Top Scratch Computer Vision Models
The models below have been fine-tuned for various scratch 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 scratch detection model.
At the bottom of this page, we have guides on how to count scratches in images and videos. To learn more about defect detection with computer vision, check out the following resources:
Guide: How to Count Scratches with Computer Vision
With a model hosted on Roboflow like the ones above and the open source supervision Python package, you can count scratches in your images and videos.
The following code snippet counts the number of scratches present in an image.
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 supervision as sv
import roboflow
roboflow.login()
rf = roboflow.Roboflow()
# replace with the scratch project you choose above
project = rf.workspace("v7").project("640640v8")
scratch_model = project.version(23).model
results = scratch_model.predict("scratch.jpg").json()
scratches = sv.Detections.from_roboflow(results)
# print number of scratches
print(len(scratches))Guide: How to Count Scratches in a Zone
With a bit more code, you can count the number of scratch present in a specific zone of your image or video.
The following code snippet counts the number of scratch present in each frame 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.
Read our blog post on counting objects in a zone
import numpy as np
import supervision as sv
import roboflow
SOURCE_VIDEO_PATH = "scratch.mp4"
TARGET_VIDEO_PATH = "scratch_out.mp4"
# use https://roboflow.github.io/polygonzone/ to get the points for your shape
polygon = np.array([
# draw 50x50 box in top left corner
[0, 0],
[50, 0],
[50, 50],
[0, 50]
])
roboflow.login()
rf = roboflow.Roboflow()
# replace with the scratch project you choose above
project = rf.workspace("v7").project("640640v8")
scratch_model = project.version(23).model
# create BYTETracker instance
scratch_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 PolygonZone instance
zone = sv.PolygonZone(polygon=polygon, frame_resolution_wh=(video_info.width, video_info.height))
# create box annotator
box_annotator = sv.BoxAnnotator(thickness=4, text_thickness=4, text_scale=2)
colors = sv.ColorPalette.default()
# create instance of BoxAnnotator
zone_annotator = sv.PolygonZoneAnnotator(thickness=4, text_thickness=4, text_scale=2, zone=zone, color=colors.colors[0])
# 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 = scratch_model.predict(frame).json()
scratches = sv.Detections.from_roboflow(results)
# show scratch detections in real time
print(scratches)
# tracking scratch detections
scratches = scratch_tracker.update_with_detections(scratches)
annotated_frame = box_annotator.annotate(scene=frame, detections=scratches)
annotated_frame = zone_annotator.annotate(scene=annotated_frame)
# return frame with box and line annotated result
return annotated_frame
# process the whole video
sv.process_video(
source_path = SOURCE_VIDEO_PATH,
target_path = TARGET_VIDEO_PATH,
callback=callback
)Guide: How to Track Scratches Crossing a Line
You can count how many scratches have crossed a line using the supervision LineCounter method.
The following code snippet counts the number of scratches 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 = "scratch.mp4"
TARGET_VIDEO_PATH = "scratch_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 scratch project you choose above
project = rf.workspace("v7").project("640640v8")
scratch_model = project.version(23).model
# create BYTETracker instance
scratch_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 = scratch_model.predict(frame).json()
scratches = sv.Detections.from_roboflow(results)
# show scratch detections in real time
print(scratches)
# tracking scratch detections
scratches = scratch_tracker.update_with_detections(scratches)
annotated_frame = trace_annotator.annotate(
scene=frame.copy(),
detections=scratches
)
annotated_frame=box_annotator.annotate(
scene=annotated_frame,
detections=scratches
)
# update line counter
line_zone.trigger(scratches)
# 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
)