from Logitech: the QuickCam Notebook and QuickCam Notebook Pro (how original. We will go through the code a little bit here, we are basically passing the image inside, then going through the posenet model by doing code_code_multiple_poses to get the coordinates, then overlaying the image back to go through cv2.imshow('posenet', overlay_image) while True: input_image, display_image, output_scale = posenet.read_cap( cap, scale_factor=args.scale_factor, output_stride=output_stride) heatmaps_result, offsets_result, displacement_fwd_result, displacement_bwd_result = n( model_outputs, feed_dict='.format(output_path)) if _name_ = '_main_': tf.app.run()Īfter that we can launch the python file via and get the me ssd_mobilenet_v3_large_coco ssage $ python3 generate_tfrecord.py -csv_input=images/train_labels. USB Project q uusbd: Iaky/ q Using the ActiveWire USB board with Linux q.
#LOGITECH QUICKCAM FOR NOTEBOOKS PRO BEAGLE BONE INSTALL#
The other reason being we can easily use posenet on Android devices for user consumption.įirst, we will install libraries needed pip3 install tensorflow-gpu scipy pyyaml pip3 install opencv-python=3.4.5.20įrom here you can clone my project git clone cd fitstream-jetson-nano python3 posenet_tensor_test.py
We will be using google's original posenet model at And this one will be based on tensorflow posenet. We will be building a brand new one in this project that's not from there. The QuickCam Pro for Notebooks has compatible software available for download and since this is a plug-and-play device, it does not come with drivers. There are currently a few AI based pose projects on Jetson, some of the featured on Jetson Community Project. Step 2: Setup tensorflow posenet on Jetson
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