Open Eyes Editor

Assumption: The most common intent for 'photo editor open closed eyes' is to automatically open eyes in portraits where the subject blinked or eyes were closed, primarily for headshots and social images. The tool provides AI-assisted eyelid adjustment that preserves realism and natural lighting. It is non-destructive and works on a single image or batch inputs when available.\n\nPurpose & User Intent: Solves automatic eyelid-position adjustment in portraits to render open eyes without manual retouching. Target users include portrait photographers, studio editors, and ecommerce image teams; applicable when a subject's eyes are closed or blinking in a shot, or when a natural open-eyed look is required for a headshot or product portrait.\n\nCore Logic & Features: Core capabilities include AI-based facial landmark detection, eyelid-position estimation, and non-destructive pixel edits that preserve skin tone and lighting. Required features: face detection, landmark extraction, eyelid translation, quality-preserving smoothing, and lossless output preservation. Optional/advanced features: batch processing, per-face parameter tuning, and post-edit color matching. Distinguishing factors: focuses explicitly on eyelid adjustments for realism, supports high-resolution outputs, and preserves EXIF data when possible.\n\nInputs & Outputs: Inputs include: image_input (image file, required), target_eye_state (enum: open, natural, slightly_open; required), edit_intensity (0-100; optional), output_format (jpeg/png; optional), output_resolution (pixels, optional). Outputs: edited_image (image file, same or higher resolution), processing_confidence (0-1; optional), log_summary (string; optional). Validation ensures image is within supported formats, target_eye_state is defined, and intensity is 0–100.\n\nAlgorithms & Calculations: Uses CNN-based facial landmark detection (e.g., eye landmarks) with geometric eyelid repositioning. Applies a constrained warp to eyelid region, followed by local color/texture smoothing to preserve realism. Confidence thresholds trigger fallback to original image if detection is unreliable.\n\nError & Edge Cases: If no face or insufficient eye visibility detected, returns original image with low confidence flag. If multiple faces detected, edits are applied to all detected faces; if faces exceed training expectations, a per-face report is provided. If output_format is unsupported, defaults to PNG.\n\nIndustry/Region & Localization: Outputs use standard sRGB color space; supports common international image formats; respects locale-based metadata handling and preserves basic EXIF when possible.\n\nAssumptions & Exclusions: Not intended for identity alteration or relabeling; not a full-face replacement or aesthetic rebranding. Explicitly excludes synthetic facial generation; results depend on input image quality and lighting.