scripts/examples: Update fomo example to use the new post-processor.

This commit is contained in:
Kwabena W. Agyeman 2025-08-24 15:11:34 -07:00
parent de7a761d3b
commit 8e72debf11

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@ -9,9 +9,8 @@
import sensor
import time
import ml
from ml.utils import NMS
from ml.postprocessing import fomo_postprocess
import math
import image
sensor.reset() # Reset and initialize the sensor.
sensor.set_pixformat(sensor.RGB565) # Set pixel format to RGB565 (or GRAYSCALE)
@ -19,9 +18,6 @@ sensor.set_framesize(sensor.QVGA) # Set frame size to QVGA (320x240)
sensor.set_windowing((240, 240)) # Set 240x240 window.
sensor.skip_frames(time=2000) # Let the camera adjust.
min_confidence = 0.4
threshold_list = [(math.ceil(min_confidence * 255), 255)]
# Load built-in FOMO face detection model
model = ml.Model("/rom/fomo_face_detection.tflite")
print(model)
@ -40,39 +36,13 @@ colors = [ # Add more colors if you are detecting more than 7 types of classes
(255, 255, 255),
]
# FOMO outputs an image per class where each pixel in the image is the centroid of the trained
# object. So, we will get those output images and then run find_blobs() on them to extract the
# centroids. We will also run get_stats() on the detected blobs to determine their score.
# The Non-Max-Supression (NMS) object then filters out overlapping detections and maps their
# position in the output image back to the original input image. The function then returns a
# list per class which each contain a list of (rect, score) tuples representing the detected
# objects.
def fomo_post_process(model, inputs, outputs):
n, oh, ow, oc = model.output_shape[0]
nms = NMS(ow, oh, inputs[0].roi)
for i in range(oc):
img = image.Image(outputs[0][0, :, :, i] * 255)
blobs = img.find_blobs(
threshold_list, x_stride=1, area_threshold=1, pixels_threshold=1
)
for b in blobs:
rect = b.rect()
x, y, w, h = rect
score = (
img.get_statistics(thresholds=threshold_list, roi=rect).l_mean() / 255.0
)
nms.add_bounding_box(x, y, x + w, y + h, score, i)
return nms.get_bounding_boxes()
clock = time.clock()
while True:
clock.tick()
img = sensor.snapshot()
for i, detection_list in enumerate(model.predict([img], callback=fomo_post_process)):
for i, detection_list in enumerate(model.predict([img], callback=fomo_postprocess())):
if i == 0:
continue # background class
if len(detection_list) == 0: