lucasjinreal / alfred

alfred-py: A deep learning utility library for **human**, more detail about the usage of lib to: https://zhuanlan.zhihu.com/p/341446046

alfred-py: Born For Deeplearning

alfred-py can be called from terminal via alfred as a tool for deep-learning usage. It also provides massive utilities to boost your daily efficiency APIs, for instance, if you want draw a box with score and label, if you want logging in your python applications, if you want convert your model to TRT engine, just import alfred, you can get whatever you want. More usage you can read instructions below.

Functions Summary

Since many new users of alfred maybe not very familiar with it, conclude functions here briefly, more details see my updates:

• Visualization, draw boxes, masks, keypoints is very simple, even 3D boxes on point cloud supported;
• Command line tools, such as view your annotation data in any format (yolo, voc, coco any one);
• Deploy, you can using alfred deploy your tensorrt models;
• DL common utils, such as torch.device() etc;
• Renders, render your 3D models.

A pic visualized from alfred:

Install

To install alfred, it is very simple:

requirements:

lxml [optional]
pycocotools [optional]
opencv-python [optional]

then:

sudo pip3 install alfred-py

alfred is both a lib and a tool, you can import it's APIs, or you can directly call it inside your terminal.

A glance of alfred, after you installed above package, you will have alfred:

• data module:

  # show VOC annotations
  alfred data vocview -i JPEGImages/ -l Annotations/
  # show coco anntations
  alfred data cocoview -j annotations/instance_2017.json -i images/
  # show yolo annotations
  alfred data yoloview -i images -l labels
  # show detection label with txt format
  alfred data txtview -i images/ -l txts/
  # show more of data
  alfred data -h
  
  # eval tools
  alfred data evalvoc -h

• cab module:

  # count files number of a type
  alfred cab count -d ./images -t jpg
  # split a txt file into train and test
  alfred cab split -f all.txt -r 0.9,0.1 -n train,val

• vision module;

  # extract video to images
  alfred vision extract -v video.mp4
  # combine images to video
  alfred vision 2video -d images/

• -h to see more:

  usage: alfred [-h] [--version] {vision,text,scrap,cab,data} ...
  
  positional arguments:
    {vision,text,scrap,cab,data}
      vision              vision related commands.
      text                text related commands.
      scrap               scrap related commands.
      cab                 cabinet related commands.
      data                data related commands.
  
  optional arguments:
    -h, --help            show this help message and exit
    --version, -v         show version info.

  inside every child module, you can call it's -h as well: alfred text -h.

if you are on windows, you can install pycocotools via: pip install "git+https://github.com/philferriere/cocoapi.git#egg=pycocotools&subdirectory=PythonAPI", we have made pycocotools as an dependencies since we need pycoco API.

Updates

alfred-py　has been updating for 3 years, and it will keep going!

• 2050-xxx: to be continue;

• 2023.04.28: Update the 3d keypoints visualizer, now you can visualize Human3DM kpts in realtime: For detailes reference to examples/demo_o3d_server.py. The result is generated from MotionBert.

• 2022.01.18: Now alfred support a Mesh3D visualizer server based on Open3D:

  from alfred.vis.mesh3d.o3dsocket import VisOpen3DSocket
  
  def main():
      server = VisOpen3DSocket()
      while True:
          server.update()
  
  
  if __name__ == "__main__":
      main()

  Then, you just need setup a client, send keypoints3d to server, and it will automatically visualized out. Here is what it looks like:

• 2021.12.22: Now alfred supported keypoints visualization, almost all datasets supported in mmpose were also supported by alfred:

  from alfred.vis.image.pose import vis_pose_result
  
  # preds are poses, which is (Bs, 17, 3) for coco body
  vis_pose_result(ori_image, preds, radius=5, thickness=2, show=True)

• 2021.12.05: You can using alfred.deploy.tensorrt for tensorrt inference now:

  from alfred.deploy.tensorrt.common import do_inference_v2, allocate_buffers_v2, build_engine_onnx_v3
  
  def engine_infer(engine, context, inputs, outputs, bindings, stream, test_image):
  
    # image_input, img_raw, _ = preprocess_np(test_image)
    image_input, img_raw, _ = preprocess_img((test_image))
    print('input shape: ', image_input.shape)
    inputs[0].host = image_input.astype(np.float32).ravel()
  
    start = time.time()
    dets, labels, masks = do_inference_v2(context, bindings=bindings, inputs=inputs,
                                          outputs=outputs, stream=stream, input_tensor=image_input)
  img_f = 'demo/demo.jpg'
  with build_engine_onnx_v3(onnx_file_path=onnx_f) as engine:
      inputs, outputs, bindings, stream = allocate_buffers_v2(engine)
      # Contexts are used to perform inference.
      with engine.create_execution_context() as context:
          print(engine.get_binding_shape(0))
          print(engine.get_binding_shape(1))
          print(engine.get_binding_shape(2))
          INPUT_SHAPE = engine.get_binding_shape(0)[-2:]
  
          print(context.get_binding_shape(0))
          print(context.get_binding_shape(1))
          dets, labels, masks, img_raw = engine_infer(
              engine, context, inputs, outputs, bindings, stream, img_f)

