ootdiffusion

preprocess/humanparsing/mhp_extension/coco_style_annotation_creator/human_to_coco.py

+import argparse
+import datetime
+import json
+import os
+from PIL import Image
+import numpy as np
+
+import pycococreatortools
+
+
+def get_arguments():
+    parser = argparse.ArgumentParser(description="transform mask annotation to coco annotation")
+    parser.add_argument("--dataset", type=str, default='CIHP', help="name of dataset (CIHP, MHPv2 or VIP)")
+    parser.add_argument("--json_save_dir", type=str, default='../data/msrcnn_finetune_annotations',
+                        help="path to save coco-style annotation json file")
+    parser.add_argument("--use_val", type=bool, default=False,
+                        help="use train+val set for finetuning or not")
+    parser.add_argument("--train_img_dir", type=str, default='../data/instance-level_human_parsing/Training/Images',
+                        help="train image path")
+    parser.add_argument("--train_anno_dir", type=str,
+                        default='../data/instance-level_human_parsing/Training/Human_ids',
+                        help="train human mask path")
+    parser.add_argument("--val_img_dir", type=str, default='../data/instance-level_human_parsing/Validation/Images',
+                        help="val image path")
+    parser.add_argument("--val_anno_dir", type=str,
+                        default='../data/instance-level_human_parsing/Validation/Human_ids',
+                        help="val human mask path")
+    return parser.parse_args()
+
+
+def main(args):
+    INFO = {
+        "description": args.split_name + " Dataset",
+        "url": "",
+        "version": "",
+        "year": 2019,
+        "contributor": "xyq",
+        "date_created": datetime.datetime.utcnow().isoformat(' ')
+    }
+
+    LICENSES = [
+        {
+            "id": 1,
+            "name": "",
+            "url": ""
+        }
+    ]
+
+    CATEGORIES = [
+        {
+            'id': 1,
+            'name': 'person',
+            'supercategory': 'person',
+        },
+    ]
+
+    coco_output = {
+        "info": INFO,
+        "licenses": LICENSES,
+        "categories": CATEGORIES,
+        "images": [],
+        "annotations": []
+    }
+
+    image_id = 1
+    segmentation_id = 1
+
+    for image_name in os.listdir(args.train_img_dir):
+        image = Image.open(os.path.join(args.train_img_dir, image_name))
+        image_info = pycococreatortools.create_image_info(
+            image_id, image_name, image.size
+        )
+        coco_output["images"].append(image_info)
+
+        human_mask_name = os.path.splitext(image_name)[0] + '.png'
+        human_mask = np.asarray(Image.open(os.path.join(args.train_anno_dir, human_mask_name)))
+        human_gt_labels = np.unique(human_mask)
+
+        for i in range(1, len(human_gt_labels)):
+            category_info = {'id': 1, 'is_crowd': 0}
+            binary_mask = np.uint8(human_mask == i)
+            annotation_info = pycococreatortools.create_annotation_info(
+                segmentation_id, image_id, category_info, binary_mask,
+                image.size, tolerance=10
+            )
+            if annotation_info is not None:
+                coco_output["annotations"].append(annotation_info)
+
+            segmentation_id += 1
+        image_id += 1
+
+    if not os.path.exists(args.json_save_dir):
+        os.makedirs(args.json_save_dir)
+    if not args.use_val:
+        with open('{}/{}_train.json'.format(args.json_save_dir, args.split_name), 'w') as output_json_file:
+            json.dump(coco_output, output_json_file)
+    else:
+        for image_name in os.listdir(args.val_img_dir):
+            image = Image.open(os.path.join(args.val_img_dir, image_name))
+            image_info = pycococreatortools.create_image_info(
+                image_id, image_name, image.size
+            )
+            coco_output["images"].append(image_info)
+
+            human_mask_name = os.path.splitext(image_name)[0] + '.png'
+            human_mask = np.asarray(Image.open(os.path.join(args.val_anno_dir, human_mask_name)))
+            human_gt_labels = np.unique(human_mask)
+
+            for i in range(1, len(human_gt_labels)):
+                category_info = {'id': 1, 'is_crowd': 0}
+                binary_mask = np.uint8(human_mask == i)
+                annotation_info = pycococreatortools.create_annotation_info(
+                    segmentation_id, image_id, category_info, binary_mask,
+                    image.size, tolerance=10
+                )
+                if annotation_info is not None:
+                    coco_output["annotations"].append(annotation_info)
+
+                segmentation_id += 1
+            image_id += 1
+
+        with open('{}/{}_trainval.json'.format(args.json_save_dir, args.split_name), 'w') as output_json_file:
+            json.dump(coco_output, output_json_file)
+
+    coco_output_val = {
+        "info": INFO,
+        "licenses": LICENSES,
+        "categories": CATEGORIES,
+        "images": [],
+        "annotations": []
+    }
+
+    image_id_val = 1
+    segmentation_id_val = 1
+
+    for image_name in os.listdir(args.val_img_dir):
+        image = Image.open(os.path.join(args.val_img_dir, image_name))
+        image_info = pycococreatortools.create_image_info(
+            image_id_val, image_name, image.size
+        )
+        coco_output_val["images"].append(image_info)
+
+        human_mask_name = os.path.splitext(image_name)[0] + '.png'
+        human_mask = np.asarray(Image.open(os.path.join(args.val_anno_dir, human_mask_name)))
+        human_gt_labels = np.unique(human_mask)
+
+        for i in range(1, len(human_gt_labels)):
+            category_info = {'id': 1, 'is_crowd': 0}
+            binary_mask = np.uint8(human_mask == i)
+            annotation_info = pycococreatortools.create_annotation_info(
+                segmentation_id_val, image_id_val, category_info, binary_mask,
+                image.size, tolerance=10
+            )
+            if annotation_info is not None:
+                coco_output_val["annotations"].append(annotation_info)
+
+            segmentation_id_val += 1
+        image_id_val += 1
+
+    with open('{}/{}_val.json'.format(args.json_save_dir, args.split_name), 'w') as output_json_file_val:
+        json.dump(coco_output_val, output_json_file_val)
+
+
+if __name__ == "__main__":
+    args = get_arguments()
+    main(args)

