Top Reading Computer Vision Models
The models below have been fine-tuned for various reading 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 reading detection model.
At the bottom of this page, we have guides on how to count readings in images and videos.
How to Count Readings with Computer Vision
With a model hosted on Roboflow like the ones above and the open source supervision Python package, you can count readings in your images and videos.
The following code snippet counts the number of readings 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 reading project you choose above
project = rf.workspace("read2heardatasets").project("r2h-dataset")
reading_model = project.version(5).model
results = reading_model.predict("reading.jpg").json()
readings = sv.Detections.from_roboflow(results)
# print number of readings
print(len(readings))How to Count Readings in a Zone
With a bit more code, you can count the number of reading present in a specific zone of your image or video.
The following code snippet counts the number of reading 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 = "reading.mp4"
TARGET_VIDEO_PATH = "reading_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 reading project you choose above
project = rf.workspace("read2heardatasets").project("r2h-dataset")
reading_model = project.version(5).model
# create BYTETracker instance
reading_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 = reading_model.predict(frame).json()
readings = sv.Detections.from_roboflow(results)
# show reading detections in real time
print(readings)
# tracking reading detections
readings = reading_tracker.update_with_detections(readings)
annotated_frame = box_annotator.annotate(scene=frame, detections=readings)
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
)