I am trying to predict with a ANN classification model made in Tensorflow to classify pose keypoints with MediaPipe. The mediapipe pose tracker has 33 keypoints for x y and z coordinates for a total of 99 data points.
I am training for 4 classes.
This is running the pose embedding
import mediapipe as mp
import numpy as np
import tensorflow as tf
from tensorflow import keras
mp_pose = mp.solutions.pose
def get_center_point(landmarks, left_bodypart, right_bodypart):
"""Calculates the center point of the two given landmarks."""
left = tf.gather(landmarks, left_bodypart.value, axis=1)
right = tf.gather(landmarks, right_bodypart.value, axis=1)
center = left * 0.5 right * 0.5
return center
def get_pose_size(landmarks, torso_size_multiplier=2.5):
"""Calculates pose size.
It is the maximum of two values:
* Torso size multiplied by `torso_size_multiplier`
* Maximum distance from pose center to any pose landmark
"""
# Hips center
hips_center = get_center_point(landmarks, mp_pose.PoseLandmark.LEFT_HIP,
mp_pose.PoseLandmark.RIGHT_HIP)
# Shoulders center
shoulders_center = get_center_point(landmarks,mp_pose.PoseLandmark.LEFT_SHOULDER,
mp_pose.PoseLandmark.RIGHT_SHOULDER)
# Torso size as the minimum body size
torso_size = tf.linalg.norm(shoulders_center - hips_center)
# Pose center
pose_center_new = get_center_point(landmarks,mp_pose.PoseLandmark.LEFT_HIP,
mp_pose.PoseLandmark.RIGHT_HIP)
pose_center_new = tf.expand_dims(pose_center_new, axis=1)
# Broadcast the pose center to the same size as the landmark vector to
# perform substraction
pose_center_new = tf.broadcast_to(pose_center_new,
[tf.size(landmarks) // (33*3), 33, 3])
# Dist to pose center
d = tf.gather(landmarks - pose_center_new, 0, axis=0,
name="dist_to_pose_center")
# Max dist to pose center
max_dist = tf.reduce_max(tf.linalg.norm(d, axis=0))
# Normalize scale
pose_size = tf.maximum(torso_size * torso_size_multiplier, max_dist)
return pose_size
def normalize_pose_landmarks(landmarks):
"""Normalizes the landmarks translation by moving the pose center to (0,0) and
scaling it to a constant pose size.
"""
# Move landmarks so that the pose center becomes (0,0)
pose_center = get_center_point(landmarks, mp_pose.PoseLandmark.LEFT_HIP,
mp_pose.PoseLandmark.RIGHT_HIP)
pose_center = tf.expand_dims(pose_center, axis=1)
# Broadcast the pose center to the same size as the landmark vector to perform
# substraction
pose_center = tf.broadcast_to(pose_center,
[tf.size(landmarks) // (33*3), 33, 3])
landmarks = landmarks - pose_center
# Scale the landmarks to a constant pose size
pose_size = get_pose_size(landmarks)
landmarks /= pose_size
return landmarks
def landmarks_to_embedding(landmarks_and_scores):
"""Converts the input landmarks into a pose embedding."""
# Reshape the flat input into a matrix with shape=(33, 3)
reshaped_inputs = keras.layers.Reshape((33, 3))(landmarks_and_scores)
# Normalize landmarks 3D
landmarks = normalize_pose_landmarks(reshaped_inputs[:, :, :3])
# Flatten the normalized landmark coordinates into a vector
embedding = keras.layers.Flatten()(landmarks)
return embedding
Then I create the model and feed the embedding inputs to it
import csv
import cv2
import itertools
import numpy as np
import pandas as pd
import os
import sys
import tempfile
import tqdm
import mediapipe as mp
from matplotlib import pyplot as plt
from matplotlib.collections import LineCollection
import tensorflow as tf
from tensorflow import keras
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
from poseEmbedding import get_center_point, get_pose_size, normalize_pose_landmarks, landmarks_to_embedding
def load_pose_landmarks(csv_path):
#load CSV file
dataframe = pd.read_csv(csv_path)
df_to_process = dataframe.copy()
#extract the list of class names
classes = df_to_process.pop('class_name').unique()
#extract the labels
y = df_to_process.pop('class_no')
#convert the input features and labels into float64 format for training
X = df_to_process.astype('float64')
y = keras.utils.to_categorical(y)
return X,y, classes, dataframe
csvs_out_train_path = 'train_data.csv'
csvs_out_test_path = 'test_data.csv'
#Load training data
X, y, class_names, _ = load_pose_landmarks(csvs_out_train_path)
#split training data(X,y) into (X_train, y_train) and (X_val, y_val)
X_train, X_val, y_train, y_val = train_test_split(X,y, test_size=0.15)
X_test, y_test, _, df_test = load_pose_landmarks(csvs_out_test_path)
mp_pose = mp.solutions.pose
inputs = tf.keras.Input(shape=(99))
embedding = landmarks_to_embedding(inputs)
layer = keras.layers.Dense(128, activation=tf.nn.relu6)(embedding)
layer = keras.layers.Dropout(0.5)(layer)
layer = keras.layers.Dense(64, activation=tf.nn.relu6)(layer)
layer = keras.layers.Dropout(0.5)(layer)
outputs = keras.layers.Dense(4, activation="softmax")(layer)
model = keras.Model(inputs, outputs)
#model.summary()
model.compile(
optimizer = 'adam',
loss = 'categorical_crossentropy',
metrics=['accuracy']
)
# Start training
history = model.fit(X_train, y_train,
epochs=200,
batch_size=16,
validation_data=(X_val, y_val))
model.save("complete_epoch_model")