• 2021.11.13: Now I add Siren SDK support!

  from functools import wraps
  from alfred.siren.handler import SirenClient
  from alfred.siren.models import ChatMessage, InvitationMessage
  
  siren = SirenClient('daybreak_account', 'password')
  
  
  @siren.on_received_invitation
  def on_received_invitation(msg: InvitationMessage):
      print('received invitation: ', msg.invitation)
      # directly agree this invitation for robots
  
  
  @siren.on_received_chat_message
  def on_received_chat_msg(msg: ChatMessage):
      print('got new msg: ', msg.text)
      siren.publish_txt_msg('I got your message O(∩_∩)O哈哈~', msg.roomId)
  
  
  if __name__ == '__main__':
      siren.loop()
  

  Using this, you can easily setup a Chatbot. By using Siren client.

• 2021.06.24: Add a useful commandline tool, change your pypi source easily!!:

  alfred cab changesource
  

  And then your pypi will using aliyun by default!

• 2021.05.07: Upgrade Open3D instructions: Open3D>0.9.0 no longer compatible with previous alfred-py. Please upgrade Open3D, you can build Open3D from source:

    git clone --recursive https://github.com/intel-isl/Open3D.git
    cd Open3D && mkdir build && cd build
    sudo apt install libc++abi-8-dev
    sudo apt install libc++-8-dev
    cmake .. -DPYTHON_EXECUTABLE=/usr/bin/python3
  

  Ubuntu 16.04 blow I tried all faild to build from source. So, please using open3d==0.9.0 for alfred-py.

• 2021.04.01: A unified evaluator had added. As all we know, for many users, writting Evaluation might coupled deeply with your project. But with Alfred's help, you can do evaluation in any project by simply writting 8 lines of codes, for example, if your dataset format is Yolo, then do this:

    def infer_func(img_f):
    image = cv2.imread(img_f)
    results = config_dict['model'].predict_for_single_image(
        image, aug_pipeline=simple_widerface_val_pipeline, classification_threshold=0.89, nms_threshold=0.6, class_agnostic=True)
    if len(results) > 0:
        results = np.array(results)[:, [2, 3, 4, 5, 0, 1]]
        # xywh to xyxy
        results[:, 2] += results[:, 0]
        results[:, 3] += results[:, 1]
    return results
  
    if __name__ == '__main__':
        conf_thr = 0.4
        iou_thr = 0.5
  
        imgs_root = 'data/hand/images'
        labels_root = 'data/hand/labels'
  
        yolo_parser = YoloEvaluator(imgs_root=imgs_root, labels_root=labels_root, infer_func=infer_func)
        yolo_parser.eval_precisely()

  Then you can get your evaluation results automatically. All recall, precision, mAP will printed out. More dataset format are on-going.

• 2021.03.10: New added ImageSourceIter class, when you want write a demo of your project which need to handle any input such as image file / folder / video file etc. You can using ImageSourceIter:

  from alfred.utils.file_io import ImageSourceIter
  
  # data_f can be image_file or image_folder or video
  iter = ImageSourceIter(ops.test_path)
  while True:
      itm = next(iter)
      if isinstance(itm, str):
          itm = cv2.imread(itm)
      # cv2.imshow('raw', itm)
      res = detect_for_pose(itm, det_model)
      cv2.imshow('res', itm)
      if iter.video_mode:
          cv2.waitKey(1)
      else:
          cv2.waitKey(0)

  And then you can avoid write anything else of deal with file glob or reading video in cv. note that itm return can be a cv array or a file path.

• 2021.01.25: alfred now support self-defined visualization on coco format annotation (not using pycoco tools):

  

  If your dataset in coco format but visualize wrongly pls fire a issue to me, thank u!

• 2020.09.27: Now, yolo and VOC can convert to each other, so that using Alfred you can:

  • convert yolo2voc;
  • convert voc2yolo;
  • convert voc2coco;
  • convert coco2voc;

  By this, you can convert any labeling format of each other.

• 2020.09.08: After a long time past, alfred got some updates: We providing coco2yolo ability inside it. Users can run this command to convert your data to yolo format:

  alfred data coco2yolo -i images/ -j annotations/val_split_2020.json
  

  Only should provided is your image root path and your json file. And then all result will generated into yolo folder under images or in images parent dir.

