Related Objects of Interest: stop, no entry, bus, car, give way, priority road, phone, no vehicles, pedestrians, roundabout
Top Snow Computer Vision Models
The models below have been fine-tuned for various snow 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 snow detection model.
At the bottom of this page, we have guides on how to count snow in images and videos.
4320 images 127 classes
by soochow
1746 images 61 classes
by kendrickxy
2312 images 119 classes
roundabout Bump Car breaking Children Crosswinds Curve Cyclist crossing Cyclists Dip Domestic animals End of all previously signed restrictions End of no overtaking End of no overtaking by heavy goods vehicles End of no parking zone End of priority road End of speed limit 30 End of speed limit zone 30 Entering city Exiting city Falling rocks
by MIS326
715 images 43 classes
1992 images 43 classes
Ahead only Beware of ice/snow Bicycles Crossing Bumpy Road Children Crossing Double curve End of all speed and passing limits End of no passing End of no passing by vehicles over 3.5 metric tons End of speed limit (80km/h) General caution Go straight or left Go straight or right Keep left Keep right Left curve No entry No passing No passing for vehicles over 3.5 metric tons No vehicles
by Traffic Sign
588 images 47 classes
roundabout Ahead Only Beware of Snow/Ice Bumpy Road Cautionary Road Sign Crossroads Ahead Cycle Crossing End-of-no-passing Give way Left Zigzag Bend No Entry No Overtaking No Overtaking By Heavy Goods Vehicles No Speed Limit No Trucks Allowed No Vehicles Other Danger Prevents Vehicles From Entering Priority Intersection Sign Priority Road
1308 images 43 classes
Ahead only Beware of ice/snow Bicycles crossing Bumpy road Children crossing Dangerous curve to the left Dangerous curve to the right Double curve End of all speed and passing limits End of no passing End of no passing by vehicles over 3.5 metric tons End of speed limit (80km/h) General caution Go straight or left Go straight or right Keep left Keep right No entry No passing No passing for vehicles over 3.5 metric tons
Guide: How to Track Snow Crossing a Line
You can count how many snow have crossed a line using the supervision LineCounter
method.
The following code snippet counts the number of snow 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 = "snow.mp4"
TARGET_VIDEO_PATH = "snow_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 snow project you choose above
project = rf.workspace("upn-veteran-yogyakarta-university").project("apple-segmentation-84luf")
snow_model = project.version(4).model
# create BYTETracker instance
snow_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 = snow_model.predict(frame).json()
snow = sv.Detections.from_roboflow(results)
# show snow detections in real time
print(snow)
# tracking snow detections
snow = snow_tracker.update_with_detections(snow)
annotated_frame = trace_annotator.annotate(
scene=frame.copy(),
detections=snow
)
annotated_frame=box_annotator.annotate(
scene=annotated_frame,
detections=snow
)
# update line counter
line_zone.trigger(snow)
# 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
)