# Visualize the training history to see whether you're overfitting.
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('Model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['TRAIN', 'VAL'], loc='lower right')
plt.show()
loss, accuracy = model.evaluate(X_test, y_test)
The model summary prints this out:
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_18 (InputLayer) [(None, 99)] 0 []
reshape_17 (Reshape) (None, 33, 3) 0 ['input_18[0][0]']
tf.__operators__.getitem_10 (S (None, 33, 3) 0 ['reshape_17[0][0]']
licingOpLambda)
tf.compat.v1.gather_69 (TFOpLa (None, 3) 0 ['tf.__operators__.getitem_10[0][
mbda) 0]']
tf.compat.v1.gather_70 (TFOpLa (None, 3) 0 ['tf.__operators__.getitem_10[0][
mbda) 0]']
tf.math.multiply_69 (TFOpLambd (None, 3) 0 ['tf.compat.v1.gather_69[0][0]']
a)
tf.math.multiply_70 (TFOpLambd (None, 3) 0 ['tf.compat.v1.gather_70[0][0]']
a)
tf.__operators__.add_31 (TFOpL (None, 3) 0 ['tf.math.multiply_69[0][0]',
ambda) 'tf.math.multiply_70[0][0]']
tf.compat.v1.size_17 (TFOpLamb () 0 ['tf.__operators__.getitem_10[0][
da) 0]']
tf.expand_dims_17 (TFOpLambda) (None, 1, 3) 0 ['tf.__operators__.add_31[0][0]']
tf.compat.v1.floor_div_17 (TFO () 0 ['tf.compat.v1.size_17[0][0]']
pLambda)
tf.broadcast_to_17 (TFOpLambda (None, 33, 3) 0 ['tf.expand_dims_17[0][0]',
) 'tf.compat.v1.floor_div_17[0][0]
']
tf.math.subtract_23 (TFOpLambd (None, 33, 3) 0 ['tf.__operators__.getitem_10[0][
a) 0]',
'tf.broadcast_to_17[0][0]']
tf.compat.v1.gather_75 (TFOpLa (None, 3) 0 ['tf.math.subtract_23[0][0]']
mbda)
tf.compat.v1.gather_76 (TFOpLa (None, 3) 0 ['tf.math.subtract_23[0][0]']
mbda)
tf.math.multiply_75 (TFOpLambd (None, 3) 0 ['tf.compat.v1.gather_75[0][0]']
a)
tf.math.multiply_76 (TFOpLambd (None, 3) 0 ['tf.compat.v1.gather_76[0][0]']
a)
tf.__operators__.add_34 (TFOpL (None, 3) 0 ['tf.math.multiply_75[0][0]',
ambda) 'tf.math.multiply_76[0][0]']
tf.compat.v1.size_18 (TFOpLamb () 0 ['tf.math.subtract_23[0][0]']
da)
tf.compat.v1.gather_73 (TFOpLa (None, 3) 0 ['tf.math.subtract_23[0][0]']
mbda)
tf.compat.v1.gather_74 (TFOpLa (None, 3) 0 ['tf.math.subtract_23[0][0]']
mbda)
tf.compat.v1.gather_71 (TFOpLa (None, 3) 0 ['tf.math.subtract_23[0][0]']
mbda)
tf.compat.v1.gather_72 (TFOpLa (None, 3) 0 ['tf.math.subtract_23[0][0]']
mbda)
tf.expand_dims_18 (TFOpLambda) (None, 1, 3) 0 ['tf.__operators__.add_34[0][0]']
tf.compat.v1.floor_div_18 (TFO () 0 ['tf.compat.v1.size_18[0][0]']
pLambda)
tf.math.multiply_73 (TFOpLambd (None, 3) 0 ['tf.compat.v1.gather_73[0][0]']
a)
tf.math.multiply_74 (TFOpLambd (None, 3) 0 ['tf.compat.v1.gather_74[0][0]']
a)
tf.math.multiply_71 (TFOpLambd (None, 3) 0 ['tf.compat.v1.gather_71[0][0]']
a)
tf.math.multiply_72 (TFOpLambd (None, 3) 0 ['tf.compat.v1.gather_72[0][0]']
a)
tf.broadcast_to_18 (TFOpLambda (None, 33, 3) 0 ['tf.expand_dims_18[0][0]',
) 'tf.compat.v1.floor_div_18[0][0]
']
tf.__operators__.add_33 (TFOpL (None, 3) 0 ['tf.math.multiply_73[0][0]',
ambda) 'tf.math.multiply_74[0][0]']
tf.__operators__.add_32 (TFOpL (None, 3) 0 ['tf.math.multiply_71[0][0]',
ambda) 'tf.math.multiply_72[0][0]']
tf.math.subtract_25 (TFOpLambd (None, 33, 3) 0 ['tf.math.subtract_23[0][0]',
a) 'tf.broadcast_to_18[0][0]']
tf.math.subtract_24 (TFOpLambd (None, 3) 0 ['tf.__operators__.add_33[0][0]',
a) 'tf.__operators__.add_32[0][0]']
tf.compat.v1.gather_77 (TFOpLa (33, 3) 0 ['tf.math.subtract_25[0][0]']
mbda)
tf.compat.v1.norm_14 (TFOpLamb () 0 ['tf.math.subtract_24[0][0]']
da)
tf.compat.v1.norm_15 (TFOpLamb (3,) 0 ['tf.compat.v1.gather_77[0][0]']
da)