  After that (you got your yolo folder), then you can visualize the conversion result to see if it correct or not:

  alfred data yolovview -i images/ -l labels/
  

  

• 2020.07.27: After a long time past, alfred finally get some updates:

  

  Now, you can using alfred draw Chinese charactors on image without xxxx undefined encodes.

  from alfred.utils.cv_wrapper import put_cn_txt_on_img
  
  img = put_cn_txt_on_img(img, spt[-1], [points[0][0], points[0][1]-25], 1.0, (255, 255, 255))

  Also, you now can merge 2 VOC datasets! This is helpful when you have 2 dataset and you want merge them into a single one.

  alfred data mergevoc -h
  

  You can see more promotes.

• 2020.03.08：Several new files added in alfred:

  alfred.utils.file_io: Provide file io utils for common purpose
  alfred.dl.torch.env: Provide seed or env setup in pytorch (same API as detectron2)
  alfred.dl.torch.distribute: utils used for distribute training when using pytorch
  

• 2020.03.04: We have added some evaluation tool to calculate mAP for object detection model performance evaluation, it's useful and can visualize result:

  this usage is also quite simple:

  alfred data evalvoc -g ground-truth -d detection-results -im images
  

  where -g is your ground truth dir (contains xmls or txts), -d is your detection result files dir, -im is your images fodler. You only need save all your detected results into txts, one image one txt, and format like this:

  bottle 0.14981 80 1 295 500  
  bus 0.12601 36 13 404 316  
  horse 0.12526 430 117 500 307  
  pottedplant 0.14585 212 78 292 118  
  tvmonitor 0.070565 388 89 500 196 

• 2020.02.27: We just update a license module inside alfred, say you want apply license to your project or update license, simple:

   alfred cab license -o 'MANA' -n 'YoloV3' -u 'manaai.cn'

  you can found more detail usage with alfred cab license -h

• 2020-02-11: open3d has changed their API. we have updated new open3d inside alfred, you can simply using latest open3d and run python3 examples/draw_3d_pointcloud.py you will see this:

  

• 2020-02-10: alfred now support windows (experimental);

• 2020-02-01: 武汉加油! alfred fix windows pip install problem related to encoding 'gbk';

• 2020-01-14: Added cabinet module, also add some utils under data module;

• 2019-07-18: 1000 classes imagenet labelmap added. Call it from:

  from alfred.vis.image.get_dataset_label_map import imagenet_labelmap
  
  # also, coco, voc, cityscapes labelmap were all added in
  from alfred.vis.image.get_dataset_label_map import coco_labelmap
  from alfred.vis.image.get_dataset_label_map import voc_labelmap
  from alfred.vis.image.get_dataset_label_map import cityscapes_labelmap

• 2019-07-13: We add a VOC check module in command line usage, you can now visualize your VOC format detection data like this:

  alfred data voc_view -i ./images -l labels/
  

• 2019-05-17: We adding open3d as a lib to visual 3d point cloud in python. Now you can do some simple preparation and visual 3d box right on lidar points and show like opencv!!

  

  You can achieve this by only using alfred-py and open3d!

  example code can be seen under examples/draw_3d_pointcloud.py. code updated with latest open3d API!.

• 2019-05-10: A minor updates but really useful which we called mute_tf, do you want to disable tensorflow ignoring log? simply do this!!

  from alfred.dl.tf.common import mute_tf
  mute_tf()
  import tensorflow as tf

  Then, the logging message were gone....

• 2019-05-07: Adding some protos, now you can parsing tensorflow coco labelmap by using alfred:

  from alfred.protos.labelmap_pb2 import LabelMap
  from google.protobuf import text_format
  
  with open('coco.prototxt', 'r') as f:
      lm = LabelMap()
      lm = text_format.Merge(str(f.read()), lm)
      names_list = [i.display_name for i in lm.item]
      print(names_list)

• 2019-04-25: Adding KITTI fusion, now you can get projection from 3D label to image like this: we will also add more fusion utils such as for nuScene dataset.

  We providing kitti fusion kitti for convert camera link 3d points to image pixel, and convert lidar link 3d points to image pixel. Roughly going through of APIs like this:

  # convert lidar prediction to image pixel
  from alfred.fusion.kitti_fusion import LidarCamCalibData, \
      load_pc_from_file, lidar_pts_to_cam0_frame, lidar_pt_to_cam0_frame
  from alfred.fusion.common import draw_3d_box, compute_3d_box_lidar_coords
  
