image_width = int(output_image_height / height * width)
    image_from_plot = cv2.resize(
        image_from_plot, dsize=(output_image_width, output_image_height),
         interpolation=cv2.INTER_CUBIC)
  return image_from_plot

def to_gif(images, duration):
  """Converts image sequence (4D numpy array) to gif."""
  imageio.mimsave('./animation.gif', images, duration=duration)
  return embed.embed_file('./animation.gif')

def progress(value, max=100):
  return HTML("""
      <progress
          value='{value}'
          max='{max}',
          style='width: 100%'
      >
          {value}
      </progress>
  """.format(value=value, max=max))

Carregue o modelo pelo TF Hub

model_name = "movenet_lightning" #@param ["movenet_lightning", "movenet_thunder", "movenet_lightning_f16.tflite", "movenet_thunder_f16.tflite", "movenet_lightning_int8.tflite", "movenet_thunder_int8.tflite"]

if "tflite" in model_name:
  if "movenet_lightning_f16" in model_name:
    !wget -q -O model.tflite https://tfhub.dev/google/lite-model/movenet/singlepose/lightning/tflite/float16/4?lite-format=tflite
    input_size = 192
  elif "movenet_thunder_f16" in model_name:
    !wget -q -O model.tflite https://tfhub.dev/google/lite-model/movenet/singlepose/thunder/tflite/float16/4?lite-format=tflite
    input_size = 256
  elif "movenet_lightning_int8" in model_name:
    !wget -q -O model.tflite https://tfhub.dev/google/lite-model/movenet/singlepose/lightning/tflite/int8/4?lite-format=tflite
    input_size = 192
  elif "movenet_thunder_int8" in model_name:
    !wget -q -O model.tflite https://tfhub.dev/google/lite-model/movenet/singlepose/thunder/tflite/int8/4?lite-format=tflite
    input_size = 256
  else:
    raise ValueError("Unsupported model name: %s" % model_name)

  # Initialize the TFLite interpreter
  interpreter = tf.lite.Interpreter(model_path="model.tflite")
  interpreter.allocate_tensors()

  def movenet(input_image):
    """Runs detection on an input image.

    Args:
      input_image: A [1, height, width, 3] tensor represents the input image
        pixels. Note that the height/width should already be resized and match the
        expected input resolution of the model before passing into this function.

    Returns:
      A [1, 1, 17, 3] float numpy array representing the predicted keypoint
      coordinates and scores.
    """
    # TF Lite format expects tensor type of uint8.
    input_image = tf.cast(input_image, dtype=tf.uint8)
    input_details = interpreter.get_input_details()
    output_details = interpreter.get_output_details()
    interpreter.set_tensor(input_details[0]['index'], input_image.numpy())
    # Invoke inference.
    interpreter.invoke()
    # Get the model prediction.
    keypoints_with_scores = interpreter.get_tensor(output_details[0]['index'])
    return keypoints_with_scores

else:
  if "movenet_lightning" in model_name:
    module = hub.load("https://tfhub.dev/google/movenet/singlepose/lightning/4")
    input_size = 192
  elif "movenet_thunder" in model_name:
    module = hub.load("https://tfhub.dev/google/movenet/singlepose/thunder/4")
    input_size = 256
  else:
    raise ValueError("Unsupported model name: %s" % model_name)

  def movenet(input_image):
    """Runs detection on an input image.

    Args:
      input_image: A [1, height, width, 3] tensor represents the input image
        pixels. Note that the height/width should already be resized and match the
        expected input resolution of the model before passing into this function.

    Returns:
      A [1, 1, 17, 3] float numpy array representing the predicted keypoint
      coordinates and scores.
    """
    model = module.signatures['serving_default']

    # SavedModel format expects tensor type of int32.
    input_image = tf.cast(input_image, dtype=tf.int32)
    # Run model inference.
    outputs = model(input_image)
    # Output is a [1, 1, 17, 3] tensor.
    keypoints_with_scores = outputs['output_0'].numpy()
    return keypoints_with_scores

Exemplo com uma única imagem

Esta sessão demonstra um exemplo mínimo funcional de execução do modelo em uma única imagem para prever os 17 pontos-chave humanos.

