python - 使用python找到具有最佳“分数”的组后，如何从列表中提取一组值？

Question

我有一个列表（“虚拟”）。我只想提取与最佳“分数”相关的值。例如，从列表中我应该提取以下值：

    "labels: imagenet_labels.txt ",
    "Model: inception_v4_299_quant_edgetpu.tflite ",
    "Image: insect.jpg ",
    "Time(ms): 121.8",
    "Time(ms): 101.2",
    "Inference: admiral",
    "Score: 0.59375 ",
    "TPU_temp(°C): 57.05",

为什么？因为“分数：0.59375”是与列表中其他“分数”相比的最高值。

我怎样才能做到这一点？

谢谢

编码

#This is the list

dummy = ['labels: imagenet_labels.txt \n', '\n', 'Model: efficientnet-edgetpu-S_quant_edgetpu.tflite \n', '\n', 'Image: insect.jpg \n', '\n', '*The first inference on Edge TPU is slow because it includes loading the model into Edge TPU memory*\n', 'Time(ms): 23.1\n', 'Time(ms): 5.7\n', '\n', '\n', 'Inference: corkscrew, bottle screw\n', 'Score: 0.03125 \n', '\n', 'TPU_temp(°C): 57.05\n', '##################################### \n', '\n', 'labels: imagenet_labels.txt \n', '\n', 'Model: efficientnet-edgetpu-M_quant_edgetpu.tflite \n', '\n', 'Image: insect.jpg \n', '\n', '*The first inference on Edge TPU is slow because it includes loading the model into Edge TPU memory*\n', 'Time(ms): 29.3\n', 'Time(ms): 10.8\n', '\n', '\n', "Inference: dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk\n", 'Score: 0.09375 \n', '\n', 'TPU_temp(°C): 56.8\n', '##################################### \n', '\n', 'labels: imagenet_labels.txt \n', '\n', 'Model: efficientnet-edgetpu-L_quant_edgetpu.tflite \n', '\n', 'Image: insect.jpg \n', '\n', '*The first inference on Edge TPU is slow because it includes loading the model into Edge TPU memory*\n', 'Time(ms): 45.6\n', 'Time(ms): 31.0\n', '\n', '\n', 'Inference: pick, plectrum, plectron\n', 'Score: 0.09766 \n', '\n', 'TPU_temp(°C): 57.55\n', '##################################### \n', '\n', 'labels: imagenet_labels.txt \n', '\n', 'Model: inception_v3_299_quant_edgetpu.tflite \n', '\n', 'Image: insect.jpg \n', '\n', '*The first inference on Edge TPU is slow because it includes loading the model into Edge TPU memory*\n', 'Time(ms): 68.8\n', 'Time(ms): 51.3\n', '\n', '\n', 'Inference: ringlet, ringlet butterfly\n', 'Score: 0.48047 \n', '\n', 'TPU_temp(°C): 57.3\n', '##################################### \n', '\n', 'labels: imagenet_labels.txt \n', '\n', 'Model: inception_v4_299_quant_edgetpu.tflite \n', '\n', 'Image: insect.jpg \n', '\n', '*The first inference on Edge TPU is slow because it includes loading the model into Edge TPU memory*\n', 'Time(ms): 121.8\n', 'Time(ms): 101.2\n', '\n', '\n', 'Inference: admiral\n', 'Score: 0.59375 \n', '\n', 'TPU_temp(°C): 57.05\n', '##################################### \n', '\n', 'labels: imagenet_labels.txt \n', '\n', 'Model: inception_v2_224_quant_edgetpu.tflite \n', '\n', 'Image: insect.jpg \n', '\n', '*The first inference on Edge TPU is slow because it includes loading the model into Edge TPU memory*\n', 'Time(ms): 34.3\n', 'Time(ms): 16.6\n', '\n', '\n', 'Inference: lycaenid, lycaenid butterfly\n', 'Score: 0.41406 \n', '\n', 'TPU_temp(°C): 57.3\n', '##################################### \n', '\n', 'labels: imagenet_labels.txt \n', '\n', 'Model: mobilenet_v2_1.0_224_quant_edgetpu.tflite \n', '\n', 'Image: insect.jpg \n', '\n', '*The first inference on Edge TPU is slow because it includes loading the model into Edge TPU memory*\n', 'Time(ms): 14.4\n', 'Time(ms): 3.3\n', '\n', '\n', 'Inference: leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea\n', 'Score: 0.36328 \n', '\n', 'TPU_temp(°C): 57.3\n', '##################################### \n', '\n', 'labels: imagenet_labels.txt \n', '\n', 'Model: mobilenet_v1_1.0_224_quant_edgetpu.tflite \n', '\n', 'Image: insect.jpg \n', '\n', '*The first inference on Edge TPU is slow because it includes loading the model into Edge TPU memory*\n', 'Time(ms): 14.5\n', 'Time(ms): 3.0\n', '\n', '\n', 'Inference: bow tie, bow-tie, bowtie\n', 'Score: 0.33984 \n', '\n', 'TPU_temp(°C): 57.3\n', '##################################### \n', '\n', 'labels: imagenet_labels.txt \n', '\n', 'Model: inception_v1_224_quant_edgetpu.tflite \n', '\n', 'Image: insect.jpg \n', '\n', '*The first inference on Edge TPU is slow because it includes loading the model into Edge TPU memory*\n', 'Time(ms): 21.2\n', 'Time(ms): 3.6\n', '\n', '\n', 'Inference: pick, plectrum, plectron\n', 'Score: 0.17578 \n', '\n', 'TPU_temp(°C): 57.3\n', '##################################### \n', '\n']


