face_landmark | simple method for face alignment | Machine Learning library
kandi X-RAY | face_landmark Summary
kandi X-RAY | face_landmark Summary
This is the tensorflow2.0 branch, if u need to work on tf1 switch to branch tf1, it still work. It is simple and flexible, trained with wingloss , multi task learning, also with data augmentation based on headpose and face attributes(eyes state and mouth state). And i suggest that you could try with another project,including face detect and keypoints, and some optimizations were made, u can check it there [pappa_pig_face_engine]. Contact me if u have problem about it. 2120140200@mail.nankai.edu.cn :). this gif is from github.com/610265158/Peppa_Pig_Face_Engine, but it is the same model : ).
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Top functions reviewed by kandi - BETA
- Rotate image with given angle
- Convert boxes to a set of boxes
- Convert a set of points to a box
- Rotate a label
- Performs a training step
- Compute the loss
- Compute weight decay loss
- Call the block
- Shuffle a tensor
- Perform custom loop
- Calculate decay for given epoch
- Wrapper for inference
- Preprocess image
- Randomize image with bboxes
- Find boxes in an image
- Calculates the loss based on the prediction
- Compute thewing loss
- Call the model
- Parse an annotation file
- Recursively gets the list of files in a directory
- Performs a simple run
- Visualize TFLite
- Get a logger
- Call the core function
- Evaluate a single test step
- Apply the projection function
face_landmark Key Features
face_landmark Examples and Code Snippets
Community Discussions
Trending Discussions on face_landmark
QUESTION
need help this is my code:
...ANSWER
Answered 2021-Sep-08 at 15:12I just looked into the sourcecode at https://github.com/google/mediapipe/blob/master/mediapipe/python/solutions/holistic.py
FACE_CONNECTIONS seems to be renamed/replaced by FACEMESH_TESSELATION.
Just changing that name in the code should work.
PS: If you want just the outlines of the face, it's now FACEMESH_CONTOURS
QUESTION
import pandas as pd #pandas working with tabular data as dataframes
from sklearn.model_selection import train_test_split #scikit-learn, building custom ML models
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression, RidgeClassifier
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
#df = pd.read_csv('coords.csv')
#df = pd.read_csv('coords.csv', header=None)
#df = pd.read_csv('coords.csv', skiprows=[0])
df = pd.read_csv('coords.csv', skiprows=[0], header=None)
#df[df['class']=='Happy']
X = df.drop('class', axis=1) # features
y = df['class'] # target value
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1234)
pipelines = {
'lr':make_pipeline(StandardScaler(), LogisticRegression()),
'rc':make_pipeline(StandardScaler(), RidgeClassifier()),
'rf':make_pipeline(StandardScaler(), RandomForestClassifier()),
'gb':make_pipeline(StandardScaler(), GradientBoostingClassifier()),
}
fit_models = {}
for algo, pipeline in pipelines.items():
model = pipeline.fit(X_train, y_train)
fit_models[algo] = model
fit_models['rc'].predict(X_test)
ANSWER
Answered 2022-Jan-05 at 07:59Base on your notepad, you can't just read it use pandas because not clearly what is the header of the data, and I assume this data just have 1 row (Because I don't see data from row of class and your just make the np array to a list can cause it).
Example of correct csv:
You can see clearly the first column indicate the data separate properly and can assume this data have more than 1 row. So difference from your csv.
So you need to try pandas to save your data. For example your pose, face data have n - pair data, the easy way to add all of the data is we can use loop to add to dict:
QUESTION
I'm preparing a dataset right now and I need it to have a certain format, such as this:
Hand Pose No Seating Back Yes Seating Back No Seating Back Yes Seating Back No Seating Back Yes Seating BackHowever, currently it's producing
0 Hand ['No', 'No', 'No', 'No', 'No', 'No', 'No'] Pose ['Seating Back', 'Seating Back', 'Seating Back']As you can see the values inside the array stored in one cell while I need it to be un-nested in a where one value occupies one cell.