Carregue a imagem de entrada

!curl -o input_image.jpeg https://images.pexels.com/photos/4384679/pexels-photo-4384679.jpeg --silent

# Load the input image.
image_path = 'input_image.jpeg'
image = tf.io.read_file(image_path)
image = tf.image.decode_jpeg(image)

Execute a inferência

# Resize and pad the image to keep the aspect ratio and fit the expected size.
input_image = tf.expand_dims(image, axis=0)
input_image = tf.image.resize_with_pad(input_image, input_size, input_size)

# Run model inference.
keypoints_with_scores = movenet(input_image)

# Visualize the predictions with image.
display_image = tf.expand_dims(image, axis=0)
display_image = tf.cast(tf.image.resize_with_pad(
    display_image, 1280, 1280), dtype=tf.int32)
output_overlay = draw_prediction_on_image(
    np.squeeze(display_image.numpy(), axis=0), keypoints_with_scores)

plt.figure(figsize=(5, 5))
plt.imshow(output_overlay)
_ = plt.axis('off')

Exemplo de um vídeo (sequência de imagens)

Esta seção demonstra como aplicar um corte inteligente baseado em detecções do quadro anterior quando a entrada é uma sequência de quadros, o que permite que o modelo dedique sua atenção e recursos ao objeto principal, resultando em uma qualidade de previsão muito melhor sem sacrificar a velocidade.

#@title Cropping Algorithm

# Confidence score to determine whether a keypoint prediction is reliable.
MIN_CROP_KEYPOINT_SCORE = 0.2

def init_crop_region(image_height, image_width):
  """Defines the default crop region.

  The function provides the initial crop region (pads the full image from both
  sides to make it a square image) when the algorithm cannot reliably determine
  the crop region from the previous frame.
  """
  if image_width > image_height:
    box_height = image_width / image_height
    box_width = 1.0
    y_min = (image_height / 2 - image_width / 2) / image_height
    x_min = 0.0
  else:
    box_height = 1.0
    box_width = image_height / image_width
    y_min = 0.0
    x_min = (image_width / 2 - image_height / 2) / image_width

  return {
    'y_min': y_min,
    'x_min': x_min,
    'y_max': y_min + box_height,
    'x_max': x_min + box_width,
    'height': box_height,
    'width': box_width
  }

def torso_visible(keypoints):
  """Checks whether there are enough torso keypoints.

  This function checks whether the model is confident at predicting one of the
  shoulders/hips which is required to determine a good crop region.
  """
  return ((keypoints[0, 0, KEYPOINT_DICT['left_hip'], 2] >
           MIN_CROP_KEYPOINT_SCORE or
          keypoints[0, 0, KEYPOINT_DICT['right_hip'], 2] >
           MIN_CROP_KEYPOINT_SCORE) and
          (keypoints[0, 0, KEYPOINT_DICT['left_shoulder'], 2] >
           MIN_CROP_KEYPOINT_SCORE or
          keypoints[0, 0, KEYPOINT_DICT['right_shoulder'], 2] >
           MIN_CROP_KEYPOINT_SCORE))

def determine_torso_and_body_range(
    keypoints, target_keypoints, center_y, center_x):
  """Calculates the maximum distance from each keypoints to the center location.

  The function returns the maximum distances from the two sets of keypoints:
  full 17 keypoints and 4 torso keypoints. The returned information will be
  used to determine the crop size. See determineCropRegion for more detail.
  """
  torso_joints = ['left_shoulder', 'right_shoulder', 'left_hip', 'right_hip']
  max_torso_yrange = 0.0
  max_torso_xrange = 0.0
  for joint in torso_joints:
    dist_y = abs(center_y - target_keypoints[joint][0])
    dist_x = abs(center_x - target_keypoints[joint][1])
    if dist_y > max_torso_yrange:
      max_torso_yrange = dist_y
    if dist_x > max_torso_xrange:
      max_torso_xrange = dist_x

  max_body_yrange = 0.0
  max_body_xrange = 0.0
  for joint in KEYPOINT_DICT.keys():
    if keypoints[0, 0, KEYPOINT_DICT[joint], 2] < MIN_CROP_KEYPOINT_SCORE:
      continue
    dist_y = abs(center_y - target_keypoints[joint][0]);
    dist_x = abs(center_x - target_keypoints[joint][1]);
    if dist_y > max_body_yrange:
      max_body_yrange = dist_y

    if dist_x > max_body_xrange:
      max_body_xrange = dist_x

  return [max_torso_yrange, max_torso_xrange, max_body_yrange, max_body_xrange]

def determine_crop_region(
      keypoints, image_height,
      image_width):
  """Determines the region to crop the image for the model to run inference on.

