add face_area_filter (face detector from dlib)

data_juicer/ops/filter/__init__.py

@@ -1,7 +1,8 @@
 from . import (alphanumeric_filter, average_line_length_filter,
                character_repetition_filter, clip_similarity_filter,
-               flagged_words_filter, image_aspect_ratio_filter,
-               image_shape_filter, image_size_filter, language_id_score_filter,
+               face_area_filter, flagged_words_filter,
+               image_aspect_ratio_filter, image_shape_filter,
+               image_size_filter, language_id_score_filter,
                maximum_line_length_filter, perplexity_filter,
                special_characters_filter, specified_field_filter,
                specified_numeric_field_filter, stopwords_filter, suffix_filter,

data_juicer/ops/filter/face_area_filter.py

@@ -0,0 +1,146 @@
+import numpy as np
+from jsonargparse.typing import ClosedUnitInterval
+
+from data_juicer.utils.availability_utils import AvailabilityChecking
+from data_juicer.utils.constant import Fields, StatsKeys
+from data_juicer.utils.mm_utils import load_image, pil_to_opencv
+
+from ..base_op import OPERATORS, Filter
+from ..op_fusion import LOADED_IMAGES
+
+OP_NAME = 'face_area_filter'
+
+with AvailabilityChecking(['dlib'], OP_NAME):
+    import dlib
+
+
+def calculate_max_face_ratio(image, detections):
+    image_area = image.width * image.height
+    max_face_ratio = 0
+    for detection in detections:
+        # 检查是否为有效的检测结果
+        if detection != (-1, -1, -1, -1):
+            _, _, width, height = detection
+            face_area = width * height
+            face_ratio = face_area / image_area
+            max_face_ratio = max(max_face_ratio, face_ratio)
+    return max_face_ratio
+
+
+@OPERATORS.register_module(OP_NAME)
+@LOADED_IMAGES.register_module(OP_NAME)
+class FaceAreaFilter(Filter):
+    """Filter to keep samples with face area ratio within a specific range.
+    """
+
+    def __init__(self,
+                 min_ratio: ClosedUnitInterval = 0.0,
+                 max_ratio: ClosedUnitInterval = 0.4,
+                 any_or_all: str = 'any',
+                 *args,
+                 **kwargs):
+        """
+        Initialization method.
+
+        :param min_ratio: Min ratio for the largest face area in an image.
+        :param max_ratio: Max ratio for the largest face area in an image.
+        :param any_or_all: Keep this sample with 'any' or 'all' strategy of
+            all images. 'any': keep this sample if any images meet the
+            condition. 'all': keep this sample only if all images meet the
+            condition.
+        :param args: Extra positional arguments.
+        :param kwargs: Extra keyword arguments.
+        """
+
+        # Extract face detector arguments from kwargs
+        detector_keys = ['upsample_num_times']
+        self.detector_kwargs = {
+            key: kwargs.pop(key)
+            for key in detector_keys if key in kwargs
+        }
+
+        super().__init__(*args, **kwargs)
+        self.min_ratio = min_ratio
+        self.max_ratio = max_ratio
+
+        if any_or_all not in ['any', 'all']:
+            raise ValueError(f'Keep strategy [{any_or_all}] is not supported. '
+                             f'Can only be one of ["any", "all"].')
+        self.any = (any_or_all == 'any')
+
+        # Initialize face detector
+        self.detector = dlib.get_frontal_face_detector()
+
+    def compute_stats(self, sample, context=False):
+        # check if it's computed already
+        if StatsKeys.face_ratios in sample[Fields.stats]:
+            return sample
+
+        # there is no image in this sample, still default ratio 0.0
+        if self.image_key not in sample or not sample[self.image_key]:
+            sample[Fields.stats][StatsKeys.face_ratios] = [0.0]
+            return sample
+
+        # load images
+        loaded_image_keys = sample[self.image_key]
+        images = {}
+        for loaded_image_key in loaded_image_keys:
+            if context and loaded_image_key in sample[Fields.context]:
+                # load from context
+                images[loaded_image_key] = sample[
+                    Fields.context][loaded_image_key]
+            else:
+                if loaded_image_key not in images:
+                    # avoid load the same images
+                    image = load_image(loaded_image_key)
+                    images[loaded_image_key] = image
+                if context:
+                    # store the image data into context
+                    sample[Fields.context][loaded_image_key] = image
+
+        # check if faces detected already
+        if StatsKeys.face_detections not in sample[Fields.stats]:
+            face_detections = {}
+            for key, image in images.items():
+                img = pil_to_opencv(image)
+                dets = self.detector(img, **self.detector_kwargs)
+                dets_formatted = [[
+                    det.left(),
+                    det.top(),
+                    det.width(),
+                    det.height()
+                ] for det in dets] if dets else [[0, 0, 0, 0]]
+                face_detections[key] = dets_formatted
+            sample[Fields.stats][StatsKeys.face_detections] = [
+                face_detections[key] for key in loaded_image_keys
+            ]
+
+        max_face_ratios = []
+        for key, dets in zip(loaded_image_keys,
+                             sample[Fields.stats][StatsKeys.face_detections]):
+            img_area = images[key].width * images[key].height
+            # Calculate the max face ratio for the current image
+            max_face_ratios.append(
+                max([w * h / img_area for _, _, w, h in dets]))
+        sample[Fields.stats][StatsKeys.face_ratios] = max_face_ratios
+
+        return sample
+
+    def process(self, sample):
+        if self.image_key not in sample or not sample[self.image_key]:
+            return True
+
+        face_ratios = sample[Fields.stats][StatsKeys.face_ratios]
+        if len(face_ratios) <= 0:
+            return True
+
+        keep_bools = np.array([
+            self.min_ratio <= face_ratio <= self.max_ratio
+            for face_ratio in face_ratios
+        ])
+
+        # different strategies
+        if self.any:
+            return keep_bools.any()
+        else:
+            return keep_bools.all()