tf.math.multiply_77 (TFOpLambd () 0 ['tf.compat.v1.norm_14[0][0]']
a)
tf.math.reduce_max_7 (TFOpLamb () 0 ['tf.compat.v1.norm_15[0][0]']
da)
tf.math.maximum_7 (TFOpLambda) () 0 ['tf.math.multiply_77[0][0]',
'tf.math.reduce_max_7[0][0]']
tf.math.truediv_7 (TFOpLambda) (None, 33, 3) 0 ['tf.math.subtract_23[0][0]',
'tf.math.maximum_7[0][0]']
flatten_7 (Flatten) (None, 99) 0 ['tf.math.truediv_7[0][0]']
dense_21 (Dense) (None, 128) 12800 ['flatten_7[0][0]']
dropout_14 (Dropout) (None, 128) 0 ['dense_21[0][0]']
dense_22 (Dense) (None, 64) 8256 ['dropout_14[0][0]']
dropout_15 (Dropout) (None, 64) 0 ['dense_22[0][0]']
dense_23 (Dense) (None, 4) 260 ['dropout_15[0][0]']
==================================================================================================
Total params: 21,316
Trainable params: 21,316
Non-trainable params: 0
__________________________________________________________________________________________________
Now when I try to run inference on my webcam, I get the following error from mediapipe and Tensorflow:
ValueError: Input 0 is incompatible with layer model: expected shape=(None, 99), found shape=(None, 3)
I am not sure how to fix this error as I could only train with shape of 99 as TF was giving me errors for using a shape of 3 when trying to compile. How do I fix this?
This is my inference code:
import cv2
import os
import tqdm
import numpy as np
import logging
from mediapipe.python.solutions import pose as mp_pose
from mediapipe.python.solutions import drawing_utils as mp_drawing
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import backend as K
from tensorflow.keras.utils import CustomObjectScope
def relu6(x):
return K.relu(x, max_value=6)
logging.getLogger().setLevel(logging.CRITICAL)
cap = cv2.VideoCapture(0)
model = tf.keras.models.load_model('weights_best.hdf5', compile = True,
custom_objects = {"relu6": relu6})
with mp_pose.Pose() as pose_tracker:
while cap.isOpened():
# Get next frame of the video.
ret, frame = cap.read()
# Run pose tracker.
imagefirst = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
image = cv2.flip(imagefirst,1)
result = pose_tracker.process(image)
pose_landmarks = result.pose_landmarks
# Draw pose prediction.
if pose_landmarks is not None:
mp_drawing.draw_landmarks(
image,
landmark_list=pose_landmarks,
connections=mp_pose.POSE_CONNECTIONS)
if pose_landmarks is not None:
# Get landmarks.
frame_height, frame_width = frame.shape[0], frame.shape[1]
pose_landmarks = np.array([[lmk.x * frame_width, lmk.y * frame_height, lmk.z * frame_width]
for lmk in pose_landmarks.landmark], dtype=np.float32)
assert pose_landmarks.shape == (33, 3), 'Unexpected landmarks shape: {}'.format(pose_landmarks.shape)
prediction = model.predict(pose_landmarks)
# Save the output frame.
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
cv2.imshow('Raw Webcam Feed', image)
if cv2.waitKey(10) & 0xFF == ord('q'):
break
# Close output video.
cap.release()
cv2.destroyAllWindows()
# Release MediaPipe resources.
pose_tracker.close()
CodePudding user response：
Maybe try changing the shape of pose_landmarks from (33, 3) to (1, 99) after your assertion and before you make a prediction:
import tensorflow as tf
pose_landmarks = tf.random.normal((33, 3))
assert pose_landmarks.shape == (33, 3), 'Unexpected landmarks shape: {}'.format(pose_landmarks.shape)
pose_landmarks = tf.expand_dims(pose_landmarks, axis=0)
shape = tf.shape(pose_landmarks)
pose_landmarks = tf.reshape(pose_landmarks, (shape[0], shape[1] * shape[2]))
tf.print(pose_landmarks.shape)
TensorShape([1, 99])