issue with masks_to_marks mapping

dokooh commented 9 months ago

Search before asking

[X] I have searched the Multimodal Maestro issues and found no similar bug report.

Bug

Hi,

First and foremost thanks for your nice work so far.

I was testing your code with your google collab tutorial, and the mark creation (SAM), visualization and refining goes smoothly. Also the prompt call with marks to gpt4 goes well without any issue and I get response back.

In the part that you try to extract and visualize relevant marks, the resultset of masks_to_marks throws the error shown below.

With the example I used I expect a large output (20-30 marks), if this helps.

Environment

0.1.0rc1 Google collab (T4 vm)

Minimal Reproducible Example

masks = maestro.extract_relevant_masks(text=response, detections=refined_marks)
masks = np.array([mask for mask in masks.values()])
detections = maestro.masks_to_marks(masks)

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
[<ipython-input-61-a9e5dd9e84f7>](https://localhost:8080/#) in <cell line: 3>()
      1 masks = maestro.extract_relevant_masks(text=response, detections=marked_image)
      2 masks = np.array([mask for mask in masks.values()])
----> 3 detections = maestro.masks_to_marks(masks)

3 frames
[/usr/local/lib/python3.10/dist-packages/supervision/detection/core.py](https://localhost:8080/#) in _validate_mask(mask, n)
     27     )
     28     if not is_valid:
---> 29         raise ValueError("mask must be 3d np.ndarray with (n, H, W) shape")
     30 
     31 

ValueError: mask must be 3d np.ndarray with (n, H, W) shape

Additional

No response

Are you willing to submit a PR?

[ ] Yes I'd like to help by submitting a PR!

SkalskiP commented 9 months ago

Hi @dokooh! 👋🏻 So you used our official Colab with no code changes? You only uploaded your own image?

dokooh commented 9 months ago

Hi @dokooh! 👋🏻 So you used our official Colab with no code changes? You only uploaded your own image?

Correct.

SkalskiP commented 9 months ago

Okay, I just ran the code to confirm it works on our example. It works. Now, let's Try to debug yours.

Could you share your version of Google Colab? The one with your example image?
Alternatively, add print statement here:

masks = maestro.extract_relevant_masks(text=response, detections=refined_marks)
masks = np.array([mask for mask in masks.values()])
>>> print(masks)
detections = maestro.masks_to_marks(masks)

dokooh commented 9 months ago

Okay, I just ran the code to confirm it works on our example. It works. Now, let's Try to debug yours.

Could you share your version of Google Colab? The one with your example image?

Alternatively, add print statement here:
masks = maestro.extract_relevant_masks(text=response, detections=refined_marks)
masks = np.array([mask for mask in masks.values()])
>>> print(masks)
detections = maestro.masks_to_marks(masks)

