Hi, I want to ask you for help, I am testing the FOMO model for the first time and I ran into a problem. The model as such works great, but I don’t know how or if it is even possible to turn off the merging of individual objects into one big one when they are close to each other. I use an OpenMV H7 device, can you help me? Well thank you 
How it suppose to looks like:
Object is merged and len(detection_list) will return 1:
# Edge Impulse - OpenMV Object Detection Example
import sensor, image, time, os, tf, math, uos, gc
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
labels = None
min_confidence = 0.5
try:
# load the model, alloc the model file on the heap if we have at least 64K free after loading
net = tf.load("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) + ')')
try:
labels = [line.rstrip('\n') for line in open("labels.txt")]
except Exception as e:
raise Exception('Failed to load "labels.txt", did you copy the .tflite and labels.txt file onto the mass-storage device? (' + str(e) + ')')
colors = [ # Add more colors if you are detecting more than 7 types of classes at once.
(255, 0, 0),
( 0, 255, 0),
(255, 255, 0),
( 0, 0, 255),
(255, 0, 255),
( 0, 255, 255),
(255, 255, 255),
]
clock = time.clock()
while(True):
clock.tick()
img = sensor.snapshot()
# detect() returns all objects found in the image (splitted out per class already)
# we skip class index 0, as that is the background, and then draw circles of the center
# of our objects
for i, detection_list in enumerate(net.detect(img, thresholds=[(math.ceil(min_confidence * 255), 255)])):
if (i == 0): continue # background class
if (len(detection_list) == 0): continue # no detections for this class?
print("********** %s **********" % labels[i])
for d in detection_list:
[x, y, w, h] = d.rect()
center_x = math.floor(x + (w / 2))
center_y = math.floor(y + (h / 2))
print('x %d\ty %d' % (center_x, center_y))
img.draw_circle((center_x, center_y, 12), color=colors[i], thickness=2)
print(len(detection_list))
Thank you very much