  # consit of prediction of lidar
  # which is x,y,z,h,w,l,rotation_y
  res = [[4.481686, 5.147319, -1.0229858, 1.5728549, 3.646751, 1.5121397, 1.5486346],
         [-2.5172017, 5.0262384, -1.0679419, 1.6241353, 4.0445814, 1.4938312, 1.620804],
         [1.1783253, -2.9209857, -0.9852259, 1.5852798, 3.7360613, 1.4671413, 1.5811548]]
  
  for p in res:
      xyz = np.array([p[: 3]])
      c2d = lidar_pt_to_cam0_frame(xyz, frame_calib)
      if c2d is not None:
          cv2.circle(img, (int(c2d[0]), int(c2d[1])), 3, (0, 255, 255), -1)
      hwl = np.array([p[3: 6]])
      r_y = [p[6]]
      pts3d = compute_3d_box_lidar_coords(xyz, hwl, angles=r_y, origin=(0.5, 0.5, 0.5), axis=2)
  
      pts2d = []
      for pt in pts3d[0]:
          coords = lidar_pt_to_cam0_frame(pt, frame_calib)
          if coords is not None:
              pts2d.append(coords[:2])
      pts2d = np.array(pts2d)
      draw_3d_box(pts2d, img)

  And you can see something like this:

  note:

  compute_3d_box_lidar_coords for lidar prediction, compute_3d_box_cam_coords for KITTI label, cause KITTI label is based on camera coordinates!.

  

  since many users ask me how to reproduces this result, you can checkout demo file under examples/draw_3d_box.py;

• 2019-01-25: We just adding network visualization tool for pytorch now!! How does it look? Simply print out every layer network with output shape, I believe this is really helpful for people to visualize their models!

  ➜  mask_yolo3 git:(master) ✗ python3 tests.py
  ----------------------------------------------------------------
          Layer (type)               Output Shape         Param #
  ================================================================
              Conv2d-1         [-1, 64, 224, 224]           1,792
                ReLU-2         [-1, 64, 224, 224]               0
                .........
             Linear-35                 [-1, 4096]      16,781,312
               ReLU-36                 [-1, 4096]               0
            Dropout-37                 [-1, 4096]               0
             Linear-38                 [-1, 1000]       4,097,000
  ================================================================
  Total params: 138,357,544
  Trainable params: 138,357,544
  Non-trainable params: 0
  ----------------------------------------------------------------
  Input size (MB): 0.19
  Forward/backward pass size (MB): 218.59
  Params size (MB): 527.79
  Estimated Total Size (MB): 746.57
  ----------------------------------------------------------------
  
  

  Ok, that is all. what you simply need to do is:

  from alfred.dl.torch.model_summary import summary
  from alfred.dl.torch.common import device
  
  from torchvision.models import vgg16
  
  vgg = vgg16(pretrained=True)
  vgg.to(device)
  summary(vgg, input_size=[224, 224])

  Support you input (224, 224) image, you will got this output, or you can change any other size to see how output changes. (currently not support for 1 channel image)

• 2018-12-7: Now, we adding a extensible class for quickly write an image detection or segmentation demo.

  If you want write a demo which do inference on an image or an video or right from webcam, now you can do this in standared alfred way:

  class ENetDemo(ImageInferEngine):
  
      def __init__(self, f, model_path):
          super(ENetDemo, self).__init__(f=f)
  
          self.target_size = (512, 1024)
          self.model_path = model_path
          self.num_classes = 20
  
          self.image_transform = transforms.Compose(
              [transforms.Resize(self.target_size),
               transforms.ToTensor()])
  
          self._init_model()
  
      def _init_model(self):
          self.model = ENet(self.num_classes).to(device)
          checkpoint = torch.load(self.model_path)
          self.model.load_state_dict(checkpoint['state_dict'])
          print('Model loaded!')
  
      def solve_a_image(self, img):
          images = Variable(self.image_transform(Image.fromarray(img)).to(device).unsqueeze(0))
          predictions = self.model(images)
          _, predictions = torch.max(predictions.data, 1)
          prediction = predictions.cpu().numpy()[0] - 1
          return prediction
  
      def vis_result(self, img, net_out):
          mask_color = np.asarray(label_to_color_image(net_out, 'cityscapes'), dtype=np.uint8)
          frame = cv2.resize(img, (self.target_size[1], self.target_size[0]))
          # mask_color = cv2.resize(mask_color, (frame.shape[1], frame.shape[0]))
          res = cv2.addWeighted(frame, 0.5, mask_color, 0.7, 1)
          return res
  
  
  if __name__ == '__main__':
      v_f = ''
      enet_seg = ENetDemo(f=v_f, model_path='save/ENet_cityscapes_mine.pth')
      enet_seg.run()

  After that, you can directly inference from video. This usage can be found at git repo:

  

The repo using alfred: http://github.com/jinfagang/pt_enet

• 2018-11-6: I am so glad to announce that alfred 2.0 released！😄⛽️👏👏 Let's have a quick look what have been updated:

  # 2 new modules, fusion and vis
  from alred.fusion import fusion_utils
  

  For the module fusion contains many useful sensor fusion helper functions you may use, such as project lidar point cloud onto image.

