Everything else works except when I try to plot this graph.
That is because the type of data expected by the Waveform Graph is not what you give it.
You may want to use Waveform Chart. Have a look at Type of Graphs and Charts. A graph expects a data-set (or multiple data points) as an array or waveform, while a chart expects a data-point.
You must connect array of numbers to Waveform Graph.
Just add a shift register with empty array on initialization and build new array by adding a new value on any iteration.
Related
So I am using MediaPipe Holistic Solutions to extract keypoints from a body, hands and face, and I am using the data from this extraction for my calculations just fine. The problem is, I want to see if my data augmentation works, but I am unable to see it in real time. An example of how the keypoints are extracted:
lh_arr = (np.array([[result .x, result .y, result .z] for result in results.left_hand_landmarks.landmark]).flatten()
if I then do lets say, lh_arr [10:15]*2, I cant use this new data in the draw_landmarks function, as lh_arr is not class 'mediapipe.python.solution_base.SolutionOutputs'. Is there a way to get draw_landmarks() to use an np array instead or can I convert the np array back into the correct format? I have tried to get get the flattened array back into a dictionary of the same format of results, but it did not work. I can neither augment the results directly, as they are unsupported operand types.
I have four sets of data which have been created in dictionaries. After plotting them in one graph it is hard to clearly differentiate the point.
I put all of the data frames in one graph. My intention is to zoom in data points without changing x values that one can see the difference.
Please, help me in this regard. Thank you!
Graph Describes 4 sets of data
I'm trying to plot the data of my DataFarme in a groupedChart and I want the columns to preserve the order I gave them before. The data looks as follows (its not all there but its in the same way organized)
dataframe
When I plot it I get the following Graph:
graph
So the months were sorted even though I specified not to sort in the chart. I used the following code:
chart2 = alt.Chart(melted).mark_bar().encode(
column=alt.Column('variable',sort=None),
x=alt.X('room',sort=None),
y=alt.Y('value'),
color='room',
tooltip= ['room', 'value']
)
Does anyone know how I could fix that?
You've already used sort=None, which is the correct way to make scales in a non-faceted chart reflect the input order.
The missing piece is that faceted charts share scales by default (See Scale and Guide Resolution), so each facet is being forced to share an order.
If you make the x scale resolution independent, then each facet should retain the input order:
chart2 = alt.Chart(melted).mark_bar().encode(
column=alt.Column('variable',sort=None),
x=alt.X('room',sort=None),
y=alt.Y('value'),
color='room',
tooltip= ['room', 'value']
).resolve_scale(x='independent')
I am trying to use DeepLabV3 for image segmentation and object detection/classification on Coral.
I was able to sucessfully run the semantic_segmentation.py example using DeepLabV3 on the coral, but that only shows an image with an object segmented.
I see that it assigns labels to colors - how do i associate the labels.txt file that I made based off of the label info of the model to these colors? (how do i know which color corresponds to which label).
When I try to run the
engine = DetectionEngine(args.model)
using the deeplab model, I get the error
ValueError: Dectection model should have 4 output tensors!This model
has 1.
I guess this way is the wrong approach?
Thanks!
I believe you have reached out to us regarding the same query. I just wanted to paste the answer here for others to reference:
"The detection model usually have 4 output tensors to specifies the locations, classes, scores, and number and detections. You can read more about it here. In contrary, the segmentation model only have a single output tensor, so if you treat it the same way, you'll most likely segfault trying to access the wrong memory region. If you want to do all three tasks on the same image, my suggestion is to create 3 different engines and feed the image into each. The only problem with this is that each time you switch the model, there will likely be data transfer bottleneck for the model to get loaded onto the TPU. We have here an example on how you can run 2 models on a single TPU, you should be able to modify it to take 3 models."
On the last note, I just saw that you added:
how do i associate the labels.txt file that I made based off of the label info of the model to these colors
I just don't think this is something you can do for segmentation model but maybe I'm just confused on your query?
Take object detection model for example, there are 4 output tensors, the second tensor gives you an array of id associates with a certain class that you can map to a a label file. Segmentaion models only give the pixel surrounding an objects.
[EDIT]
Apology, looks like I'm the one confused on segmentation models.
Quote form my college :)
"You are interested to know the name of the label, you can find the corresponding integer to that label from result array in Semantic_segmentation.py. Where result is classification data of each pixel.
For example;
if you print result array in the with bird.jpg as input you would find few pixel's value as 3 which is corresponding 4th label in pascal_voc_segmentation_labels.txt (as indexing starts at 0 )."
I'm trying to determine what the data points are on a matplotlib axes. Is there an attribute I'm missing on the Axes object to get the x/y data values?
For example, say my code is passed a line plot, and I want to print out the x/y values that are plotted.
Your plot call will give you a lines.Line2D, which has the get_xdata(orig=True) and the get_ydata(orig=True) methods.
You can check axes.get_children() for Line2D instances.
Note that what you're doing sounds horrible from a software design point of view. You should rather implement something like a wrapper for plot that prints your raw data.
#JRichardSnape adds that iff your plot is only lines, you can use get_lines() rather than filtering the output of get_children().