Top Moon Computer Vision Models
The models below have been fine-tuned for various moon detection tasks. You can try out each model in your browser, or test an edge deployment solution (i.e. to an NVIDIA Jetson). You can use the datasets associated with the models below as a starting point for building your own moon detection model.
At the bottom of this page, we have guides on how to count the moon in images and videos.
How to Track The Moon Crossing a Line
You can count how many the moon have crossed a line using the supervision LineCounter method.
The following code snippet counts the number of the moon that cross a line in a video.
To use the snippet below, you will need to run pip install roboflow supervision. Replace the project name and model name with any model trained on Universe, such as those listed above.
import numpy as np
import supervision as sv
import roboflow
SOURCE_VIDEO_PATH = "moon.mp4"
TARGET_VIDEO_PATH = "moon_out.mp4"
# use https://roboflow.github.io/polygonzone/ to get the points for your line
LINE_START = sv.Point(0, 300)
LINE_END = sv.Point(800, 300)
roboflow.login()
rf = roboflow.Roboflow()
# replace with the moon project you choose above
project = rf.workspace("esra").project("esra-data-annotation")
moon_model = project.version(5).model
# create BYTETracker instance
moon_tracker = sv.ByteTrack(track_thresh=0.25, track_buffer=30, match_thresh=0.8, frame_rate=30)
# create VideoInfo instance
video_info = sv.VideoInfo.from_video_path(SOURCE_VIDEO_PATH)
# create frame generator
generator = sv.get_video_frames_generator(SOURCE_VIDEO_PATH)
# create LineZone instance, it is previously called LineCounter class
line_zone = sv.LineZone(start=LINE_START, end=LINE_END)
# create instance of BoxAnnotator
box_annotator = sv.BoxAnnotator(thickness=4, text_thickness=4, text_scale=2)
# create instance of TraceAnnotator
trace_annotator = sv.TraceAnnotator(thickness=4, trace_length=50)
line_zone_annotator = sv.LineZoneAnnotator(thickness=4, text_thickness=4, text_scale=2)
# define call back function to be used in video processing
def callback(frame: np.ndarray, index:int) -> np.ndarray:
# model prediction on single frame and conversion to supervision Detections
results = moon_model.predict(frame).json()
the_moon = sv.Detections.from_roboflow(results)
# show moon detections in real time
print(the_moon)
# tracking moon detections
the_moon = moon_tracker.update_with_detections(the_moon)
annotated_frame = trace_annotator.annotate(
scene=frame.copy(),
detections=the_moon
)
annotated_frame=box_annotator.annotate(
scene=annotated_frame,
detections=the_moon
)
# update line counter
line_zone.trigger(the_moon)
# return frame with box and line annotated result
return line_zone_annotator.annotate(annotated_frame, line_counter=line_zone)
# process the whole video
sv.process_video(
source_path = SOURCE_VIDEO_PATH,
target_path = TARGET_VIDEO_PATH,
callback=callback
)