This commit is contained in:
Kwabena W Agyeman 2025-10-22 08:39:54 -07:00 committed by GitHub
commit 334b0e97d2
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
5 changed files with 59 additions and 0 deletions

View File

@ -38,6 +38,12 @@
"alignment": 16,
"optimize": "Performance"
},
{
"type": "tflite",
"path": "{TOP}/lib/models/yolov8n_192.tflite",
"alignment": 16,
"optimize": "Performance"
},
{
"type": "tflite",
"path": "{TOP}/lib/models/blazeface_front_128.tflite",

View File

@ -26,6 +26,12 @@
"alignment": 32,
"profile": "default"
},
{
"type": "tflite",
"path": "{TOP}/lib/models/yolov8n_192.tflite",
"alignment": 32,
"profile": "default"
},
{
"type": "tflite",
"path": "{TOP}/lib/models/blazeface_front_128.tflite",

Binary file not shown.

View File

@ -0,0 +1 @@
person

View File

@ -0,0 +1,46 @@
# This work is licensed under the MIT license.
# Copyright (c) 2013-2025 OpenMV LLC. All rights reserved.
# https://github.com/openmv/openmv/blob/master/LICENSE
#
# TensorFlow Lite YOLO V8 Example
#
# This example runs a YOLO V8 person detection model.
#
# NOTE: This exaxmple requires an OpenMV Cam with an NPU like the AE3 or N6 to run real-time.
import csi
import time
import ml
from ml.postprocessing.ultralytics import YoloV8
# Initialize the sensor.
csi0 = csi.CSI()
csi0.reset()
csi0.pixformat(csi.RGB565)
csi0.framesize(csi.VGA)
csi0.window((400, 400))
# Load YOLO V8 model from ROM FS.
model = ml.Model("/rom/yolov8n_192.tflite", postprocess=YoloV8(threshold=0.4))
print(model)
# Visualization parameters.
n = len(model.labels)
model_class_colors = [(int(255 * i // n), int(255 * (n - i - 1) // n), 255) for i in range(n)]
clock = time.clock()
while True:
clock.tick()
img = csi0.snapshot()
# boxes is a list of list per class of ((x, y, w, h), score) tuples
boxes = model.predict([img])
# Draw bounding boxes around the detected objects
for i, class_detections in enumerate(boxes):
rects = [r for r, score in class_detections]
labels = [model.labels[i] for j in range(len(rects))]
colors = [model_class_colors[i] for j in range(len(rects))]
ml.utils.draw_predictions(img, rects, labels, colors, format=None)
print(clock.fps(), "fps")