  The algorithm uses the detected joints from the previous frame to estimate
  the square region that encloses the full body of the target person and
  centers at the midpoint of two hip joints. The crop size is determined by
  the distances between each joints and the center point.
  When the model is not confident with the four torso joint predictions, the
  function returns a default crop which is the full image padded to square.
  """
  target_keypoints = {}
  for joint in KEYPOINT_DICT.keys():
    target_keypoints[joint] = [
      keypoints[0, 0, KEYPOINT_DICT[joint], 0] * image_height,
      keypoints[0, 0, KEYPOINT_DICT[joint], 1] * image_width
    ]

  if torso_visible(keypoints):
    center_y = (target_keypoints['left_hip'][0] +
                target_keypoints['right_hip'][0]) / 2;
    center_x = (target_keypoints['left_hip'][1] +
                target_keypoints['right_hip'][1]) / 2;

    (max_torso_yrange, max_torso_xrange,
      max_body_yrange, max_body_xrange) = determine_torso_and_body_range(
          keypoints, target_keypoints, center_y, center_x)

    crop_length_half = np.amax(
        [max_torso_xrange * 1.9, max_torso_yrange * 1.9,
          max_body_yrange * 1.2, max_body_xrange * 1.2])

    tmp = np.array(
        [center_x, image_width - center_x, center_y, image_height - center_y])
    crop_length_half = np.amin(
        [crop_length_half, np.amax(tmp)]);

    crop_corner = [center_y - crop_length_half, center_x - crop_length_half];

    if crop_length_half > max(image_width, image_height) / 2:
      return init_crop_region(image_height, image_width)
    else:
      crop_length = crop_length_half * 2;
      return {
        'y_min': crop_corner[0] / image_height,
        'x_min': crop_corner[1] / image_width,
        'y_max': (crop_corner[0] + crop_length) / image_height,
        'x_max': (crop_corner[1] + crop_length) / image_width,
        'height': (crop_corner[0] + crop_length) / image_height -
            crop_corner[0] / image_height,
        'width': (crop_corner[1] + crop_length) / image_width -
            crop_corner[1] / image_width
      }
  else:
    return init_crop_region(image_height, image_width)

def crop_and_resize(image, crop_region, crop_size):
  """Crops and resize the image to prepare for the model input."""
  boxes=[[crop_region['y_min'], crop_region['x_min'],
          crop_region['y_max'], crop_region['x_max']]]
  output_image = tf.image.crop_and_resize(
      image, box_indices=[0], boxes=boxes, crop_size=crop_size)
  return output_image

def run_inference(movenet, image, crop_region, crop_size):
  """Runs model inferece on the cropped region.

  The function runs the model inference on the cropped region and updates the
  model output to the original image coordinate system.
  """
  image_height, image_width, _ = image.shape
  input_image = crop_and_resize(
    tf.expand_dims(image, axis=0), crop_region, crop_size=crop_size)
  # Run model inference.
  keypoints_with_scores = movenet(input_image)
  # Update the coordinates.
  for idx in range(17):
    keypoints_with_scores[0, 0, idx, 0] = (
        crop_region['y_min'] * image_height +
        crop_region['height'] * image_height *
        keypoints_with_scores[0, 0, idx, 0]) / image_height
    keypoints_with_scores[0, 0, idx, 1] = (
        crop_region['x_min'] * image_width +
        crop_region['width'] * image_width *
        keypoints_with_scores[0, 0, idx, 1]) / image_width
  return keypoints_with_scores

Carregue a sequência de imagens de entrada

!wget -q -O dance.gif https://github.com/tensorflow/tfjs-models/raw/master/pose-detection/assets/dance_input.gif

# Load the input image.
image_path = 'dance.gif'
image = tf.io.read_file(image_path)
image = tf.image.decode_gif(image)

Execute a inferência com o algoritmo de corte

# Load the input image.
num_frames, image_height, image_width, _ = image.shape
crop_region = init_crop_region(image_height, image_width)

output_images = []
bar = display(progress(0, num_frames-1), display_id=True)
for frame_idx in range(num_frames):
  keypoints_with_scores = run_inference(
      movenet, image[frame_idx, :, :, :], crop_region,
      crop_size=[input_size, input_size])
  output_images.append(draw_prediction_on_image(
      image[frame_idx, :, :, :].numpy().astype(np.int32),
      keypoints_with_scores, crop_region=None,
      close_figure=True, output_image_height=300))
  crop_region = determine_crop_region(
      keypoints_with_scores, image_height, image_width)
  bar.update(progress(frame_idx, num_frames-1))

# Prepare gif visualization.
output = np.stack(output_images, axis=0)
to_gif(output, duration=100)