### This is to clean the data

regex = re.compile(r'(\w+)\((.+)\):\s(.*)|(\w+:)\s(.*)')
match_regex = list(filter(regex.match, dummy))
match = [line.strip('\n').strip() for line in match_regex]  
print("match list", match, "\n")

“虚拟”列表看起来像这样

[
    "labels: imagenet_labels.txt ",
    "Model: efficientnet-edgetpu-S_quant_edgetpu.tflite ",
    "Image: insect.jpg ",
    "Time(ms): 23.1",
    "Time(ms): 5.7",
    "Inference: corkscrew, bottle screw",
    "Score: 0.03125 ",
    "TPU_temp(°C): 57.05",
    "labels: imagenet_labels.txt ",
    "Model: efficientnet-edgetpu-M_quant_edgetpu.tflite ",
    "Image: insect.jpg ",
    "Time(ms): 29.3",
    "Time(ms): 10.8",
    "Inference: dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk",
    "Score: 0.09375 ",
    "TPU_temp(°C): 56.8",
    "labels: imagenet_labels.txt ",
    "Model: efficientnet-edgetpu-L_quant_edgetpu.tflite ",
    "Image: insect.jpg ",
    "Time(ms): 45.6",
    "Time(ms): 31.0",
    "Inference: pick, plectrum, plectron",
    "Score: 0.09766 ",
    "TPU_temp(°C): 57.55",
    "labels: imagenet_labels.txt ",
    "Model: inception_v3_299_quant_edgetpu.tflite ",
    "Image: insect.jpg ",
    "Time(ms): 68.8",
    "Time(ms): 51.3",
    "Inference: ringlet, ringlet butterfly",
    "Score: 0.48047 ",
    "TPU_temp(°C): 57.3",
    "labels: imagenet_labels.txt ",
    "Model: inception_v4_299_quant_edgetpu.tflite ",
    "Image: insect.jpg ",
    "Time(ms): 121.8",
    "Time(ms): 101.2",
    "Inference: admiral",
    "Score: 0.59375 ",
    "TPU_temp(°C): 57.05",
    "labels: imagenet_labels.txt ",
    "Model: inception_v2_224_quant_edgetpu.tflite ",
    "Image: insect.jpg ",
    "Time(ms): 34.3",
    "Time(ms): 16.6",
    "Inference: lycaenid, lycaenid butterfly",
    "Score: 0.41406 ",
    "TPU_temp(°C): 57.3",
    "labels: imagenet_labels.txt ",
    "Model: mobilenet_v2_1.0_224_quant_edgetpu.tflite ",
    "Image: insect.jpg ",
    "Time(ms): 14.4",
    "Time(ms): 3.3",
    "Inference: leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea",
    "Score: 0.36328 ",
    "TPU_temp(°C): 57.3",
    "labels: imagenet_labels.txt ",
    "Model: mobilenet_v1_1.0_224_quant_edgetpu.tflite ",
    "Image: insect.jpg ",
    "Time(ms): 14.5",
    "Time(ms): 3.0",
    "Inference: bow tie, bow-tie, bowtie",
    "Score: 0.33984 ",
    "TPU_temp(°C): 57.3",
    "labels: imagenet_labels.txt ",
    "Model: inception_v1_224_quant_edgetpu.tflite ",
    "Image: insect.jpg ",
    "Time(ms): 21.2",
    "Time(ms): 3.6",
    "Inference: pick, plectrum, plectron",
    "Score: 0.17578 ",
    "TPU_temp(°C): 57.3",
]

标签： pythonlist

Answer 1

您可能应该使用字典而不是您显示的列表，但是您可以使用当前的设置这样做：

假设您的列表名称是 x 那么您可以：

max_score = 0
pos = 0
for element in x:
    if "Score" in element:
        if max_score<=(float(element[7:])): # the text is always in the same position, you could use a regular expression instead 
            max_score = float(element[7:])
            max_pos = pos
    pos+=1

print(x[max_pos-6:max_pos+1])