^jL|dZddlmZddlZddlZddlmZmZddlm Z ddl m Z ddl m Z e ZgdZed d Gd d Zed d Gd dZeezZd"dZd#dZd$dZd%dZd&dZd'dZd(dZd)dZd*dZd+dZd,dZd-dZd.dZd/dZ dd d0dZ!dd d1d Z"d2d!Z#y)3z4Private helpers for COCO keypoint schema extraction.) annotationsN) dataclassfield)Path)Any) get_logger)CocoKeypointSchemaKeypointSchemaYoloKeypointSchemaactive_keypoint_countsinfer_coco_keypoint_schemainfer_yolo_keypoint_schemaT)frozenslotscLeZdZUdZded<ded<ded<eeZded <y ) r axKeypoint schema inferred from COCO category metadata. Args: class_names: Category names sorted by category id. num_keypoints_per_class: Number of keypoints for each sorted category. keypoint_oks_sigmas: Default OKS sigmas matching the largest keypoint class. keypoint_flip_pairs: Flat horizontal-flip swap pairs inferred from keypoint names. Returns: Immutable schema value used to configure keypoint training. Raises: This value object does not raise. Example: >>> CocoKeypointSchema(["person"], [17], [0.1] * 17).num_keypoints_per_class [17] list[str] class_names list[int]num_keypoints_per_class list[float]keypoint_oks_sigmasdefault_factorykeypoint_flip_pairsN__name__ __module__ __qualname____doc____annotations__rlistrk/var/www/ramen.bs-engineer-server.com/venv/lib/python3.12/site-packages/rfdetr/datasets/_keypoint_schema.pyr r s+&&&$$%*4%@@r#r cjeZdZUdZded<ded<ded<ded<ded <d ed <ee Zded <y)r aKeypoint schema inferred from an Ultralytics YOLO pose YAML file. Args: class_names: Class names ordered by YOLO class id. num_keypoints_per_class: Number of keypoints for each YOLO class slot. keypoint_oks_sigmas: Default OKS sigmas matching the global keypoint count. keypoint_names: Keypoint names ordered by keypoint index. flip_idx: Optional Ultralytics horizontal-flip index mapping. keypoint_dim: Number of keypoint dimensions in label files, either 2 or 3. Returns: Immutable schema value used to configure YOLO pose training. Raises: This value object does not raise. Example: >>> YoloKeypointSchema(["person"], [1], [0.1], ["nose"], [], 3).keypoint_dim 3 rrrrrrkeypoint_namesflip_idxint keypoint_dimrrNrr"r#r$r r 9s=*&&$$%*4%@@r#r cddl}|jd5}|j|}dddtts.t dt |dt|jd|S#1swYIxYw)aLoad a YAML file and require a mapping root. Args: yaml_path: Path to a YAML data file. Returns: Parsed YAML mapping. Raises: ValueError: If the YAML root is not a mapping. OSError: If the file cannot be read. Example: >>> import tempfile >>> path = Path(tempfile.mkdtemp()) / "data.yaml" >>> _ = path.write_text("names: [person]\nkpt_shape: [1, 3]\n", encoding="utf-8") >>> sorted(_load_yaml_mapping(path)) ['kpt_shape', 'names'] rNutf-8encodingzExpected mapping in data file , got .) yamlopen safe_load isinstancedict ValueErrorstrtyper) yaml_pathr0filedatas r$_load_yaml_mappingr;]sx(  )$T~~d#$ dD !9#i.9K6RVW[R\ReReQffghii K $$s A11A:c|jd}t|trg}g}|jD]I}t |}|j r|j t|9|j |Ktt|}|r"|ttt|k7s|rtdt |d|Dcgc]}t ||c}St|tr|D cgc] } t | c} Stdt |dt|jdcc}wcc} w)z:Extract contiguous YOLO class names from parsed YAML data.namesz)Unsupported 'names' mapping in data file z,: expected integer keys 0..N-1 with no gaps.z)Expected 'names' to be a list or dict in r.r/)getr3r4keysr6isdigitappendr(sortedsetr!rangelenr5r7r) r: data_filer= numeric_keysnon_numeric_keyskeykey_strunique_sorted_keysidxnames r$#_extract_yolo_class_names_from_datarNzs2 HHW E%"$ &(::< -C#hG ##CL1 '',  -$C $56!%74cJ\F]@^;_%_cs;y>$$PR ,>>CE#J>>%&+,dD ,, @Y@RRXY]^cYdYmYmXnnop qq?,s EE cjt|ttfrt|dk7rt dt |d t |d}t |d}|dks|d vrt dt |d|d ||fS#ttf$r!}t dt |d|d|d}~wwxYw) z7Validate and normalize a YOLO pose ``kpt_shape`` entry.YOLO pose data file z2 must define kpt_shape as [num_keypoints, 2_or_3].rz has invalid kpt_shape=z; expected integer values.N)rPz,; expected [positive_num_keypoints, 2_or_3].)r3r!tuplerEr5r6r( TypeError) raw_kpt_shaperF num_keypointsr)excs r$_validate_yolo_kpt_shaperYs mdE] 3s=7IQ7N/I/AAstuuM!,- =+, \7"3y>"44KMK\]9 9   , && z ""3y>"44KMK\\v w  sBB2B--B2c|jd}d}t|tr$|r"|jd|jd}nt|tr|}|t |Dcgc]}d| c}St|trt ||k7rt d|d|d|Dcgc] }t|c}Scc}wcc}w) z>Extract YOLO keypoint names or synthesize stable placeholders. kpt_namesNr0 keypoint_z?YOLO pose kpt_names length must match kpt_shape keypoint count r.r/)r>r3r4r!rDrEr5r6)r:rW raw_kpt_namesr&rLrMs r$_extract_yolo_keypoint_namesr_sHH[)MN-&=&**1m.?.?.DE M4 (&-2=-ABc)C5!BB nd +s>/Bm/SMm_\bcqbttu v  #1 1$CI 11 C 2s ) C,Ccp|jd}|gSt|trt||k7rt d|d |Dcgc] }t |}}t|tt|k7rt d|dz d|Scc}w#t tf$r}t d|d}~wwxYw) z9Extract and validate optional YOLO ``flip_idx`` metadata.r'Nz YOLO pose flip_idx must contain z integer indexes.z0YOLO pose flip_idx must contain integer indexes.z/YOLO pose flip_idx must be a permutation of 0..rRr/) r>r3r!rEr5r(rUrBrD)r:rW raw_flip_idxrLr'rXs r$_extract_yolo_flip_idxrbs88J'L lD )S->--O;M?J[\]]V(45CH55h4m 455J=[\K\J]]^_`` O 6 z "VKLRUUVs*BBBBB5$ B00B5cg}t}t|D]b\}}||vs ||vs||k(r|j|%|t|ks4|||k(s=|j ||g|j ||hd|S)aKConvert a YOLO flip_idx permutation to flat swap pairs. Args: flip_idx: Full permutation where ``flip_idx[i]`` is the horizontal mirror of joint ``i``. Returns: Flat list ``[a0, b0, a1, b1, ...]`` of left/right joint index pairs. Example: >>> _flip_idx_to_pairs([0, 2, 1]) [1, 2] )rC enumerateaddrEextendupdate)r'pairsseenrL mirror_idxs r$_flip_idx_to_pairsrksEUD$X.+Z $;*,z0A HHSM  H %(:*>#*E LL#z* + KKj) * + Lr#ctjddt|jj jdS)z0Normalize a keypoint name for symmetry matching.z [^a-z0-9]+_)resubr6striplower)rMs r$_normalize_keypoint_namerrs4 66-c$ioo&7&=&=&? @ F Fs KKr#c|jd}|jd}|jd}||zdk7ry|Dcgc]}|dk(rdn|dk(rdn|}}dj|Scc}w)zAReturn the left/right counterpart for a normalized keypoint name.rmleftrightrRN)splitcountjoin)normalized_nametokens left_count right_counttoken mirror_tokenss r$_mirror_keypoint_namers|  " "3 'Ff%J,,w'KK1$hno_dWu?OVUZZoMo 88M ""psA+c(|Dcgc] }t|}}tt|t|k7rgSt|Dcic]\}}|| }}}g}t}t|D]m\}}||vr t |}||j |} | | |k(s| |vr6t || |k7rH|j || g|j|| ho|s%|r#tjdt||dd|Scc}wcc}}w)z@Infer horizontal-flip swap pairs from left/right keypoint names.NzInferred 0 flip pairs from %d keypoint names %s. Automatic horizontal-flip slot swapping is disabled for this category. Provide keypoint_flip_pairs explicitly if your keypoints have left/right symmetry.) rrrErCrdrr>rfrgloggerinfo) r&rMnormalized_namesrL index_by_namerhriry mirror_namerjs r$%_infer_keypoint_flip_pairs_from_namesrs=CQR406RR 3 !S)9%:: 09:J0KL93T3YLMLEUD )*: ; '_ $; +O<   "&&{3  s!2jD6H  !1*!= >/ Q  c:&' S*%& ' ^  a   2A    L9SMs D  Dc|sgSt|dk(rt|dS|dtfd|ddDr tSgS)zDReturn one global flip-pair list when all keypoint categories agree.rRrc3(K|] }|k( yw)Nr").0rhfirsts r$ z6_merge_category_keypoint_flip_pairs..s :e5E> :sN)rEr!all)category_pairsrs @r$#_merge_category_keypoint_flip_pairsrsV   >aN1%&& 1 E :~ab'9 ::E{ Ir#c|jd5}tj|}dddtts"t dt |jd|S#1swY=xYw)aJLoad a COCO annotation JSON file. Args: annotation_path: Path to a COCO annotation JSON file. Returns: Parsed COCO annotation mapping. Raises: ValueError: If the JSON root is not an object. OSError: If the file cannot be read. Example: >>> import tempfile >>> path = Path(tempfile.mkdtemp()) / "annotations.json" >>> _ = path.write_text('{"images": [], "annotations": [], "categories": []}', encoding="utf-8") >>> sorted(_load_coco_annotation(path)) ['annotations', 'categories', 'images'] r+r,Nz3Expected COCO annotation root to be an object, got r/)r1jsonloadr3r4r5r7r)annotation_pathr9r:s r$_load_coco_annotationr sj(   w  /4yy dD !NtTXzObObNccdeff K s A%%A.c:t|tr|s tdg}|D]j}t|ts"tdt |j dd|vr tdd|vrtd|dd|j |lt|d S) aValidate and sort COCO categories by category id. Args: categories: Raw ``categories`` value from a COCO annotation file. Returns: Category dictionaries sorted by ``id``. Raises: ValueError: If categories are missing or malformed. Example: >>> _validate_categories([{"id": 2, "name": "b"}, {"id": 1, "name": "a"}]) [{'id': 1, 'name': 'a'}, {'id': 2, 'name': 'b'}] zCExpected COCO annotations to contain a non-empty 'categories' list.z1Expected each COCO category to be an object, got r/idz5Expected each COCO category to contain an 'id' field.rMzExpected COCO category_id z to contain a 'name' field.ct|dS)Nr)r()items r$z&_validate_categories..Wsc$t*or#)rI)r3r!r5r4r7rrArB) categories validatedcategorys r$_validate_categoriesr;s j$ 'z^__&(I#(D)PQUV^Q_QhQhPiijkl l x TU U  !9(4.9KKfgh h"# )!= >>r#ct|ts td|D]4}t|trtdt |j d|S)agValidate COCO annotations container type. Args: annotations: Raw ``annotations`` value from a COCO annotation file. Returns: COCO annotation dictionaries. Raises: ValueError: If annotations are not a list of objects. Example: >>> _validate_annotations([{"category_id": 1}]) [{'category_id': 1}] z;Expected COCO annotations to contain an 'annotations' list.z3Expected each COCO annotation to be an object, got r/)r3r!r5r4r7r)r annotations r$_validate_annotationsrZsa k4 (VWW!q *d+RSWXbScSlSlRmmnop pq r#c 2d}|D]}t|jdd|k7r"|jd}||gk(r;t|tsLt |dzdk7rt d|dt |dt |t |dz}|S) aInfer keypoint count for one category from annotation vectors. Args: annotations: COCO annotation dictionaries. category_id: Category id whose annotations should be inspected. Returns: Maximum keypoint vector length