data_juicer/utils/constant.py

@@ -31,6 +31,8 @@ class StatsKeys(object):
     image_width = 'image_width'
     image_height = 'image_height'
     image_sizes = 'image_sizes'
+    face_ratios = 'face_ratios'
+    face_detections = 'face_detections'
 
     # multimodal
     clip_image_text_similarity = 'clip_image_text_similarity'

data_juicer/utils/mm_utils.py

@@ -1,3 +1,4 @@
+import numpy as np
 from datasets import Audio, Image
 
 from data_juicer.utils.constant import DEFAULT_PREFIX
@@ -34,6 +35,15 @@ def load_audio(path, sampling_rate=None):
     return (aud['array'], aud['sampling_rate'])
 
 
+def pil_to_opencv(pil_image):
+    if pil_image.mode != 'RGB':
+        pil_image = pil_image.convert('RGB')
+    numpy_image = np.array(pil_image)
+    # RGB to BGR
+    opencv_image = numpy_image[:, :, ::-1]
+    return opencv_image
+
+
 def get_image_size(path, ):
     import os
     return os.path.getsize(path)

docs/Operators.md

@@ -11,7 +11,7 @@ The operators in Data-Juicer are categorized into 5 types.
 |-----------------------------------|:------:|-------------------------------------------------|
 | [ Formatter ]( #formatter )       |   7    | Discovers, loads, and canonicalizes source data |
 | [ Mapper ]( #mapper )             |   21   | Edits and transforms samples                    |
-| [ Filter ]( #filter )             |   20   | Filters out low-quality samples                 |
+| [ Filter ]( #filter )             |   21   | Filters out low-quality samples                 |
 | [ Deduplicator ]( #deduplicator ) |   4    | Detects and removes duplicate samples           |
 | [ Selector ]( #selector )         |   2    | Selects top samples based on ranking            |
 
@@ -47,16 +47,16 @@ All the specific operators are listed below, each featured with several capabili
 
 | Operator                                            | Domain             | Lang   | Description                                                                                                    |
 |-----------------------------------------------------|--------------------|--------|----------------------------------------------------------------------------------------------------------------|
-| chinese_convert_mapper                              | General            | zh     | Convert Chinese between Traditional Chinese, Simplified Chinese and Japanese Kanji (by [opencc](https://github.com/BYVoid/OpenCC))                 |
+| chinese_convert_mapper                              | General            | zh     | Converts Chinese between Traditional Chinese, Simplified Chinese and Japanese Kanji (by [opencc](https://github.com/BYVoid/OpenCC))                 |
 | clean_copyright_mapper                              | Code               | en, zh | Removes copyright notice at the beginning of code files (:warning: must contain the word *copyright*)          |
 | clean_email_mapper                                  | General            | en, zh | Removes email information                                                                                      |
 | clean_html_mapper                                   | General            | en, zh | Removes HTML tags and returns plain text of all the nodes                                                      |
 | clean_ip_mapper                                     | General            | en, zh | Removes IP addresses                                                                                           |
 | clean_links_mapper                                  | General, Code      | en, zh | Removes links, such as those starting with http or ftp                                                         |
 | expand_macro_mapper                                 | LaTeX              | en, zh | Expands macros usually defined at the top of TeX documents                                                     |
 | fix_unicode_mapper                                  | General            | en, zh | Fixes broken Unicodes (by [ftfy](https://ftfy.readthedocs.io/))                                                |
-| nlpaug_en_mapper                                    | General            | en     | Simply augment texts in English based on the `nlpaug` library                                                  | 
-| nlpcda_zh_mapper                                    | General            | zh     | Simply augment texts in Chinese based on the `nlpcda` library                                                  | 
+| nlpaug_en_mapper                                    | General            | en     | Simply augments texts in English based on the `nlpaug` library                                                 | 
+| nlpcda_zh_mapper                                    | General            | zh     | Simply augments texts in Chinese based on the `nlpcda` library                                                 | 
 | punctuation_normalization_mapper                    | General            | en, zh | Normalizes various Unicode punctuations to their ASCII equivalents                                             |
 | remove_bibliography_mapper                          | LaTeX              | en, zh | Removes the bibliography of TeX documents                                                                      |
 | remove_comments_mapper                              | LaTeX              | en, zh | Removes the comments of TeX documents                                                                          |
@@ -77,11 +77,12 @@ All the specific operators are listed below, each featured with several capabili
 | alphanumeric_filter            | General | en, zh | Keeps samples with alphanumeric ratio within the specified range                           |
 | average_line_length_filter     | Code    | en, zh | Keeps samples with average line length within the specified range                          |
 | character_repetition_filter    | General | en, zh | Keeps samples with char-level n-gram repetition ratio within the specified range           |
-| clip_similarity_filter      | Multimodal |   -    |  Keeps samples with similarity between text and images within the specified range           |
+| clip_similarity_filter         | Multimodal |   -    |  Keeps samples with similarity between text and images within the specified range       |
+| face_area_filter               | Image   |   -    | Keeps samples contains images with face area ratios within the specified range             |
 | flagged_words_filter           | General | en, zh | Keeps samples with flagged-word ratio below the specified threshold                        |
-| image_aspect_ratio_filter      | Image   |   -    | Keeps samples contains images with aspect ratios within specific range                     |
-| image_shape_filter             | Image   |   -    | Keeps samples contains images with widths and heights within specific ranges               |
-| image_size_filter      | Image   |   -    | Keeps samples contains images whose size in bytes are within specific range                     |
+| image_aspect_ratio_filter      | Image   |   -    | Keeps samples contains images with aspect ratios within the specified range                |
+| image_shape_filter             | Image   |   -    | Keeps samples contains images with widths and heights within specified range               |
+| image_size_filter              | Image   |   -    | Keeps samples contains images whose size in bytes are within specified range               |
 | language_id_score_filter       | General | en, zh | Keeps samples of the specified language, judged by a predicted confidence score            |
 | maximum_line_length_filter     | Code    | en, zh | Keeps samples with maximum line length within the specified range                          |
 | perplexity_filter              | General | en, zh | Keeps samples with perplexity score below the specified threshold                          |
@@ -98,12 +99,12 @@ All the specific operators are listed below, each featured with several capabili
 
