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Related Objects of Interest: slush , snowy owl , bird-drop , clean , dusty , electrical-damage , physical-damage , snow-covered , fogsmog , frost
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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.
1.4k images 2220 classes 1 model
5.8k images 8 classes 1 model
9.9k images 525 classes 1 model
ABBOTTS BABBLER ABBOTTS BOOBY ABYSSINIAN GROUND HORNBILL AFRICAN CROWNED CRANE AFRICAN EMERALD CUCKOO AFRICAN FIREFINCH AFRICAN OYSTER CATCHER AFRICAN PIED HORNBILL AFRICAN PYGMY GOOSE ALBATROSS ALBERTS TOWHEE ALEXANDRINE PARAKEET ALPINE CHOUGH ALTAMIRA YELLOWTHROAT AMERICAN AVOCET AMERICAN BITTERN AMERICAN COOT AMERICAN DIPPER AMERICAN FLAMINGO AMERICAN GOLDFINCH
3.3k images 525 classes 1 model
ABBOTTS BABBLER ABBOTTS BOOBY ABYSSINIAN GROUND HORNBILL AFRICAN CROWNED CRANE AFRICAN EMERALD CUCKOO AFRICAN FIREFINCH AFRICAN OYSTER CATCHER AFRICAN PIED HORNBILL AFRICAN PYGMY GOOSE ALBATROSS ALBERTS TOWHEE ALEXANDRINE PARAKEET ALPINE CHOUGH ALTAMIRA YELLOWTHROAT AMERICAN AVOCET AMERICAN BITTERN AMERICAN COOT AMERICAN DIPPER AMERICAN FLAMINGO AMERICAN GOLDFINCH
793 images 7 classes 2 models
793 images 6 classes 1 model
773 images 6 classes 1 model
7.5k images 160 classes 1 model
6.2k images 35 classes 1 model
acorn-woodpecker annas-hummingbird blue-jay blue-winged-warbler carolina-chickadee carolina-wren chipping-sparrow common-eider common-yellowthroat dark-eyed-junco eastern-bluebird eastern-towhee harris-hawk hermit-thrush indigo-bunting juniper-titmouse northern-cardinal northern-mockingbird northern-waterthrush orchard-oriole
5.6k images 36 classes 1 model
acorn-woodpecker annas-hummingbird blue-jay blue-winged-warbler carolina-chickadee carolina-wren chipping-sparrow common-eider common-yellowthroat dark-eyed-junco eastern-bluebird eastern-towhee harris-hawk hermit-thrush indigo-bunting juniper-titmouse northern-cardinal northern-mockingbird northern-waterthrush orchard-oriole
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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("jeswin-wilson-vc5cd").project("roads-0oaql")
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
)