My code:
...ANSWER
Answered 2021-Nov-21 at 20:13You're creating a Series instead of not a DataFrame. You want to create a DataFrame.
Change
QUESTION
I have code which produces a CSV by printing as shown below
...ANSWER
Answered 2021-Nov-21 at 11:39In this line of code:
QUESTION
I'm working on a face tracking app (Android studio / Java) and I need to identify face landmarks. I'm interested using Mediapipe face mesh model. The problem is: I use Windows OS, and Mediapipe is not working on Windows OS.
I have very basic knowledge in Tensorflow, Can anybody explain to me how can i use Mediapipe's face_landmark.tflite model to detect faces in images and generate face mesh in Android studio with Java independently without the whole Mediapipe framework?
...ANSWER
Answered 2021-Apr-15 at 12:13You can try look into my notebook below for usage example in python. This only needs tflite model and does not require Mediapipe installation.
This is the output image,
This should give a starting point to use android tflite interpreter to get face landmarks and draw them. It will require a face detector such as blazeface to output the face bounding box first.
As I have not implemented this model in android yet I cannot say what else may be needed. Further details may be found in mediapipe face mesh codes. The notebook is based on this code,
MediaPipe TensorflowLite Iris Model
https://github.com/shortcipher3/stackoverflow/blob/master/mediapipe_iris_2d_landmarks.ipynb
Further references,
https://github.com/google/mediapipe/blob/master/mediapipe/modules/face_landmark/face_landmark.tflite
https://google.github.io/mediapipe/solutions/face_mesh
Model card with input, output details,
https://drive.google.com/file/d/1QvwWNfFoweGVjsXF3DXzcrCnz-mx-Lha/view
Alternate optionAndroid ML Kit also has face landmarks with very good documentation and code example.
https://developers.google.com/ml-kit/vision/face-detection
https://developers.google.com/android/reference/com/google/mlkit/vision/face/package-summary
QUESTION
I have implemented the code where i am capturing the image and saving that image, After that i have another code which adds jewelry to that captured image, But i am facing issue while adding jewelry to captured face error==> "face_landmarks = face_landmarks_list[0]
IndexError: list index out of range"
Can some one help me with the solutions.
ANSWER
Answered 2021-Jan-29 at 07:31Error says the answer
face_landmarks = face_landmarks_list[0]
You need to check whether a single face is detected or not.
Your second code, should start by checking the stored list length.
If the length is greater than 0, meaning some faces are detected, then continue.
QUESTION
I have here a script and i keep getting and indentation error but i cant figure out why
...ANSWER
Answered 2020-Dec-27 at 16:18Answer
The indentation issue is most likely caused by using both tabs and spaces for indentation. The PEP 8 formatting rules state to use spaces for indents. In certain IDEs you can set spaces to convert to tab, which can save you from the headaches of python syntax.
References and Additional Links
PEP 8: https://www.python.org/dev/peps/pep-0008/#indentation
Similar Stack Overflow Question: IndentationError: unindent does not match any outer indentation level
QUESTION
I have the following code:
...ANSWER
Answered 2020-Dec-10 at 23:44face variable may contain multiple values, therefore you need to use predictor for each value.
For instance:
QUESTION
I am working on a personal project to help me learn Python. I am working on drawing on an image using face_recognition library and PIL library. I am currently drawing "eyeliner" on a person's face by doing the following:
...ANSWER
Answered 2020-Oct-18 at 23:37Now I'll be honest I have no idea if this is more efficient, and it's definitely more complicated, but I gave it a shot:
QUESTION
I am working on this face recognition system.I have a folder with subfolders that has face images inside it. I am trying to loop through all the subfolders that consists of images and use my 'align_face' function that detects the face and crops and aligns all the images in subfolders. It then has to save all the aligned and cropped in another folder
I have tried this:
...ANSWER
Answered 2020-Sep-01 at 19:39The argument to align_face is the name of the file containing the image data, not the image data. So you don't need to open and read the data in your loop.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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Install face_landmark
You can use face_landmark like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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