218 lines
8.2 KiB
Python
218 lines
8.2 KiB
Python
# copyright (c) 2022 PaddlePaddle Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import numpy as np
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import paddle
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import paddle.nn.functional as F
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class VehicleAttribute(object):
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def __init__(self, color_threshold=0.5, type_threshold=0.5):
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self.color_threshold = color_threshold
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self.type_threshold = type_threshold
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self.color_list = [
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"yellow", "orange", "green", "gray", "red", "blue", "white",
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"golden", "brown", "black"
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]
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self.type_list = [
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"sedan", "suv", "van", "hatchback", "mpv", "pickup", "bus",
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"truck", "estate"
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]
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def __call__(self, x, file_names=None):
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if isinstance(x, dict):
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x = x['logits']
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assert isinstance(x, paddle.Tensor)
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if file_names is not None:
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assert x.shape[0] == len(file_names)
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x = F.sigmoid(x).numpy()
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# postprocess output of predictor
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batch_res = []
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for idx, res in enumerate(x):
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res = res.tolist()
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label_res = []
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color_idx = np.argmax(res[:10])
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type_idx = np.argmax(res[10:])
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print(color_idx, type_idx)
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if res[color_idx] >= self.color_threshold:
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color_info = f"Color: ({self.color_list[color_idx]}, prob: {res[color_idx]})"
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else:
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color_info = "Color unknown"
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if res[type_idx + 10] >= self.type_threshold:
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type_info = f"Type: ({self.type_list[type_idx]}, prob: {res[type_idx + 10]})"
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else:
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type_info = "Type unknown"
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label_res = f"{color_info}, {type_info}"
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threshold_list = [self.color_threshold
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] * 10 + [self.type_threshold] * 9
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pred_res = (np.array(res) > np.array(threshold_list)
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).astype(np.int8).tolist()
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batch_res.append({
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"attr": label_res,
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"pred": pred_res,
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"file_name": file_names[idx]
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})
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return batch_res
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class PersonAttribute(object):
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def __init__(self,
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threshold=0.5,
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glasses_threshold=0.3,
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hold_threshold=0.6):
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self.threshold = threshold
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self.glasses_threshold = glasses_threshold
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self.hold_threshold = hold_threshold
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def __call__(self, x, file_names=None):
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if isinstance(x, dict):
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x = x['logits']
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assert isinstance(x, paddle.Tensor)
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if file_names is not None:
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assert x.shape[0] == len(file_names)
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x = F.sigmoid(x).numpy()
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# postprocess output of predictor
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age_list = ['AgeLess18', 'Age18-60', 'AgeOver60']
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direct_list = ['Front', 'Side', 'Back']
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bag_list = ['HandBag', 'ShoulderBag', 'Backpack']
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upper_list = ['UpperStride', 'UpperLogo', 'UpperPlaid', 'UpperSplice']
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lower_list = [
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'LowerStripe', 'LowerPattern', 'LongCoat', 'Trousers', 'Shorts',
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'Skirt&Dress'
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]
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batch_res = []
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for idx, res in enumerate(x):
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res = res.tolist()
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label_res = []
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# gender
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gender = 'Female' if res[22] > self.threshold else 'Male'
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label_res.append(gender)
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# age
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age = age_list[np.argmax(res[19:22])]
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label_res.append(age)
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# direction
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direction = direct_list[np.argmax(res[23:])]
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label_res.append(direction)
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# glasses
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glasses = 'Glasses: '
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if res[1] > self.glasses_threshold:
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glasses += 'True'
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else:
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glasses += 'False'
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label_res.append(glasses)
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# hat
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hat = 'Hat: '
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if res[0] > self.threshold:
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hat += 'True'
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else:
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hat += 'False'
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label_res.append(hat)
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# hold obj
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hold_obj = 'HoldObjectsInFront: '
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if res[18] > self.hold_threshold:
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hold_obj += 'True'
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else:
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hold_obj += 'False'
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label_res.append(hold_obj)
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# bag
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bag = bag_list[np.argmax(res[15:18])]
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bag_score = res[15 + np.argmax(res[15:18])]
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bag_label = bag if bag_score > self.threshold else 'No bag'
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label_res.append(bag_label)
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# upper
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upper_res = res[4:8]
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upper_label = 'Upper:'
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sleeve = 'LongSleeve' if res[3] > res[2] else 'ShortSleeve'
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upper_label += ' {}'.format(sleeve)
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for i, r in enumerate(upper_res):
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if r > self.threshold:
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upper_label += ' {}'.format(upper_list[i])
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label_res.append(upper_label)
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# lower
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lower_res = res[8:14]
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lower_label = 'Lower: '
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has_lower = False
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for i, l in enumerate(lower_res):
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if l > self.threshold:
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lower_label += ' {}'.format(lower_list[i])
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has_lower = True
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if not has_lower:
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lower_label += ' {}'.format(lower_list[np.argmax(lower_res)])
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label_res.append(lower_label)
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# shoe
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shoe = 'Boots' if res[14] > self.threshold else 'No boots'
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label_res.append(shoe)
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threshold_list = [0.5] * len(res)
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threshold_list[1] = self.glasses_threshold
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threshold_list[18] = self.hold_threshold
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pred_res = (np.array(res) > np.array(threshold_list)
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).astype(np.int8).tolist()
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batch_res.append({"attributes": label_res, "output": pred_res})
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return batch_res
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class TableAttribute(object):
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def __init__(self,
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source_threshold=0.5,
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number_threshold=0.5,
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color_threshold=0.5,
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clarity_threshold=0.5,
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obstruction_threshold=0.5,
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angle_threshold=0.5,
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):
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self.source_threshold = source_threshold
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self.number_threshold = number_threshold
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self.color_threshold = color_threshold
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self.clarity_threshold = clarity_threshold
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self.obstruction_threshold = obstruction_threshold
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self.angle_threshold = angle_threshold
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def __call__(self, x, file_names=None):
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if isinstance(x, dict):
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x = x['logits']
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assert isinstance(x, paddle.Tensor)
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if file_names is not None:
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assert x.shape[0] == len(file_names)
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x = F.sigmoid(x).numpy()
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# postprocess output of predictor
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batch_res = []
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for idx, res in enumerate(x):
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res = res.tolist()
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label_res = []
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source = 'Scanned' if res[0] > self.source_threshold else 'Photo'
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number = 'Little' if res[1] > self.number_threshold else 'Numerous'
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color = 'Black-and-White' if res[2] > self.color_threshold else 'Multicolor'
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clarity = 'Clear' if res[3] > self.clarity_threshold else 'Blurry'
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obstruction = 'Without-Obstacles' if res[4] > self.number_threshold else 'With-Obstacles'
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angle = 'Horizontal' if res[5] > self.number_threshold else 'Tilted'
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label_res = [source, number, color, clarity, obstruction, angle]
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threshold_list = [self.source_threshold, self.number_threshold, self.color_threshold, self.clarity_threshold, self.obstruction_threshold, self.angle_threshold]
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pred_res = (np.array(res) > np.array(threshold_list)
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).astype(np.int8).tolist()
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batch_res.append({"attributes": label_res, "output": pred_res, "file_name": file_names[idx]})
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return batch_res
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