See this post for how to post-process the image:
OpenMV Firmware v4.5.6 and up TensorFlow Porting Guide - OpenMV Products - OpenMV Forums
Segmentation images are just FOMO models. So, basically you just need this code:
import sensor, image, time, os, ml, math, uos, gc
from ulab import numpy as np
sensor.reset() # Reset and initialize the sensor.
sensor.set_pixformat(sensor.RGB565) # Set pixel format to RGB565 (or GRAYSCALE)
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.
net = None
try:
# load the model, alloc the model file on the heap if we have at least 64K free after loading
net = ml.Model("trained.tflite", load_to_fb=uos.stat('trained.tflite')[6] > (gc.mem_free() - (64*1024)))
except Exception as e:
raise Exception('Failed to load "trained.tflite", did you copy the .tflite and labels.txt file onto the mass-storage device? (' + str(e) + ')')
def post_process(model, inputs, outputs):
# example of creating an image from a (1, 16, 16, N) fomo output image.
ob, oh, ow, oc = model.output_shape[0]
return [image.Image(outputs[0][0, :, :, i] * 255) for range(oc)]
clock = time.clock()
while(True):
clock.tick()
img = sensor.snapshot()
image_list = net.predict([img], callback=post_process)
print(clock.fps(), "fps", end="\n\n")
From your model output above: ((1,37, 525), (1,32,40,40)) looks like a dual tensor output model. So, we should be able to run it. You’ll find each tensor output under output[0]
and ouput[1]
.
For slicing (1,32,40,40) do:
# making an assumption here... change this if wrong.
ob, oc, oh, ow = model.output_shape[1]
return [image.Image(outputs[1][0, i, :, :] * 255) for range(oc)]