| import comfy
|
| import re
|
| from impact import utils
|
|
|
|
|
| hf_transformer_model_urls = [
|
| "rizvandwiki/gender-classification-2",
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| "NTQAI/pedestrian_gender_recognition",
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| "Leilab/gender_class",
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| "ProjectPersonal/GenderClassifier",
|
| "crangana/trained-gender",
|
| "cledoux42/GenderNew_v002",
|
| "ivensamdh/genderage2"
|
| ]
|
|
|
|
|
| class HF_TransformersClassifierProvider:
|
| @classmethod
|
| def INPUT_TYPES(s):
|
| global hf_transformer_model_urls
|
| return {"required": {
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| "preset_repo_id": (hf_transformer_model_urls + ['Manual repo id'],),
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| "manual_repo_id": ("STRING", {"multiline": False}),
|
| "device_mode": (["AUTO", "Prefer GPU", "CPU"],),
|
| },
|
| }
|
|
|
| RETURN_TYPES = ("TRANSFORMERS_CLASSIFIER",)
|
| FUNCTION = "doit"
|
|
|
| CATEGORY = "ImpactPack/HuggingFace"
|
|
|
| def doit(self, preset_repo_id, manual_repo_id, device_mode):
|
| from transformers import pipeline
|
|
|
| if preset_repo_id == 'Manual repo id':
|
| url = manual_repo_id
|
| else:
|
| url = preset_repo_id
|
|
|
| if device_mode != 'CPU':
|
| device = comfy.model_management.get_torch_device()
|
| else:
|
| device = "cpu"
|
|
|
| classifier = pipeline('image-classification', model=url, device=device)
|
|
|
| return (classifier,)
|
|
|
|
|
| preset_classify_expr = [
|
| '#Female > #Male',
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| '#Female < #Male',
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| 'female > 0.5',
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| 'male > 0.5',
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| 'Age16to25 > 0.1',
|
| 'Age50to69 > 0.1',
|
| ]
|
|
|
| symbolic_label_map = {
|
| '#Female': {'female', 'Female', 'Human Female', 'woman', 'women', 'girl'},
|
| '#Male': {'male', 'Male', 'Human Male', 'man', 'men', 'boy'}
|
| }
|
|
|
| def is_numeric_string(input_str):
|
| return re.match(r'^-?\d+(\.\d+)?$', input_str) is not None
|
|
|
|
|
| classify_expr_pattern = r'([^><= ]+)\s*(>|<|>=|<=|=)\s*([^><= ]+)'
|
|
|
|
|
| class SEGS_Classify:
|
| @classmethod
|
| def INPUT_TYPES(s):
|
| global preset_classify_expr
|
| return {"required": {
|
| "classifier": ("TRANSFORMERS_CLASSIFIER",),
|
| "segs": ("SEGS",),
|
| "preset_expr": (preset_classify_expr + ['Manual expr'],),
|
| "manual_expr": ("STRING", {"multiline": False}),
|
| },
|
| "optional": {
|
| "ref_image_opt": ("IMAGE", ),
|
| }
|
| }
|
|
|
| RETURN_TYPES = ("SEGS", "SEGS", "STRING")
|
| RETURN_NAMES = ("filtered_SEGS", "remained_SEGS", "detected_labels")
|
| OUTPUT_IS_LIST = (False, False, True)
|
|
|
| FUNCTION = "doit"
|
|
|
| CATEGORY = "ImpactPack/HuggingFace"
|
|
|
| @staticmethod
|
| def lookup_classified_label_score(score_infos, label):
|
| global symbolic_label_map
|
|
|
| if label.startswith('#'):
|
| if label not in symbolic_label_map:
|
| return None
|
| else:
|
| label = symbolic_label_map[label]
|
| else:
|
| label = {label}
|
|
|
| for x in score_infos:
|
| if x['label'] in label:
|
| return x['score']
|
|
|
| return None
|
|
|
| def doit(self, classifier, segs, preset_expr, manual_expr, ref_image_opt=None):
|
| if preset_expr == 'Manual expr':
|
| expr_str = manual_expr
|
| else:
|
| expr_str = preset_expr
|
|
|
| match = re.match(classify_expr_pattern, expr_str)
|
|
|
| if match is None:
|
| return (segs[0], []), segs, []
|
|
|
| a = match.group(1)
|
| op = match.group(2)
|
| b = match.group(3)
|
|
|
| a_is_lab = not is_numeric_string(a)
|
| b_is_lab = not is_numeric_string(b)
|
|
|
| classified = []
|
| remained_SEGS = []
|
| provided_labels = set()
|
|
|
| for seg in segs[1]:
|
| cropped_image = None
|
|
|
| if seg.cropped_image is not None:
|
| cropped_image = seg.cropped_image
|
| elif ref_image_opt is not None:
|
|
|
| cropped_image = utils.crop_image(ref_image_opt, seg.crop_region)
|
|
|
| if cropped_image is not None:
|
| cropped_image = utils.to_pil(cropped_image)
|
| res = classifier(cropped_image)
|
| classified.append((seg, res))
|
|
|
| for x in res:
|
| provided_labels.add(x['label'])
|
| else:
|
| remained_SEGS.append(seg)
|
|
|
| filtered_SEGS = []
|
| for seg, res in classified:
|
| if a_is_lab:
|
| avalue = SEGS_Classify.lookup_classified_label_score(res, a)
|
| else:
|
| avalue = a
|
|
|
| if b_is_lab:
|
| bvalue = SEGS_Classify.lookup_classified_label_score(res, b)
|
| else:
|
| bvalue = b
|
|
|
| if avalue is None or bvalue is None:
|
| remained_SEGS.append(seg)
|
| continue
|
|
|
| avalue = float(avalue)
|
| bvalue = float(bvalue)
|
|
|
| if op == '>':
|
| cond = avalue > bvalue
|
| elif op == '<':
|
| cond = avalue < bvalue
|
| elif op == '>=':
|
| cond = avalue >= bvalue
|
| elif op == '<=':
|
| cond = avalue <= bvalue
|
| else:
|
| cond = avalue == bvalue
|
|
|
| if cond:
|
| filtered_SEGS.append(seg)
|
| else:
|
| remained_SEGS.append(seg)
|
|
|
| return (segs[0], filtered_SEGS), (segs[0], remained_SEGS), list(provided_labels)
|
|
|
