出现“module object has no attribute "load"是啥原因，按照视频教程弄得

[vucr]

# 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(clock.fps(), "fps", end="\n\n")

[kidswong999]

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")