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047076d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 | import gradio as gr
from PIL import Image
from typing import Tuple, Optional
# -------------------------------------------------------------------
# A360 Croptool – Model-Agnostic Cropping Test Harness
#
# NOTE:
# - This version only stubs the model calls.
# - I will later plug in:
# • RetinaFace / InsightFace (face + landmarks)
# • YOLOv8/YOLOv9 (face/body/region detection)
# • Face Alignment nets
# • SAM / SAM-HQ
# • BiSeNet (face parsing)
# • CLIPSeg (text-guided cropping)
# -------------------------------------------------------------------
FACE_MODEL_CHOICES = [
"RetinaFace (InsightFace)",
"InsightFace 2D Landmarks (2d106)",
"Face-Alignment (1adrianb)",
]
BODY_MODEL_CHOICES = [
"YOLOv8 Body Detector",
"YOLOv9 Body / Part Detector",
"Human Pose (OpenPose-style)",
]
SEGMENTATION_MODEL_CHOICES = [
"SAM / SAM-HQ",
"BiSeNet Face Parsing",
"CLIPSeg (text-guided)",
]
CROP_TARGET_CHOICES = [
"Full Face",
"Eyes / Upper Face",
"Lips / Lower Face",
"Jawline / Chin",
"Neck",
"Chest / Breasts",
"Abdomen",
"Waist / Hips",
"Arms",
"Thighs / Legs",
"Custom Region",
]
def stub_crop(
image: Optional[Image.Image],
crop_target: str,
face_model: str,
body_model: str,
seg_model: str,
text_prompt: str,
) -> Tuple[Image.Image, str]:
"""
Placeholder cropping callback.
For now, simply returns the original image and a text summary of
the options the user selected. I'll replace this with real model
calls (RetinaFace / YOLO / SAM / CLIPSeg) later.
"""
if image is None:
# Gradio will show this as an error toast
raise gr.Error("Please upload an image first.")
summary_lines = [
"Cropping request received:",
f"• Target region: {crop_target}",
f"• Face model: {face_model or 'None selected'}",
f"• Body model: {body_model or 'None selected'}",
f"• Segmentation model: {seg_model or 'None selected'}",
]
if text_prompt.strip():
summary_lines.append(f"• Text prompt (for CLIPSeg / SAM): {text_prompt.strip()}")
summary_lines.append("")
summary_lines.append("NOTE: This is a stub implementation. "
"Model hooks are ready; image is returned unmodified for now.")
return image, "\n".join(summary_lines)
def create_app() -> gr.Blocks:
with gr.Blocks(theme="gradio/soft", css="""
.a360-header { font-size: 1.8rem; font-weight: 700; }
.a360-subtitle { opacity: 0.8; }
""") as demo:
gr.Markdown(
"<div class='a360-header'>A360 Croptool 🧬</div>"
"<div class='a360-subtitle'>"
"Test and prototype clinical cropping models for faces, bodies, and regions of interest."
"</div>"
)
with gr.Row():
with gr.Column(scale=1):
input_image = gr.Image(
label="Input Image",
type="pil",
height=480,
elem_id="input_image",
)
crop_target = gr.Dropdown(
CROP_TARGET_CHOICES,
value="Full Face",
label="Target Region",
info="What do you want to crop to?",
)
gr.Markdown("### Model Stack (configuration only – models wired later)")
face_model = gr.Radio(
FACE_MODEL_CHOICES,
value="RetinaFace (InsightFace)",
label="Face / Landmark Model",
)
body_model = gr.Radio(
BODY_MODEL_CHOICES,
value="YOLOv8 Body Detector",
label="Body / Region Model",
)
seg_model = gr.Radio(
SEGMENTATION_MODEL_CHOICES,
value="SAM / SAM-HQ",
label="Segmentation / Mask Model",
)
text_prompt = gr.Textbox(
label="Optional Text Prompt (for CLIPSeg / SAM)",
placeholder="e.g., 'crop to lips', 'crop to abdomen', 'crop to jawline'",
lines=2,
)
run_btn = gr.Button("Run Cropping Prototype", variant="primary")
with gr.Column(scale=1):
output_image = gr.Image(
label="Cropped Output (stub – currently same as input)",
height=480,
)
debug_text = gr.Markdown(label="Cropping Summary")
run_btn.click(
stub_crop,
inputs=[input_image, crop_target, face_model, body_model, seg_model, text_prompt],
outputs=[output_image, debug_text],
)
return demo
app = create_app()
if __name__ == "__main__":
app.launch()
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