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Added visualization script to visualize results and render videos
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‎README.md

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## Usage
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### Validation
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The package supports evaluation and validation of tracking results. The
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procedure can be shown for an example directory structure in the CTC-format
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```
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In this example, all four sequences will be validated.
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### Evaluation
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To evaluate results against Ground Truth, similar commands can be used.
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To evaluate the results of the sequence ```challenge_x/01``` against the
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```
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### Visualization
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<center><img src="assets/visualization.jpg"></center>
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You can visualize the tracking results with the following command:
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```bash
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ctc_visualize --img "/ctc/train/challenge_x/01" --res "/ctc/train/challenge_x/01_RES"
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```
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The command will show the visualizations of the tracking results. You can
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control the visualization with specific keys:
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| Key | Description |
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|-------|---------------------------------------------------------------------|
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| q | Quits the Application |
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| w | Start or Pause the auto visualization |
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| d | Move to the next frame |
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| a | Move to the previous frame |
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| l | Toggle the show labels option |
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| p | Toggle the show parents option |
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| s | Save the current frame to the visualization directory as .jpg image |
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There are additional arguments that can be used to specify the evaluation. The
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following table shows the available arguments:
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| Argument | Description | Default |
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|-------------------|------------------------------------------------------------------------------------------|---------|
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| --img | The directory to the images (required) | |
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| --res | The directory to the result masks (required) | |
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| --viz | The directory to save the visualizations | None |
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| --video-name | The path to the video if a video should be created | None |
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| --border-width | The width of the border. Either an integer or a string that describes the challenge name | None |
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| --show-no-labels | Print no instance labels to the output as default | False |
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| --show-no-parents | Print no parent labels to the output as default | False |
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| --ids-to-show | The IDs of the instances to show. If defined, all others will be ignored. | None |
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| --start-frame | The frame to start the visualization | 0 |
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| --framerate | The framerate of the video | 10 |
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| --opacity | The opacity of the instance colors | 0.5 |
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**Note:** The argument `--video-name` describes a path to a video file. The file
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extension needs to be `.mp4`. If a video will be created, the visualization is
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not shown.
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## Notes
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‎assets/visualization.jpg

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‎ctc_metrics/scripts/visualize.py