 ## Deduplicator <a name="deduplicator"/>
 
-| Operator                      | Domain  | Lang   | Description                                                 |
-|-------------------------------|---------|--------|-------------------------------------------------------------|
-| document_deduplicator         | General | en, zh | Deduplicate samples at document-level by comparing MD5 hash |
-| document_minhash_deduplicator | General | en, zh | Deduplicate samples at document-level using MinHashLSH      |
-| document_simhash_deduplicator | General | en, zh | Deduplicate samples at document-level using SimHash         |
-| image_deduplicator            | Image   |   -    | Deduplicate samples at document-level using exact matching of images between documents |
+| Operator                      | Domain  | Lang   | Description                                                  |
+|-------------------------------|---------|--------|--------------------------------------------------------------|
+| document_deduplicator         | General | en, zh | Deduplicates samples at document-level by comparing MD5 hash |
+| document_minhash_deduplicator | General | en, zh | Deduplicates samples at document-level using MinHashLSH      |
+| document_simhash_deduplicator | General | en, zh | Deduplicates samples at document-level using SimHash         |
+| image_deduplicator            | Image   |   -    | Deduplicates samples at document-level using exact matching of images between documents |
 
 
 ## Selector <a name="selector"/>

docs/Operators_ZH.md

@@ -10,7 +10,7 @@ Data-Juicer 中的算子分为以下 5 种类型。
 |------------------------------------|:--:|---------------|
 | [ Formatter ]( #formatter )        |  7 | 发现、加载、规范化原始数据 |
 | [ Mapper ]( #mapper )              | 21 | 对数据样本进行编辑和转换  |
-| [ Filter ]( #filter )              | 20  | 过滤低质量样本       |
+| [ Filter ]( #filter )              | 21 | 过滤低质量样本       |
 | [ Deduplicator ]( #deduplicator )  |  4 | 识别、删除重复样本     |
 | [ Selector ]( #selector )          |  2 | 基于排序选取高质量样本   |
 
@@ -75,6 +75,7 @@ Data-Juicer 中的算子分为以下 5 种类型。
 | average_line_length_filter     | Code    | en, zh | 保留平均行长度在指定范围内的样本                   |
 | character_repetition_filter    | General | en, zh | 保留 char-level n-gram 重复比率在指定范围内的样本 |
 | clip_similarity_filter         | Multimodal |   -    | 保留文本图像相似度在指定范围内的样本                 |
+| face_area_filter               | Image   |   -    | 保留样本中包含的图片的最大脸部区域在指定范围内的样本     |
 | flagged_words_filter           | General | en, zh | 保留使标记字比率保持在指定阈值以下的样本               |
 | image_aspect_ratio_filter      | Image   | -      | 保留样本中包含的图片的宽高比在指定范围内的样本            |
 | image_shape_filter             | Image   |   -    | 保留样本中包含的图片的形状（即宽和高）在指定范围内的样本       |

environments/science_requires.txt

@@ -12,3 +12,4 @@ transformers
 opencc==1.1.6
 imagededup
 torch
+opencv-python