• 2018-08-01: Fix the video combined function not work well with sequence. Add a order algorithm to ensure video sequence right. also add some draw bbox functions into package.

  can be called like this:

• 2018-03-16: Slightly update alfred, now we can using this tool to combine a video sequence back original video! Simply do:

  # alfred binary exectuable program
  alfred vision 2video -d ./video_images

Capable

alfred is both a library and a command line tool. It can do those things:

# extract images from video
alfred vision extract -v video.mp4
# combine image sequences into a video
alfred vision 2video -d /path/to/images
# get faces from images
alfred vision getface -d /path/contains/images/

Just try it out!!

Copyright

Alfred build by Lucas Jin with ❤️， welcome star and send PR. If you got any question, you can ask me via wechat: jintianiloveu, this code released under GPL-3 license.

alfred's Issues

failed to run demo_o3d_server.py

After run pip install alfred-py, run script demo_o3d_server.py in examples dir get error:

❯❯❯ py .\demo_o3d_server.py
Traceback (most recent call last):
  File ".\demo_o3d_server.py", line 18, in <module>
    main()
  File ".\demo_o3d_server.py", line 8, in main
    cfg = get_default_visconfig()
  File "C:\Users\xq\miniconda3\envs\nn\lib\site-packages\alfred\vis\mesh3d\o3d_visconfig.py", line 87, in get_default_visconfig
    cfg = Config.load(
  File "C:\Users\xq\miniconda3\envs\nn\lib\site-packages\alfred\utils\base_config.py", line 21, in load
    cfg.merge_from_file(filename)
  File "C:\Users\xq\miniconda3\envs\nn\lib\site-packages\yacs\config.py", line 211, in merge_from_file
    with open(cfg_filename, "r") as f:
FileNotFoundError: [Errno 2] No such file or directory: 'C:\\Users\\xq\\miniconda3\\envs\\nn\\lib\\site-packages\\alfred\\vis\\mesh3d\\default_viscfg.yml'

Then found there is no assets and default_viscfg.yml under site-packages:

❯❯❯ ls -l
total 88
drwxrwxr-x    2 xq       xq            4096 Feb 27 15:32 __pycache__
-rw-rw-r--    1 xq       xq           35379 Feb 27 15:32 o3d_visconfig.py
-rw-rw-r--    1 xq       xq            8368 Feb 27 15:32 o3dsocket.py
-rw-rw-r--    1 xq       xq           15428 Feb 27 15:32 o3dwrapper.py
-rw-rw-r--    1 xq       xq            5552 Feb 27 15:32 skelmodel.py
-rw-rw-r--    1 xq       xq            9290 Feb 27 15:32 utils.py

So should I copy those files to site-packages as function get_default_visconfig do not allow arguments. Or other good suggestions?

Thanks!

AttributeError: 'NoneType' object has no attribute 'background_color'

Traceback (most recent call last):
File "core/pointpillars_detector.py", line 156, in
detector.predict_on_nucenes_local_file(sys.argv[1])
File "core/pointpillars_detector.py", line 146, in predict_on_nucenes_local_file
draw_pcs_open3d(geometries)
File "/usr/local/lib/python3.8/dist-packages/alfred/vis/pointcloud/pointcloud_vis.py", line 76, in draw_pcs_open3d
opt.background_color = np.asarray([0, 0, 0])
AttributeError: 'NoneType' object has no attribute 'background_color'

when following: https://pythonawesome.com/a-deep-learning-utility-library-for-visualization-and-sensor-fusion-purpose/

ModuleNotFoundError: No module named 'alfred.dl'

python
Python 3.8.13 | packaged by conda-forge | (default, Mar 25 2022, 06:04:18)
[GCC 10.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.

import alfred.dl.torch.distribute.utils as comm

Traceback (most recent call last):
File "", line 1, in
ModuleNotFoundError: No module named 'alfred.dl'

using pip install aflred-py doesnot work

inconsistent version: expected '2.2022.10.17.1', but metadata has '2.2022.10.25.1'

I got this error from pip3:

alfred-py-2.2022.10.17.1.tar.gz has inconsistent version: expected '2.2022.10.17.1', but metadata has '2.2022.10.25.1'

Full log:

$ pip3 install alfred-py
Defaulting to user installation because normal site-packages is not writeable
Collecting alfred-py
  Using cached alfred-py-2.2022.10.17.1.tar.gz (1.8 MB)
  Preparing metadata (setup.py) ... done
Discarding https://files.pythonhosted.org/packages/9f/2d/832c6183c2fdd7815bea1057048bda188656adbc4ddc223a342321be94e5/alfred-py-2.2022.10.17.1.tar.gz (from https://pypi.org/simple/alfred-py/): Requested alfred-py from https://files.pythonhosted.org/packages/9f/2d/832c6183c2fdd7815bea1057048bda188656adbc4ddc223a342321be94e5/alfred-py-2.2022.10.17.1.tar.gz has inconsistent version: expected '2.2022.10.17.1', but metadata has '2.2022.10.25.1'
  Using cached alfred-py-2.12.6.tar.gz (232 kB)
  Preparing metadata (setup.py) ... error
  error: subprocess-exited-with-error
  
  × python setup.py egg_info did not run successfully.
  │ exit code: 1
  ╰─> [11 lines of output]
      running egg_info
      creating /tmp/pip-pip-egg-info-r8x814pr/alfred_py.egg-info
      writing /tmp/pip-pip-egg-info-r8x814pr/alfred_py.egg-info/PKG-INFO
      writing dependency_links to /tmp/pip-pip-egg-info-r8x814pr/alfred_py.egg-info/dependency_links.txt
      writing entry points to /tmp/pip-pip-egg-info-r8x814pr/alfred_py.egg-info/entry_points.txt
      writing requirements to /tmp/pip-pip-egg-info-r8x814pr/alfred_py.egg-info/requires.txt
      writing top-level names to /tmp/pip-pip-egg-info-r8x814pr/alfred_py.egg-info/top_level.txt
      writing manifest file '/tmp/pip-pip-egg-info-r8x814pr/alfred_py.egg-info/SOURCES.txt'
      /usr/lib/python3.8/distutils/dist.py:274: UserWarning: Unknown distribution option: 'find_packages'
        warnings.warn(msg)
      error: package directory 'alfred/fonts' does not exist
      [end of output]
  
  note: This error originates from a subprocess, and is likely not a problem with pip.
error: metadata-generation-failed

× Encountered error while generating package metadata.
╰─> See above for output.

note: This is an issue with the package mentioned above, not pip.
hint: See above for details.

any way to avoid installing opencv-contrib-python ?

I need to install this module for object detection task running on Jetson Nano
However it failed to install opencv-contrib-python, maybe it's about Arm architecture problem， I guess, can I avoid install opencv-contrib-python ？

Collecting alfred-py
  Using cached https://files.pythonhosted.org/packages/14/06/22789e703af1c65d762fb5cd4be849a95df959be3c717fa2d4fbac6c0665/alfred-py-2.5.21.tar.gz
Collecting colorama
  Using cached https://files.pythonhosted.org/packages/4f/a6/728666f39bfff1719fc94c481890b2106837da9318031f71a8424b662e12/colorama-0.4.1-py2.py3-none-any.whl
Collecting deprecated
  Using cached https://files.pythonhosted.org/packages/f6/89/62912e01f3cede11edcc0abf81298e3439d9c06c8dce644369380ed13f6d/Deprecated-1.2.7-py2.py3-none-any.whl
Collecting future
  Using cached https://files.pythonhosted.org/packages/45/0b/38b06fd9b92dc2b68d58b75f900e97884c45bedd2ff83203d933cf5851c9/future-0.18.2.tar.gz
Collecting loguru
  Using cached https://files.pythonhosted.org/packages/57/dd/be19f64691d250bbd98906254307abd626dbbd674b019a313f57d6338bc7/loguru-0.4.0-py3-none-any.whl
Requirement already satisfied: lxml in /usr/lib/python3/dist-packages (from alfred-py) (4.2.1)
Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from alfred-py) (1.17.4)
ERROR: Could not find a version that satisfies the requirement opencv-contrib-python (from alfred-py) (from versions: none)
ERROR: No matching distribution found for opencv-contrib-python (from alfred-py)

How do I visualize bounding boxes on my own point cloud?

How do I visualize bounding boxes on my own point cloud? I mean, using draw_3d_pointcloud.py , what information does it need?

For bounding box , xyz, hwl, r_y are needed, right?

glx error in pointcloud visualization.

For visualizing pointcloud, I use this repo.

In draw_pcs_open3d function (after passing the geometries to it),

I get the following error while it tries to create a visualization window:

~/anaconda3/envs/pointpillars/lib/python3.7/site-packages/alfred/vis/pointcloud/pointcloud_vis.py in draw_pcs_open3d(geometries)
     46         return False
     47     vis = visualization.Visualizer()
---> 48     vis.create_window()
     49     for g in geometries:
     50         vis.add_geometry(g)

RuntimeError: [Open3D ERROR] GLFW Error: GLX: Failed to create context: GLXBadFBConfig

Any idea how to fix this error?

shape_predictor_68_face_landmarks.dat not fund.

