有没有人遇到这个问题的，在eage impulse上训练完模型，烧录一直报这个错误

[xe3b]

[vucr]

我也是这样的，不知道为啥

[avco]

我的plus也这样，这周刚买的

[fysq]

官方的例子也是一样,应该是版本的问题，这么长时间了也没解决？
https://book.openmv.cc/example/25-Machine-Learning/tf-mnist.html

https://book.openmv.cc/project/traffic-sign.html

[fysq]

@avco 版本问题，4.6.0也会出现，需要回退固件版本到4.5.4，

[kidswong999]

新的固件，使用下面新的代码：

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")