问题遇到的现象和发生背景

python如何在下方代码中加个判断，如输出项大于0.9输出该数字，如果小于0.9，则是上个数识别出的数+1？

问题相关代码，请勿粘贴截图

max_idx = 0; max_val = predictions_list[0][1]
for i in range(len(predictions_list)):
if predictions_list[i][1] >= max_val:
max_val = predictions_list[i][1]
max_idx = i

print("%s = %f" % (predictions_list[max_idx][0], predictions_list[max_idx][1]))

运行结果及报错内容

现在是运行正常的

我的解答思路和尝试过的方法

我尝试添加if嵌套，但是输出项变为了多个。刚入行

我想要达到的结果

输出项大于0.9输出该数字，如果小于0.9，则是上个数+1？

如果max_val就是第一项呢？
不考虑这个的话，循环完成了判断max_val的值是否符合条件，不符合就把max_idx减1再输出

看看你的predictions_list是什么样的

不想又多个输出项可以先初始化一个标记变量flag=True，接着写一个if判断语句，如果大于0.9则重新个flag赋值为flag=True，如果小于0.9重新个flag赋值为flag=Falase，或者计数变量直接加1.再写一个if语句判断当前的flag是True还是Falase，True输出，False则+1

Edge Impulse - OpenMV Image Classification Example

import sensor, image, time, os, tf, uos, gc

sensor.reset() # Reset and initialize the sensor.
sensor.set_pixformat(sensor.GRAYSCALE) # Set pixel format to RGB565 (or GRAYSCALE)
sensor.set_framesize(sensor.QVGA) # Set frame size to QVGA (320x240)
sensor.set_windowing((60, 30, 160, 160)) # Set 240x240 window. # Set 240x240 window.
sensor.skip_frames(time=2000) # Let the camera adjust.

您必须关闭自动增益控制和自动白平衡，否则他们将更改图像增益以撤消您放置的任何曝光设置...

sensor.set_auto_gain(False)
sensor.set_auto_whitebal(False)
sensor.set_auto_exposure(False,exposure_us = int(6000))

net = None
labels = None

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:
print(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) + ')')

clock = time.clock()
while(True):
clock.tick()

img = sensor.snapshot()

# default settings just do one detection... change them to search the image...
for obj in net.classify(img, min_scale=1.0, scale_mul=0.8, x_overlap=0.5, y_overlap=0.5):
    print("**********\nPredictions at [x=%d,y=%d,w=%d,h=%d]" % obj.rect())
    img.draw_rectangle(obj.rect())
    # This combines the labels and confidence values into a list of tuples
    predictions_list = list(zip(labels, obj.output()))


    max_idx = 0; max_val = predictions_list[0][1]
    for i in range(len(predictions_list)):
        if predictions_list[i][1] >= max_val:
            max_val = predictions_list[i][1]
            max_idx = i
    print("%s = %f" % (predictions_list[max_idx][0], predictions_list[max_idx][1]))


print(clock.fps(), "fps")