Alfred - Valet of Artificial Intelligence.
Author: Lucas Jin
At : 20202.10.01, since 2019.11.11
Loc : Shenzhen, China
Star : http://github.com/jinfagang/alfred
Ver. : 2.7.1

=> Module: vision
=> Action: getface
Extract faces from ../data
Traceback (most recent call last):
File "/Users/MRJ/anaconda3/envs/python37/lib/python3.7/site-packages/alfred/alfred.py", line 256, in main
face_extractor = FaceExtractor()
File "/Users/MRJ/anaconda3/envs/python37/lib/python3.7/site-packages/alfred/modules/vision/face_extractor.py", line 46, in init
self.predictor = dlib.shape_predictor(predictor_path)
RuntimeError: Unable to open shape_predictor_68_face_landmarks.dat
parse args error, type -h to see help. msg: Unable to open shape_predictor_68_face_landmarks.dat

Roadmap

alfred is a system wide tools cabinet.

Now, you can install via pip install alfred-py

and you can get a as a command.

You can simple push your git repo by:

a ps

Every simple.

The tools added to alfred update as follow:

1. a pl: or a pl origin master pull the git;
2. a ps: push your project, and automatically commit;

How to exit alfred data viewer?

There is not way to kill the process before looping through all the images under the folder.
Now, I need to use Ctrl + Z to put alfred to background and then kill %1 to kill the process.
Is there any more efficient way to do that?

验证我自己的coco数据集的时候，大部分mask有问题

不知道是工具的问题还是我自己数据的问题；不只一张图有这样的问题；我是从labelme转过来的.

Pip installation fails despite having colorama in dependencies

Got this error from a docker build of another system that depends on Alfred:
Ubuntu 20.04, Python 3.8.4, pip 22.0.2.

Collecting alfred-py
  Downloading alfred-py-2.10.0.tar.gz (1.8 MB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.8/1.8 MB 51.6 MB/s eta 0:00:00
  Preparing metadata (setup.py): started
  Preparing metadata (setup.py): finished with status 'error'
  error: subprocess-exited-with-error
  
  × python setup.py egg_info did not run successfully.
  │ exit code: 1
  ╰─> [8 lines of output]
      Traceback (most recent call last):
        File "<string>", line 2, in <module>
        File "<pip-setuptools-caller>", line 34, in <module>
        File "/tmp/pip-install-dc1ysaug/alfred-py_3fa144628b9e4c4795003ef8f4b41a70/setup.py", line 32, in <module>
          from alfred.alfred import __VERSION__
        File "/tmp/pip-install-dc1ysaug/alfred-py_3fa144628b9e4c4795003ef8f4b41a70/alfred/alfred.py", line 31, in <module>
          from colorama import Fore, Back, Style
      ModuleNotFoundError: No module named 'colorama'
      [end of output]
  
  note: This error originates from a subprocess, and is likely not a problem with pip.
error: metadata-generation-failed
× Encountered error while generating package metadata.
╰─> See above for output.
note: This is an issue with the package mentioned above, not pip.
hint: See above for details.

no image found

when i use 'alfred vocview -i JPEGImages/ -l Annotations/' in my pic catalog，it appears
**Alfred - Valet of Artificial Intelligence.
Author: Lucas Jin
At : 20202.10.01, since 2019.11.11
Loc : Shenzhen, China
Star : http://github.com/jinfagang/alfred
Ver. : 2.7.1

=> Module: data
=> Action: vocview
INFO 08.19 01:53:30 view_voc.py:58: img root: JPEGImages/, label root: Annotations/
INFO 08.19 01:53:30 view_voc.py:61: label major will using xmls to found images... it might cause no image found
: cannot connect to X server
**

How to install alfred from source code?

My system can't use pip install because proxy problem. The setup.py seems to build environment, but there is no introduction on how to uee setup.py.

bug

 alfred data voc_view -i JPEGImages -l Annotations
Alfred - Valet of Artificial Intelligence.
Author: Lucas Jin
At    : 2018.11.11
Loc   : Shenzhen, China
Star  : http://github.com/jinfagang/alfred
Ver.  : 2.5.15

=> Module: scrap
=> Action: image
 parse args error, type -h to see help. msg: 'query'

No module named 'alfred.dl.torch.nn.weights_init'

from alfred.dl.torch.nn.weights_init import constant_init, kaiming_init
from alfred.dl.torch.runner.checkpoint import load_checkpoint
error

ImportError: cannot import name 'device'

I got this issue when run alfred

win10下alfred保错

No.1
File "........\pip-install-pgy54cbe\alfred-py\setup.py", line 29, in
long_description = f.read()
UnicodeDecodeError: 'gbk' codec can't decode byte 0x9c in position 5247: illegal multibyte sequence
----------------------------------------
ERROR: Command errored out with exit status 1: python setup.py egg_info Check the logs for full command output.
No.2
安装alfred时会安装所需的包pycocotools在windows下安装会提示error: Failed building wheel for pycocotools

ModuleNotFoundError: No module named 'pascal_voc_writer'

python3.7/site-packages/alfred/modules/data/mergevoc.py", line 39, in <module> from pascal_voc_writer import Writer ModuleNotFoundError: No module named 'pascal_voc_writer'
An unused python module (may has been deleted) pascal_voc_writer cause the error.
Just delete this line and reinstall the alfred code.
alfred/modules/data/mergevoc.py", line 39, in <module> from pascal_voc_writer import Writer

Then it works!