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import argparse
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from os import listdir, makedirs
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from os.path import join
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import tifffile as tiff
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import cv2
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import numpy as np
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from ctc_metrics.utils.filesystem import read_tracking_file
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SHOW_BORDER = True
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BORDER_WIDTH = {
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"BF-C2DL-HSC": 25,
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"BF-C2DL-MuSC": 25,
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"Fluo-N2DL-HeLa": 25,
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"PhC-C2DL-PSC": 25,
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"Fluo-N2DH-SIM+": 0,
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"DIC-C2DH-HeLa": 50,
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"Fluo-C2DL-Huh7": 50,
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"Fluo-C2DL-MSC": 50,
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"Fluo-N2DH-GOWT1": 50,
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"PhC-C2DH-U373": 50,
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}
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np.random.seed(0)
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PALETTE = np.random.randint(0, 256, (100000, 3))
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def get_palette_color(i):
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i = i % PALETTE.shape[0]
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return PALETTE[i]
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def visualize(
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img_dir: str,
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res_dir: str,
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viz_dir: str = None,
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video_name: str = None,
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border_width=None,
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show_labels: bool=True,
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show_parents: bool = True,
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ids_to_show: list = None,
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start_frame: int = 0,
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framerate: int = 30,
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opacity: float = 0.5,
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): # pylint: disable=too-many-arguments
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"""
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Visualizes the tracking results.
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Args:
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img_dir: str
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The path to the images.
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res_dir: str
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The path to the results.
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viz_dir: str
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The path to save the visualizations.
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video_name: str
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The path to the video if a video should be created. Note that no
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visualization is available during video creation.
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border_width: str or int
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The width of the border. Either an integer or a string that
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describes the challenge name.
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show_labels: bool
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Print instance labels to the output.
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show_parents: bool
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Print parent labels to the output.
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ids_to_show: list
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The IDs of the instances to show. All others will be ignored.
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start_frame: int
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The frame to start the visualization.
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framerate: int
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The framerate of the video.
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opacity: float
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The opacity of the instance colors.
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Returns:
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None
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"""
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# Define initial video parameters
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wait_time = max(1, round(1000 / framerate))
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if border_width is None:
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border_width = 0
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elif isinstance(border_width, str):
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try:
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border_width = int(border_width)
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except ValueError as exc:
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if border_width in BORDER_WIDTH:
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border_width = BORDER_WIDTH[border_width]
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else:
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raise ValueError(
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f"Border width '{border_width}' not recognized. "
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f"Existing datasets: {BORDER_WIDTH.keys()}"
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) from exc
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# Load image and tracking data
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images = [x for x in sorted(listdir(img_dir)) if x.endswith(".tif")]
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results = [x for x in sorted(listdir(res_dir)) if x.endswith(".tif")]
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parents = {
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l[0]: l[3] for l in read_tracking_file(join(res_dir, "res_track.txt"))
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}
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# Create visualization directory
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if viz_dir:
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makedirs(viz_dir, exist_ok=True)
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video_writer = None
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# Loop through all images
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while start_frame < len(images):
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# Read image file
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img_name, res_name = images[start_frame], results[start_frame]
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img_path, res_path, = join(img_dir, img_name), join(res_dir, res_name)
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print(f"\rFrame {img_name} (of {len(images)})", end="")
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# Visualize the image
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viz = create_colored_image(
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cv2.imread(img_path),
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tiff.imread(res_path),
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labels=show_labels,
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frame=start_frame,
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parents=parents if show_parents else None,
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ids_to_show=ids_to_show,
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opacity=opacity,
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)
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if border_width > 0:
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viz = cv2.rectangle(
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viz,
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(border_width, border_width),
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(viz.shape[1] - border_width, viz.shape[0] - border_width),
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(0, 0, 255), 1
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)
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# Save the visualization
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if video_name is not None:
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if video_writer is None:
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video_path = join(
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viz_dir, f"{video_name.replace('.mp4', '')}.mp4")
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video_writer = cv2.VideoWriter(
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video_path,
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cv2.VideoWriter_fourcc(*"mp4v"),
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framerate,
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(viz.shape[1], viz.shape[0])
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)
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video_writer.write(viz)
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start_frame += 1
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continue
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# Show the video
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cv2.imshow("VIZ", viz)
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key = cv2.waitKey(wait_time)
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if key == ord("q"):
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# Quit the visualization
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break
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if key == ord("w"):
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# Start or stop the auto visualization
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if wait_time == 0:
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wait_time = max(1, round(1000 / framerate))
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else:
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wait_time = 0
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elif key == ord("d"):
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# Move to the next frame
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start_frame += 1
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wait_time = 0
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elif key == ord("a"):
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# Move to the previous frame
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start_frame -= 1
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wait_time = 0
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elif key == ord("l"):
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# Toggle the show labels option
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show_labels = not show_labels
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elif key == ord("p"):
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# Toggle the show parents option
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show_parents = not show_parents
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elif key == ord("s"):
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# Save the visualization
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if viz_dir is None:
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print("Please define the '--viz' argument to save the "
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"visualizations.")
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continue
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viz_path = join(viz_dir, img_name) + ".jpg"
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cv2.imwrite(viz_path, viz)
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else:
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# Move to the next frame
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start_frame += 1
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def create_colored_image(
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img: np.ndarray,
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res: np.ndarray,
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labels: bool = False,
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opacity: float = 0.5,
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ids_to_show = None,
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frame: int = None,
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parents: dict = None,
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):
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"""
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Creates a colored image from the input image and the results.
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Args:
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img: np.ndarray
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The input image.
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res: np.ndarray
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The results.
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labels: bool
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Print instance labels to the output.
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opacity: float
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The opacity of the instance colors.
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ids_to_show: list
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The IDs of the instances to show. All others will be ignored.
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frame: int
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The frame number.
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parents: dict
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The parent dictionary.
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Returns:
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The colored image.
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"""
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img = np.clip(img, 0, 255).astype(np.uint8)
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kernel = np.ones((3, 3), dtype=np.uint8)
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for i in np.unique(res):
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if i == 0:
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continue
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if ids_to_show is not None:
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if i not in ids_to_show:
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continue
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mask = res == i
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contour = (mask * 255).astype(np.uint8) - \
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cv2.erode((mask * 255).astype(np.uint8), kernel)
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contour = contour != 0
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img[mask] = (
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np.round((1 - opacity) * img[mask] + opacity * get_palette_color(i))
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)
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img[contour] = get_palette_color(i)
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if frame is not None:
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cv2.putText(img, str(frame), (10, 30),
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cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)
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if labels:
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# Print label to the center of the object
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y, x = np.where(mask)
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y, x = np.mean(y), np.mean(x)
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text = str(i)
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if parents is not None:
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if i in parents:
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if parents[i] != 0:
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text += f"({parents[i]})"
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cv2.putText(img, text, (int(x), int(y)),
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cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
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return img
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def parse_args():
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""" Parses the arguments. """
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parser = argparse.ArgumentParser(description='Validates CTC-Sequences.')
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parser.add_argument(
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'--img', type=str, required=True,
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help='The path to the images.'
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)
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parser.add_argument(
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'--res', type=str, required=True, help='The path to the results.'
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)
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parser.add_argument(
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'--viz', type=str, default=None,
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help='The path to save the visualizations.'
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)
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parser.add_argument(
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'--video-name', type=str, default=None,
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help='The path to the video if a video should be created. Note that no '
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'visualization is available during video creation.'
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)
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parser.add_argument(
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'--border-width', type=str, default=None,
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help='The width of the border. Either an integer or a string that '
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'describes the challenge name.'
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)
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parser.add_argument(
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'--show-no-labels', action="store_false",
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help='Print no instance labels to the output.'
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)
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parser.add_argument(
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'--show-no-parents', action="store_false",
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help='Print no parent labels to the output.'
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)
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parser.add_argument(
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'--ids-to-show', type=int, nargs='+', default=None,
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help='The IDs of the instances to show. All others will be ignored.'
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)
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parser.add_argument(
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'--start-frame', type=int, default=0,
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help='The frame to start the visualization.'
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)
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parser.add_argument(
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'--framerate', type=int, default=10,
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help='The framerate of the video.'
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)
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parser.add_argument(
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'--opacity', type=float, default=0.5,
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help='The opacity of the instance colors.'
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)
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args = parser.parse_args()
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return args
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def main():
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"""
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Main function that is called when the script is executed.
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"""
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args = parse_args()
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visualize(
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args.img,
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args.res,
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viz_dir=args.viz,
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video_name=args.video_name,
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border_width=args.border_width,
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show_labels=args.show_no_labels,
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show_parents=args.show_no_parents,
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ids_to_show=args.ids_to_show,
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start_frame=args.start_frame,
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framerate=args.framerate,
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opacity=args.opacity,
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)
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if __name__ == "__main__":
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main()

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