mtcnn | mtcnn face detect caffe | Computer Vision library

I have this code in which mtcnn detects faces on an image, draws a red rectangle around each face and prints on the screen.

Code taken from: https://machinelearningmastery.com/how-to-perform-face-detection-with-classical-and-deep-learning-methods-in-python-with-keras/

But I want to save the image with the red boxes arround each face. So that i can do some preprocessing on it. Any help is good.

Is it possible to activate virtual environment created without using the anaconda prompt? For instance, I want to activate my virtual environment in c# in order to execute my python code.

This is my current code

I have created a simple Protobuf based config file and have been using it without any issues until now. The issue is that I added two new items to my settings (Config.proto) and now whatever value I set for the last variable is reflected in the previous one.

The following snapshot demonstrates this better. As you can see below, the value of fv_shape_predictor_path and fv_eyenet_path depend solely on order of being set. the one that is set last changes the others value.

I made sure the cpp files related to Config.proto are built afresh. I also tested this under Linux and there it works just fine. It seems its only happening in windows! it also doesn't affect any other items in the same settings. its just these two new ones.

I have no idea what is causing this or how to go about it. For reference this is how the protobuf looks like:

I am currently trying to train a dataset using OpenCV 4.2.2, I scoured the web but there are only examples for 2 params. OpenCV 4.2.2 loadDatasetList requires 4 parameters but there have been shortcomings which I did my best to overcome with the following. I tried with an array at first but loadDatasetList complained that the array was not iterable, I then proceeded to the code below with no luck. Any help is appreciated thank you for your time, and hope everyone is being safe and well.

The prior error passing in an array without iter()

PS E:\MTCNN> python kazemi-train.py No valid input file was given, please check the given filename. Traceback (most recent call last): File "kazemi-train.py", line 35, in status, images_train, landmarks_train = cv2.face.loadDatasetList(args.training_images,args.training_annotations, imageFiles, annotationFiles) TypeError: cannot unpack non-iterable bool object

The current error is:

PS E:\MTCNN> python kazemi-train.py Traceback (most recent call last): File "kazemi-train.py", line 35, in status, images_train, landmarks_train = cv2.face.loadDatasetList(args.training_images,args.training_annotations, iter(imageFiles), iter(annotationFiles)) SystemError: returned NULL without setting an error

I am trying to save the images after detecting faces in them. I tried using matplotlib.pyplot.savefig(img, bbox_inches='tight', pad_inches=0) and this stored images but all the images stored are blank white images. The output I get

what I want it to store is:- The Output I expect

I am trying to run inference on a jit traced model in C++ and currently the output I get in Python is different than the output I get in C++.

Initially I thought this be caused by the jit model itself, but now I don't think so, as I spotted some small deviations in the input tensor in the C++ code. I believe I did everything as instructed by the documentation so that might as well show an issue in torch::from_blob. I'm not sure!

Therefore in order to make sure which is the case, here are the snippets both in Python and C++ plus the sample input to test it.

Here is the sample image:

For Pytorch run the following snippet of code:

I have tensorflow 2.0 -gpu installed. I am doing face detection using MTCNN. On the first call to detect the face it takes 3.86 seconds. On the next call it takes only .049 seconds. I suspect it is not using the GPU on the first call but it does on the second call. I know MTCNN does import tensorflow but I do not understand why the GPU is not used on the first call. Code is below.

I've been trying to deploy my model to the AI platform for Prediction through the console on my vm instance, but I've gotten the error "(gcloud.beta.ai-platform.versions.create) Create Version failed. Bad model detected with error: "Failed to load model: Unexpected error when loading the model: problem in predictor - ModuleNotFoundError: No module named 'torchvision' (Error code: 0)"

I need to include both torch and torchvision. I followed the steps in this question Cannot deploy trained model to Google Cloud Ai-Platform with custom prediction routine: Model requires more memory than allowed, but I couldn't fetch the files pointed to by user gogasca. I tried downloading this .whl file from Pytorch website and uploading it to my cloud storage but got the same error that there is no module torchvision, even though this version is supposed to include both torch and torchvision. Also tried using Cloud AI compatible packages here, but they don't include torchvision.

I tried pointing to two separate .whl files for torch and torchvision in the --package-uris arguments, those point to files in my cloud storage, but then I got the error that the memory capacity was exceeded. This is strange, because collectively their size is around 130Mb. An example of my command that resulted in absence of torchvision looked like this:

i'm working on face recognition project using pytorch and mtcnn and after trained my training dataset , now i want to make prediction on test data set

this my trained code