该皮肤面部数据分析API允许您通过面部图像高精度评估皮肤状况。当您上传照片时,系统应用计算机视觉和人工智能算法来识别瑕疵、皱纹、 blemish(瑕疵)、痤疮、毛孔扩大、湿度水平和其他与护肤相关的指标。
该API生成结构化结果,采用清晰易于集成的格式,包括热图、严重程度指数和受影响面部区域的百分比。这使得对皮肤状况的详细理解成为可能,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,API还包括面部分割选项,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肤质和光照环境。
简而言之,该API将一张简单的照片转化为详细的皮肤病学分析,帮助提供明智的建议,改善用户关系,并通过准确的皮肤数据创造附加值。
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/9339/skin+face+data+analyzer+api/16877/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、毛孔粗大和水分水平。它还提供热图、严重程度指数和受影响区域的百分比,使对皮肤健康的全面理解成为可能
响应数据中的关键字段包括“瑕疵”“皱纹”“缺陷”“水分水平”和“受影响区域百分比”每个字段提供了关于特定皮肤状况的定量见解,从而可以进行有针对性的护肤建议
响应数据采用JSON格式结构化,分为整体皮肤健康指标、按面部区域(额头、脸颊、鼻子、下巴)进行本地化分析以及热图等视觉表现。这种结构便于轻松集成到应用程序中
用户可以通过指定参数如肤色类型、光照条件和特定面部区域进行自定义请求。这种灵活性允许根据个人需求进行量身定制的评估
通过先进的计算机视觉和人工智能算法分析面部图像来保持数据准确性。持续的模型训练和与皮肤病学标准的验证确保可靠的结果,提高分析质量
典型的使用案例包括个性化的护肤建议、随时间跟踪皮肤健康以及评估治疗效果。皮肤科医生和护肤专业人士可以利用这些数据进行明智的决策
用户可以通过解读严重性指数和热图来识别问题区域来利用返回的数据。这些信息可以指导护肤程序、产品选择和针对个人肤质的治疗计划
质量检查包括针对临床皮肤评估和用户反馈循环的算法验证定期更新和性能评估确保API提供准确可靠的皮肤健康洞察
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