preprocess/humanparsing/mhp_extension/coco_style_annotation_creator/pycococreatortools.py

+import re
+import datetime
+import numpy as np
+from itertools import groupby
+from skimage import measure
+from PIL import Image
+from pycocotools import mask
+
+convert = lambda text: int(text) if text.isdigit() else text.lower()
+natrual_key = lambda key: [convert(c) for c in re.split('([0-9]+)', key)]
+
+
+def resize_binary_mask(array, new_size):
+    image = Image.fromarray(array.astype(np.uint8) * 255)
+    image = image.resize(new_size)
+    return np.asarray(image).astype(np.bool_)
+
+
+def close_contour(contour):
+    if not np.array_equal(contour[0], contour[-1]):
+        contour = np.vstack((contour, contour[0]))
+    return contour
+
+
+def binary_mask_to_rle(binary_mask):
+    rle = {'counts': [], 'size': list(binary_mask.shape)}
+    counts = rle.get('counts')
+    for i, (value, elements) in enumerate(groupby(binary_mask.ravel(order='F'))):
+        if i == 0 and value == 1:
+            counts.append(0)
+        counts.append(len(list(elements)))
+
+    return rle
+
+
+def binary_mask_to_polygon(binary_mask, tolerance=0):
+    """Converts a binary mask to COCO polygon representation
+    Args:
+        binary_mask: a 2D binary numpy array where '1's represent the object
+        tolerance: Maximum distance from original points of polygon to approximated
+            polygonal chain. If tolerance is 0, the original coordinate array is returned.
+    """
+    polygons = []
+    # pad mask to close contours of shapes which start and end at an edge
+    padded_binary_mask = np.pad(binary_mask, pad_width=1, mode='constant', constant_values=0)
+    contours = measure.find_contours(padded_binary_mask, 0.5)
+    contours = np.subtract(contours, 1)
+    for contour in contours:
+        contour = close_contour(contour)
+        contour = measure.approximate_polygon(contour, tolerance)
+        if len(contour) < 3:
+            continue
+        contour = np.flip(contour, axis=1)
+        segmentation = contour.ravel().tolist()
+        # after padding and subtracting 1 we may get -0.5 points in our segmentation 
+        segmentation = [0 if i < 0 else i for i in segmentation]
+        polygons.append(segmentation)
+
+    return polygons
+
+
+def create_image_info(image_id, file_name, image_size,
+                      date_captured=datetime.datetime.utcnow().isoformat(' '),
+                      license_id=1, coco_url="", flickr_url=""):
+    image_info = {
+        "id": image_id,
+        "file_name": file_name,
+        "width": image_size[0],
+        "height": image_size[1],
+        "date_captured": date_captured,
+        "license": license_id,
+        "coco_url": coco_url,
+        "flickr_url": flickr_url
+    }
+
+    return image_info
+
+
+def create_annotation_info(annotation_id, image_id, category_info, binary_mask,
+                           image_size=None, tolerance=2, bounding_box=None):
+    if image_size is not None:
+        binary_mask = resize_binary_mask(binary_mask, image_size)
