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