该皮肤面部分析 API 允许您通过面部图像高精度地评估皮肤状况。当您上传一张照片时,系统应用计算机视觉和人工智能算法来识别瑕疵、皱纹、 blemishes、痤疮、毛孔增大、湿度水平以及与皮肤护理相关的其他指标
该 API 生成具有清晰且易于集成格式的结构化结果,包括热图、严重程度指数和受影响面部区域的百分比。这使得对皮肤状况有详细的了解,并进行定期跟进以评估治疗的进展或有效性
它提供客观的自动化分析,消除了主观变异性,并提供可量化的皮肤健康数据
此外,该 API 包括面部分割选项,以识别特定区域(额头、面颊、鼻子、下巴),提供局部诊断。它还支持参数自定义,以适应不同的皮肤类型和光照环境
简而言之,该 API 将简单的照片转变为详细的皮肤科分析,帮助提供有根据的建议,改善用户关系,并通过准确的皮肤数据创造附加价值
皮肤分析 - 端点功能
| 对象 | 描述 |
|---|---|
请求体 |
[必需] Json |
{"log_id":"1776444169,7f33f409-61d2-4af7-a38b-a5a81a30a1f7","request_id":"1776444169,0f55ec05-37f1-43c6-a510-ef24dd51df0c","timestamp":"2026-04-17T16:42:49.350404","analysis_type":"comprehensive","focus_areas":["acne","wrinkles","pores"],"image_url":"https://a.files.bbci.co.uk/worldservice/live/assets/images/2016/04/21/160421151857_acne_624x351_thinkstock_nocredit.jpg","image_info":{"original_size":{"width":512,"height":288},"processed_size":{"width":512,"height":288},"bbox_format":"x1,y1,x2,y2","coordinate_system":"pixels"},"quality":{"blur_score":0.824,"exposure_score":0.16,"contrast_score":0.294,"overall_quality":"poor","quality_score":0.333,"warnings":["High blur detected - texture-dependent analysis may be unreliable","Consider retaking photo with better focus","Underexposed image - may affect lesion detection"],"scales":{"blur_score":"0=sharp, 1=blurry","exposure_score":"0=dark, 1=overexposed","contrast_score":"0=low, 1=high","quality_score":"0=poor, 1=excellent"}},"face_regions":{"left_cheek":[115,86,201,173],"right_cheek":[288,86,375,173],"chin":[180,173,310,260],"forehead":[180,0,310,86]},"lesions":{"count":0,"severity":"none","severity_percentage":0.0,"confidence":0.95,"detection_status":"not_present"},"pores":{"left_cheek":{"count":1,"density":1.34,"density_units":"pores/10k_pixels","severity":"low","confidence":0.600133654103181,"filtering_applied":"morphological + circularity"},"right_cheek":{"count":7,"density":9.25,"density_units":"pores/10k_pixels","severity":"low","confidence":0.6009248249438499,"filtering_applied":"morphological + circularity"},"chin":{"count":2,"density":1.77,"density_units":"pores/10k_pixels","severity":"low","confidence":0.6001768346595933,"filtering_applied":"morphological + circularity"},"forehead":{"count":1,"density":0.89,"density_units":"pores/10k_pixels","severity":"low","confidence":0.6000894454382826,"filtering_applied":"morphological + circularity"}},"wrinkles":{"left_cheek":{"wrinkle_score":0.546,"severity":"moderate","confidence":0.8638320685224598},"right_cheek":{"wrinkle_score":0.37,"severity":"moderate","confidence":0.8111346385265074},"chin":{"wrinkle_score":0.444,"severity":"moderate","confidence":0.8332612127886169},"forehead":{"wrinkle_score":0.585,"severity":"moderate","confidence":0.8756066997274834}},"pigmentation":{"left_cheek":{"spot_count":1,"density":1.34,"density_units":"spots/10k_pixels","severity":"none","confidence":0.600133654103181,"filtering_applied":"morphological + circularity","detection_type":"defined_spots_only"},"right_cheek":{"spot_count":1,"density":1.32,"density_units":"spots/10k_pixels","severity":"none","confidence":0.6001321178491213,"filtering_applied":"morphological + circularity","detection_type":"defined_spots_only"},"chin":{"spot_count":0,"density":0.0,"density_units":"spots/10k_pixels","severity":"none","confidence":0.6,"filtering_applied":"morphological + circularity","detection_type":"defined_spots_only"},"forehead":{"spot_count":1,"density":0.89,"density_units":"spots/10k_pixels","severity":"none","confidence":0.6000894454382826,"filtering_applied":"morphological + circularity","detection_type":"defined_spots_only"}},"skin_type":{"label":"mixed","confidence":0.8,"texture_score":17442.5879},"severity":{"overall":"mild","confidence":0.703,"component_scores":{"inflammatory_acne":0,"pores":0.2,"wrinkles":0.7,"pigmentation":0.0},"total_weighted_score":0.9,"weighting_system":"mature_skin_optimized","explanation":"Wrinkles and pigmentation weighted higher for mature skin analysis","criteria":{"inflammatory_acne":">5 lesions or >2% area","pores":">300 pores/10k_pixels in any region","wrinkles":">0.6 wrinkle_score in any region","pigmentation":">500 spots/10k_pixels in any region","thresholds":{"mild":"0-2 lesions, <100 pores/10k_pixels, <0.3 wrinkle_score","moderate":"3-5 lesions, 100-300 pores/10k_pixels, 0.3-0.6 wrinkle_score","severe":">5 lesions, >300 pores/10k_pixels, >0.6 wrinkle_score"}}}}
curl --location --request POST 'https://zylalabs.com/api/9328/skin+face+analyzer+api/16871/skin+analysis' --header 'Authorization: Bearer YOUR_API_KEY'
--data-raw '{
"analysis_type": "comprehensive",
"image_url": "https://a.files.bbci.co.uk/worldservice/live/assets/images/2016/04/21/160421151857_acne_624x351_thinkstock_nocredit.jpg",
"focus_areas": ["acne", "wrinkles", "pores"]
}'
| 标头 | 描述 |
|---|---|
授权
|
[必需] 应为 Bearer access_key. 订阅后,请查看上方的"您的 API 访问密钥"。 |
无长期承诺。随时升级、降级或取消。 免费试用包括最多 50 个请求。
皮肤分析端点返回有关皮肤状况的详细指标,包括瑕疵、皱纹、斑点、痤疮、毛孔 enlarged 以及水分水平。它提供结构化的结果,如热图、严重程度指数和受影响区域的百分比
响应数据中的关键字段包括“瑕疵”、“皱纹”、“ blemishes”、“水分水平”和“受影响区域百分比”。每个字段提供有关皮肤状态的特定见解,允许进行针对性的分析
响应数据以结构化的JSON格式组织,其中包含整体肤质分析、局部区域(额头、脸颊等)和热图等视觉表示。这种结构便于与应用程序的集成
用户可以通过指定图像质量、肤色类型和光照条件等参数来定制他们的请求。这使得可以根据个别用户的需求和环境因素进行量身定制的分析
数据准确性通过先进的计算机视觉和人工智能算法来维护,这些算法分析面部图像。对多样化数据集的持续更新和训练确保了可靠和准确的皮肤状况评估
用户可以通过指定皮肤类型和光照条件等参数来自定义他们的请求。这允许API根据不同的皮肤特征量身定制分析,提高结果的准确性
典型的使用案例包括个性化护肤建议 随时间跟踪皮肤健康 并协助皮肤科医生诊断疾病 该API还可以增强用户在美容应用中的参与度
用户可以通过将返回的数据集成到护肤应用程序中为皮肤科医生生成报告或根据严重程度指数和受影响区域百分比创建个性化护肤计划
用户可以期待数据模式突出常见皮肤问题,例如油性肤质中瑕疵的更高比例或随着年龄增长而增加的皱纹 这些模式有助于识别趋势并量身定制护肤解决方案
质量检查包括对临床数据的算法验证 定期绩效评估和用户反馈机制以确保皮肤分析结果的准确性和可靠性
皮肤分析端点可以检测各种皮肤状况,包括瑕疵、皱纹、色斑、痤疮、毛孔扩大和水分水平。这一全面的分析帮助用户了解他们的皮肤健康并识别需要关注的领域
该响应包括对特定面部区域的本地化分析,如额头、脸颊、鼻子和下巴。每个区域单独评估,提供针对皮肤状况的有针对性的见解,并允许更精确的建议
分析结果包括诸如热图等视觉表现,突显受影响区域,以及量化皮肤问题严重程度的指标。这些视觉效果增强了理解并促进了更好的护肤决策
用户可以通过指定皮肤类型(油性、干性、混合性)和光照条件(自然光、人造光)来自定义他们的分析。这确保分析与个体皮肤特征和环境因素相匹配
严重性指数指示皮肤状况的强度,值越高代表问题越严重。用户可以利用这些指数来优先考虑治疗区域并跟踪随着时间的改善
如果分析返回的数据不完整,用户应检查图像质量并确保其满足API的要求。调整照明或肤色等参数可能也会改善后续请求的结果
API支持常见的图像格式,如JPEG和PNG进行上传 确保图像为这些格式之一对成功分析至关重要
该API旨在通过利用多样的训练数据集来适应各种肤色。这确保了对不同肌肤类型的准确分析,提高了所有用户结果的可靠性
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