+
+    binary_mask_encoded = mask.encode(np.asfortranarray(binary_mask.astype(np.uint8)))
+
+    area = mask.area(binary_mask_encoded)
+    if area < 1:
+        return None
+
+    if bounding_box is None:
+        bounding_box = mask.toBbox(binary_mask_encoded)
+
+    if category_info["is_crowd"]:
+        is_crowd = 1
+        segmentation = binary_mask_to_rle(binary_mask)
+    else:
+        is_crowd = 0
+        segmentation = binary_mask_to_polygon(binary_mask, tolerance)
+        if not segmentation:
+            return None
+
+    annotation_info = {
+        "id": annotation_id,
+        "image_id": image_id,
+        "category_id": category_info["id"],
+        "iscrowd": is_crowd,
+        "area": area.tolist(),
+        "bbox": bounding_box.tolist(),
+        "segmentation": segmentation,
+        "width": binary_mask.shape[1],
+        "height": binary_mask.shape[0],
+    }
+
+    return annotation_info

preprocess/humanparsing/mhp_extension/coco_style_annotation_creator/test_human2coco_format.py

+import argparse
+import datetime
+import json
+import os
+from PIL import Image
+
+import pycococreatortools
+
+
+def get_arguments():
+    parser = argparse.ArgumentParser(description="transform mask annotation to coco annotation")
+    parser.add_argument("--dataset", type=str, default='CIHP', help="name of dataset (CIHP, MHPv2 or VIP)")
+    parser.add_argument("--json_save_dir", type=str, default='../data/CIHP/annotations',
+                        help="path to save coco-style annotation json file")
+    parser.add_argument("--test_img_dir", type=str, default='../data/CIHP/Testing/Images',
+                        help="test image path")
+    return parser.parse_args()
+
+args = get_arguments()
+
+INFO = {
+    "description": args.dataset + "Dataset",
+    "url": "",
+    "version": "",
+    "year": 2020,
+    "contributor": "yunqiuxu",
+    "date_created": datetime.datetime.utcnow().isoformat(' ')
+}
+
+LICENSES = [
+    {
+        "id": 1,
+        "name": "",
+        "url": ""
+    }
+]
+
+CATEGORIES = [
+    {
+        'id': 1,
+        'name': 'person',
+        'supercategory': 'person',
+    },
+]
+
+
+def main(args):
+    coco_output = {
+        "info": INFO,
+        "licenses": LICENSES,
+        "categories": CATEGORIES,
+        "images": [],
+        "annotations": []
+    }
+
+    image_id = 1
+
+    for image_name in os.listdir(args.test_img_dir):
+        image = Image.open(os.path.join(args.test_img_dir, image_name))
+        image_info = pycococreatortools.create_image_info(
+            image_id, image_name, image.size
+        )
+        coco_output["images"].append(image_info)
+        image_id += 1
+
+    if not os.path.exists(os.path.join(args.json_save_dir)):
+        os.mkdir(os.path.join(args.json_save_dir))
+
+    with open('{}/{}.json'.format(args.json_save_dir, args.dataset), 'w') as output_json_file:
+        json.dump(coco_output, output_json_file)
+
+
+if __name__ == "__main__":
+    main(args)