found for the category. Raises: ValueError: If keypoint annotation length is not divisible by three. Example: >>> anns = [{"category_id": 1, "keypoints": [1, 2, 2, 3, 4, 2]}] >>> _keypoint_count_from_annotations(anns, 1) 2 r category_id keypointsrSz COCO annotation for category_id z has zL keypoint values; expected a flat [x, y, v] list with length divisible by 3.)r(r>r3r!rEr5max)rrkeypoint_countr raw_keypointss r$ _keypoint_count_from_annotationsrrs$N! F z~~mR0 1[ @ "{3  MR$7 -.  }  !Q &2;/s=GYFZ[MM ^S-?1-DE F r#g?)keypoint_oks_sigmact|}t|}t|d}t|j dg}g}g}g}|D]} t | d} |j t| d| j d} t| tr7| r5|j t| |j t| t|| } |j | | dkDs|j gtd|Dstd|d t|d } t!|||g| zt#| S) aZInfer a keypoint schema from a COCO annotation JSON file. Args: annotation_path: Path to a COCO annotation JSON file. keypoint_oks_sigma: Default OKS sigma to repeat for the largest keypoint class. Returns: Category-aligned class names, ``num_keypoints_per_class``, and OKS sigmas. Raises: FileNotFoundError: If the annotation file does not exist. ValueError: If the annotation file has no categories, no keypoint metadata, malformed COCO fields, or malformed keypoint vectors. KeyError: If required COCO keys are missing. rrrrMrrc3&K|] }|dkD yw)rNr")rrws r$rz-infer_coco_keypoint_schema..s>Uuqy>szCOCO annotation file 'za' has no keypoint metadata. Expected category 'keypoints' entries or annotation keypoint vectors.)default)rrrr)rrrrr>r(rAr6r3r!rErranyr5rr r)rrpathr:rrrrcategory_flip_pairsrrcategory_keypointsinferred_count max_keypointss r$r r s_(  D  &D%d<&89J'(CDKK)++- /(4.) 3x/01%\\+6 ($ /4F # * *3/A+B C  & &'LM_'` a=k;WN # * *> :!#**2. / >&=> >$TF+T T  /;M  7/0=@?@ST  r#c *t|}t|}t||}d|vrtdt |dt |d|\}}t ||}t||}t||gt|z|g|z|||t|S)aInfer a keypoint schema from an Ultralytics YOLO pose YAML file. Args: data_file: Path to a YOLO ``data.yaml`` or ``data.yml`` file. keypoint_oks_sigma: Default OKS sigma to repeat for the global keypoint count. Returns: Class-aligned keypoint counts plus optional YOLO keypoint names and flip indexes. Raises: FileNotFoundError: If the YAML file does not exist. ValueError: If required YOLO pose fields are missing or malformed. Example: >>> import tempfile >>> path = Path(tempfile.mkdtemp()) / "data.yaml" >>> _ = path.write_text("names: [person]\nkpt_shape: [1, 3]\n", encoding="utf-8") >>> infer_yolo_keypoint_schema(path).num_keypoints_per_class [1] kpt_shaperQz( is missing required kpt_shape metadata.)rrrr&r'r)r) rr;rNr5r6rYr_rbr rErk) rFrrr:rrWr)r&r's r$rrs2  ?D d #D5dDAK$/D }>> active_keypoint_counts([0, 17, 25]) [17, 25] rr")rrws r$r r s 7 De%!)E DD Ds )r8rreturndict[str, Any])r:rrFrrr)rVrrFrrztuple[int, int])r:rrWr(rr)r:rrWr(rr)r'rrr)rMrrr6)ryr6rz str | None)r&z list[Any]rr)rzlist[list[int]]rr)rrrr)rrrlist[dict[str, Any]])rrrr)rrrr(rr()r str | Pathrfloatrr )rFrrrrr )rrrr)$r __future__rrrn dataclassesrrpathlibrtypingrrfdetr.utilities.loggerrr__all__r r r r;rNrYr_rbrkrrrrrrrrrr rr r"r#r$rs ;" (.   $d#AA$A4 $d#AA$A@$&88:r2'&2$  2L #B 6?>0!N!$555 5v!$))) )XEr#