ModuleNotFoundError: No module named 'alfred.vis'

ModuleNotFoundError: No module named 'alfred.vis'

when running draw_3d_pointcloud.py

ModuleNotFoundError: No module named 'alfred'

My dev enviroment is pycharm and anaconda.
Below error was shown when install alfred by command "conda install alfred-py", how can I install alfred in my environment? thanks a lot.

_Fetching package metadata .................

PackageNotFoundError: Packages missing in current channels:

• alfred-py

We have searched for the packages in the following channels:

• https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/win-64
• https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/noarch
• https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/win-64
• https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/noarch
• https://repo.continuum.io/pkgs/main/win-64
• https://repo.continuum.io/pkgs/main/noarch
• https://repo.continuum.io/pkgs/free/win-64
• https://repo.continuum.io/pkgs/free/noarch
• https://repo.continuum.io/pkgs/r/win-64
• https://repo.continuum.io/pkgs/r/noarch
• https://repo.continuum.io/pkgs/pro/win-64
• https://repo.continuum.io/pkgs/pro/noarch
• https://repo.continuum.io/pkgs/msys2/win-64
• https://repo.continuum.io/pkgs/msys2/noarch_

如何通过3d点云数据识别人体？

no any documents?

>>> from alfred.dl.torch.common import device Traceback (most recent call last): File "<stdin>", line 1, in <module> ModuleNotFoundError: No module named 'alfred.dl'

有scrap视频的小工具吗？

import 错误

cannot reshape array of size 1 into shape (0,2)

dalao你好，非常感谢alfred！今天尝试着使用alfred可视化了voc和coco，结果发现voc能够正常可视化，但coco好像有些问题。具体命令如下：

alfred data cocoview -j ./annotations/train.json -i ./train2017/

coco报错如下：

Alfred - Valet of Artificial Intelligence.
Author: Lucas Jin
At    : 20202.10.01, since 2019.11.11
Loc   : Shenzhen, China
Star  : http://github.com/jinfagang/alfred
Ver.  : 2.7.1

=> Module: data
=> Action: cocoview
loading annotations into memory...
Done (t=0.05s)
creating index...
index created!
INFO 04.16 17:48:22 view_coco.py:60: cats: [{'supercategory': 'insulator', 'id': 1, 'name': 'insulator'}]
INFO 04.16 17:48:22 view_coco.py:62: all images we got: 2208
checking img: {'height': 640, 'width': 640, 'id': 1, 'file_name': '772.jpg'}, id: 1
INFO 04.16 17:48:22 view_coco.py:73: showing anno: [{'segmentation': [[0.0]], 'iscrowd': 0, 'image_id': 1, 'area': 19434, 'bbox': [22, 248, 79, 246], 'category_id': 1, 'id': 1}, {'segmentation': [[0.0]], 'iscrowd': 0, 'image_id': 1, 'area': 16896, 'bbox': [272, 150, 66, 256], 'category_id': 1, 'id': 2}, {'segmentation': [[0.0]], 'iscrowd': 0, 'image_id': 1, 'area': 16960, 'bbox': [572, 36, 64, 265], 'category_id': 1, 'id': 3}]
Traceback (most recent call last):
  File "/home/ly/anaconda3/envs/evaluate/lib/python3.7/site-packages/alfred/alfred.py", line 307, in main
    vis_coco(img_d, json_f)
  File "/home/ly/anaconda3/envs/evaluate/lib/python3.7/site-packages/alfred/modules/data/view_coco.py", line 109, in vis_coco
    coco.showAnns(annos)
  File "/home/ly/anaconda3/envs/evaluate/lib/python3.7/site-packages/pycocotools/coco.py", line 258, in showAnns
    poly = np.array(seg).reshape((int(len(seg)/2), 2))
ValueError: cannot reshape array of size 1 into shape (0,2)
 parse args error, type -h to see help. msg: cannot reshape array of size 1 into shape (0,2)

想问一下是因为我的json格式和coco不一样造成的报错么？我这个coco格式是从voc那转化过来了，两者在mmdetection上都实践过，都是能正常训练和测试的，希望dalao能够告诉解决方案