Detections(xyxy=array([[448, 374, 504, 430],
       [339, 267, 364, 292],
       [195,   5, 224,  34],
       [424,  88, 453, 117],
       [496, 145, 521, 170],
       [249, 159, 273, 184],
       [ 31, 194,  60, 224],
       [392,  57, 421,  87],
       [245, 268, 274, 297],
       [539,  84, 564, 109],
       [506, 405, 591, 430],
       [507,  90, 531, 114],
       [453,  17, 499,  58],
       [379,   0, 404,  31],
       [  0, 141, 122, 319],
       [189,  84, 214, 110],
       [354, 173, 378, 199],
       [274, 117, 320, 157],
       [355,  69, 380, 114],
       [274, 212, 320, 252],
       [189, 179, 214, 221],
       [450, 111, 498, 130],
       [189, 179, 214, 204],
       [466,  75, 481,  90],
       [355,  69, 380,  94],
       [280,  45, 309,  74],
       [353, 173, 379, 209],
       [457,  56, 490,  67],
       [  0,   0, 194,  63],
       [530,   0, 555,  11],
       [287,  88, 302, 104],
       [274, 212, 320, 281],
       [416,  33, 426,  42],
       [581,  33, 591, 118],
       [  4, 196,  11, 223],
       [  0, 323,  50, 394],
       [584, 363, 591, 376],
       [ 42, 371,  50, 394],
       [  0, 322,  72, 394],
       [562,   0, 571,  24],
       [280,  45, 309, 116],
       [189,  84, 214, 138],
       [331, 229, 367, 240],
       [562,   0, 582,  24],
       [228, 109, 259, 117],
       [ 64, 103,  93, 118],
       [317, 275, 326, 293],
       [  0, 194,  60, 224],
       [409, 323, 502, 332],
       [238, 227, 249, 237],
       [244,  29, 368,  38],
       [287, 275, 302, 290],
       [516,  58, 564,  66],
       [500,   0, 559,  28],
       [332, 134, 368, 145],
       [278, 251, 311, 262],
       [558, 114, 567, 128],
       [  0,  91,  12, 115],
       [406, 134, 497, 142],
       [397, 221, 489, 229],
       [  0,   6,  91,  14],
       [229, 216, 255, 223],
       [452,   0, 500,   8],
       [504,  32, 540,  43],
       [340, 254, 433, 263],
       [320, 161, 329, 180],
       [517,  47, 561,  55],
       [  0, 139, 323, 319],
       [121,  70, 123, 119],
       [229, 121, 255, 128],
       [409, 308, 502, 319],
       [278, 156, 311, 167],
       [ 44, 348,  58, 357],
       [504,  33, 511,  42],
       [408, 307, 503, 332],
       [228, 206, 258, 213],
       [  0,  50,  66,  60],
       [340, 149, 433, 157],
       [259, 225, 268, 239],
       [  0,  90,  16, 135],
       [244, 388, 252, 403],
       [228, 109, 260, 129],
       [513,  33, 540,  43],
       [544, 173, 557, 182],
       [238, 132, 249, 142],
       [574,   0, 582,  24],
       [ 75, 343,  89, 357],
       [176, 245, 219, 253],
       [404, 254, 432, 262],
       [329, 105, 355, 113],
       [228, 205, 258, 223],
       [370,   0, 438,  42],
       [ 51, 124, 144, 134],
       [515,  46, 564,  66],
       [259, 130, 268, 145],
       [317, 267, 364, 293],
       [176, 245, 267, 253],
       [ 82, 323,  83, 329],
       [436,  47, 459,  85],
       [ 81, 323,  83, 329],
       [519,  59, 531,  66],
       [400, 101, 414, 110],
       [340, 254, 400, 262],
       [404,  13, 434,  30],
       [340, 149, 398, 157],
       [462,  94, 489, 108],
       [448, 374, 591, 430],
       [  0,   6,  61,  14],
       [193, 230, 198, 235],
       [244,  29, 319,  37],
       [192, 204, 197, 225],
       [244,  30, 254,  37],
       [239, 130, 268, 145],
       [192, 110, 197, 130],
       [450,  75, 498, 129]]), mask=array([[[False, False, False, ..., False, False, False],
        [False, False, False, ..., False, False, False],
        [False, False, False, ..., False, False, False],
        ...,
        [False, False, False, ..., False, False, False],
        [False, False, False, ..., False, False, False],
        [False, False, False, ..., False, False, False]],

       [[False, False, False, ..., False, False, False],
        [False, False, False, ..., False, False, False],
        [False, False, False, ..., False, False, False],
        ...,
        [False, False, False, ..., False, False, False],
        [False, False, False, ..., False, False, False],
        [False, False, False, ..., False, False, False]],

       [[False, False, False, ..., False, False, False],
        [False, False, False, ..., False, False, False],
        [False, False, False, ..., False, False, False],
        ...,
        [False, False, False, ..., False, False, False],
        [False, False, False, ..., False, False, False],
        [False, False, False, ..., False, False, False]],

       ...,

       [[False, False, False, ..., False, False, False],
        [False, False, False, ..., False, False, False],
        [False, False, False, ..., False, False, False],
        ...,
        [False, False, False, ..., False, False, False],
        [False, False, False, ..., False, False, False],
        [False, False, False, ..., False, False, False]],

       [[False, False, False, ..., False, False, False],
        [False, False, False, ..., False, False, False],
        [False, False, False, ..., False, False, False],
        ...,
        [False, False, False, ..., False, False, False],
        [False, False, False, ..., False, False, False],
        [False, False, False, ..., False, False, False]],

       [[False, False, False, ..., False, False, False],
        [False, False, False, ..., False, False, False],
        [False, False, False, ..., False, False, False],
        ...,
        [False, False, False, ..., False, False, False],
        [False, False, False, ..., False, False, False],
        [False, False, False, ..., False, False, False]]]), confidence=None, class_id=None, tracker_id=None)

SkalskiP commented 9 months ago

@dokooh, I can't open it.

dokooh commented 9 months ago

@dokooh, I can't open it.

added your email, let me know if you still can't open so I download the ipynb file and share it.

SkalskiP commented 9 months ago

I need your image as well. Could you upload it here?

dokooh commented 9 months ago

I need your image as well. Could you upload it here?

SkalskiP commented 9 months ago

@dokooh looks like after refinement, you lost all makes. I will add a fix for that edge case. As a workaround try skipping maestro.refine_marks step.

roboflow / multimodal-maestro