preprocess/humanparsing/mhp_extension/detectron2/.clang-format

+AccessModifierOffset: -1
+AlignAfterOpenBracket: AlwaysBreak
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+AlignEscapedNewlinesLeft: true
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+AllowShortBlocksOnASingleLine: false
+AllowShortCaseLabelsOnASingleLine: false
+AllowShortFunctionsOnASingleLine: Empty
+AllowShortIfStatementsOnASingleLine: false
+AllowShortLoopsOnASingleLine: false
+AlwaysBreakAfterReturnType: None
+AlwaysBreakBeforeMultilineStrings: true
+AlwaysBreakTemplateDeclarations: true
+BinPackArguments: false
+BinPackParameters: false
+BraceWrapping:
+  AfterClass:      false
+  AfterControlStatement: false
+  AfterEnum:       false
+  AfterFunction:   false
+  AfterNamespace:  false
+  AfterObjCDeclaration: false
+  AfterStruct:     false
+  AfterUnion:      false
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+  BeforeElse:      false
+  IndentBraces:    false
+BreakBeforeBinaryOperators: None
+BreakBeforeBraces: Attach
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+BreakConstructorInitializersBeforeComma: false
+BreakAfterJavaFieldAnnotations: false
+BreakStringLiterals: false
+ColumnLimit:     80
+CommentPragmas:  '^ IWYU pragma:'
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+ConstructorInitializerIndentWidth: 4
+ContinuationIndentWidth: 4
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+DerivePointerAlignment: false
+DisableFormat:   false
+ForEachMacros:   [ FOR_EACH, FOR_EACH_ENUMERATE, FOR_EACH_KV, FOR_EACH_R, FOR_EACH_RANGE, ]
+IncludeCategories:
+  - Regex:           '^<.*\.h(pp)?>'
+    Priority:        1
+  - Regex:           '^<.*'
+    Priority:        2
+  - Regex:           '.*'
+    Priority:        3
+IndentCaseLabels: true
+IndentWidth:     2
+IndentWrappedFunctionNames: false
+KeepEmptyLinesAtTheStartOfBlocks: false
+MacroBlockBegin: ''
+MacroBlockEnd:   ''
+MaxEmptyLinesToKeep: 1
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+ObjCBlockIndentWidth: 2
+ObjCSpaceAfterProperty: false
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+PenaltyBreakBeforeFirstCallParameter: 1
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+PenaltyExcessCharacter: 1000000
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+SpaceBeforeParens: ControlStatements
+SpaceInEmptyParentheses: false
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+SpacesInContainerLiterals: true
+SpacesInCStyleCastParentheses: false
+SpacesInParentheses: false
+SpacesInSquareBrackets: false
+Standard:        Cpp11
+TabWidth:        8
+UseTab:          Never

preprocess/humanparsing/mhp_extension/detectron2/.flake8

+# This is an example .flake8 config, used when developing *Black* itself.
+# Keep in sync with setup.cfg which is used for source packages.
+
+[flake8]
+ignore = W503, E203, E221, C901, C408, E741
+max-line-length = 100
+max-complexity = 18
+select = B,C,E,F,W,T4,B9
+exclude = build,__init__.py

preprocess/humanparsing/mhp_extension/detectron2/.github/CODE_OF_CONDUCT.md

+# Code of Conduct
+
+Facebook has adopted a Code of Conduct that we expect project participants to adhere to.
+Please read the [full text](https://code.fb.com/codeofconduct/)
+so that you can understand what actions will and will not be tolerated.