这个皮肤科人工智能API允许您通过面部图像高准确性评估皮肤状况。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、斑点、痤疮、毛孔扩大、水分水平以及与护肤相关的其他指标
该API以清晰且易于集成的格式生成结构化结果,包括热图、严重程度指数和受影响面部区域的百分比。这使得对皮肤状况的详细理解成为可能,并进行定期跟踪以评估治疗的进展或效果
它提供客观的自动化分析,消除了主观变异性,并提供关于皮肤健康的可量化数据
此外,API还包含面部分割选项,以识别特定区域(额头、脸颊、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
总之,这个API将简单的照片转化为详细的皮肤科分析,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/9343/dermatology+ai+api/16880/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。
皮肤分析端点返回对皮肤状况的全面评估,包括瑕疵、皱纹、斑点、痤疮、毛孔扩大和水分水平等指标。它还提供热图和严重程度指数等视觉输出
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”每个字段量化特定的皮肤状况,有助于详细分析
响应数据采用JSON格式结构化,包括整体皮肤评估、按面部区域的局部分析以及热图等视觉输出。这种组织方式便于与应用程序的轻松集成
用户可以通过指定参数来自定义他们的请求,例如皮肤类型、光照条件和要分析的面部区域(例如额头、脸颊)这允许根据个人需求进行量身定制的评估
数据准确性通过先进的计算机视觉和人工智能算法得到维护,这些算法在多样化的数据集上持续训练 定期更新和质量检查确保分析结果的可靠性
典型的用例包括皮肤评估 个性化护肤建议 跟踪治疗进展 和增强护肤应用中的用户参与 数据支持对皮肤健康的明智决策
用户可以利用返回的数据识别特定的皮肤问题,监测变化,并定制护肤方案。结构化格式便于集成到健康应用或平台中,以便获取用户反馈
标准数据模式包括不同皮肤状况的不同严重程度水平,并且有清晰的百分比指示受影响区域 用户可以期待在相似皮肤类型和状况之间的指标一致性,有助于比较分析
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