运行这个代码：
import sensor
import time
import ml
from ml.utils import NMS
import math
import image
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.
min_confidence = 0.4
threshold_list = [(math.ceil(min_confidence * 255), 255)]
print(model)
model = ml.Model("trained.tflite", load_to_fb=True)
labels = [line.rstrip('\n') for line in open("labels.txt")]
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),
]
# 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)):
if i == 0:
continue # background class
if len(detection_list) == 0:
continue # no detections for this class?
print("********** %s **********" % labels[i])
for (x, y, w, h), score in detection_list:
center_x = math.floor(x + (w / 2))
center_y = math.floor(y + (h / 2))
print(f"x {center_x}\ty {center_y}\tscore {score}")
img.draw_circle((center_x, center_y, 12), color=colors[i])
print(clock.fps(), "fps", end="\n")