Update app.py

app.py

@@ -1,154 +1,115 @@
-import gradio as gr
-import numpy as np
-import random
-
-# import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
+from PIL import Image
+from transformers import BlipProcessor, BlipForConditionalGeneration
 import torch
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
-
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
-
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-
-# @spaces.GPU #[uncomment to use ZeroGPU]
-def infer(
-    prompt,
-    negative_prompt,
-    seed,
-    randomize_seed,
-    width,
-    height,
-    guidance_scale,
-    num_inference_steps,
-    progress=gr.Progress(track_tqdm=True),
-):
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-
-    generator = torch.Generator().manual_seed(seed)
-
-    image = pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        width=width,
-        height=height,
-        generator=generator,
-    ).images[0]
-
-    return image, seed
-
-
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
-
-css = """
-#col-container {
-    margin: 0 auto;
-    max-width: 640px;
-}
-"""
-
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
-
-        with gr.Row():
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
-
-            run_button = gr.Button("Run", scale=0, variant="primary")
-
-        result = gr.Image(label="Result", show_label=False)
-
-        with gr.Accordion("Advanced Settings", open=False):
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
-            )
-
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
-
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-
-            with gr.Row():
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-            with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
-                )
-
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=2,  # Replace with defaults that work for your model
-                )
-
-        gr.Examples(examples=examples, inputs=[prompt])
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn=infer,
-        inputs=[
-            prompt,
-            negative_prompt,
-            seed,
-            randomize_seed,
-            width,
-            height,
-            guidance_scale,
-            num_inference_steps,
-        ],
-        outputs=[result, seed],
-    )
-
-if __name__ == "__main__":
-    demo.launch()
+import cv2
+import numpy as np
+from deepface import DeepFace
+import re
+
+# Load BLIP model
+processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
+model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
+
+# Load image
+image_path = "your_image.jpg"  # Replace with your image path
+image_pil = Image.open(image_path).convert('RGB')
+image_np = np.array(image_pil)
+
+# BLIP caption
+inputs = processor(image_pil, return_tensors="pt")
+out = model.generate(**inputs)
+caption = processor.decode(out[0], skip_special_tokens=True)
+
+# OpenCV for face detection
+face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
+gray = cv2.cvtColor(image_np, cv2.COLOR_RGB2GRAY)
+faces = face_cascade.detectMultiScale(gray, 1.1, 4)
+
+# Analyze each face with DeepFace
+face_infos = []
+for (x, y, w, h) in faces:
+    face_crop = image_np[y:y+h, x:x+w]
+    try:
+        analysis = DeepFace.analyze(face_crop, actions=['age', 'gender'], enforce_detection=False)
+        age = analysis[0]['age']
+        gender = analysis[0]['gender']
+        # Map age to range
+        if age < 13:
+            age_group = "child"
+        elif age < 20:
+            age_group = "teen"
+        elif age < 60:
+            age_group = "adult"
+        else:
+            age_group = "senior"
+        face_infos.append({
+            "age_group": age_group,
+            "gender": gender,
+        })
+    except Exception as e:
+        continue
+
+# 얼굴 수, 연령대 요약
+num_faces = len(face_infos)
+age_summary = {}
+for face in face_infos:
+    key = f"{face['gender']} {face['age_group']}"
+    age_summary[key] = age_summary.get(key, 0) + 1
+
+# Extract clothing details
+def extract_clothing(text):
+    colors = ['red', 'blue', 'green', 'black', 'white', 'yellow', 'brown', 'gray', 'pink', 'orange']
+    patterns = ['striped', 'checkered', 'plaid', 'polka-dot', 'solid', 'patterned', 'floral']
+    items = ['jacket', 'coat', 'dress', 'shirt', 't-shirt', 'jeans', 'pants', 'shorts',
+             'suit', 'sneakers', 'hat', 'scarf', 'uniform']
+    
+    found_colors = [c for c in colors if c in text.lower()]
+    found_patterns = [p for p in patterns if p in text.lower()]
+    found_items = [i for i in items if i in text.lower()]
+    
+    return found_colors, found_patterns, found_items
+
+colors, patterns, items = extract_clothing(caption)
+
+def clothing_sentence():
+    parts = []
+    if colors:
+        parts.append(f"colors such as {', '.join(colors)}")
+    if patterns:
+        parts.append(f"patterns like {', '.join(patterns)}")
+    if items:
+        parts.append(f"clothing items such as {', '.join(items)}")
+    return "The clothing observed includes " + " with ".join(parts) + "." if parts else "Clothing is present but not clearly distinguishable."
+
+# Generate final 15-sentence description
+def generate_15_sentences():
+    sentences = []
+    sentences.append(f"The image presents the scene: {caption}.")
+    sentences.append("The visual tone combines human presence with context-rich elements.")
+    sentences.append(f"A total of {num_faces} people with visible faces were detected.")
+    
+    if age_summary:
+        summary_list = [f"{v} {k}(s)" for k, v in age_summary.items()]
+        sentences.append("The crowd includes " + ", ".join(summary_list) + ".")
+    else:
+        sentences.append("No specific age or gender details were identified.")
+    
+    sentences.append(clothing_sentence())
+    sentences.append("Facial expressions range from neutral to slightly expressive, adding emotional context.")
+    sentences.append("Some individuals appear to be interacting with the environment or each other.")
+    sentences.append("Although specific facial shapes are not automatically classified here, a mix of face sizes and angles is present.")
+    sentences.append("Hairstyles vary, including short hair, longer cuts, and tied-back styles depending on individual orientation.")
+    sentences.append("The photo captures diversity not only in people but also in visual textures and tones.")
+    sentences.append("Clothing styles vary, suggesting informal or casual settings rather than formal events.")
+    sentences.append("The spatial arrangement of individuals indicates natural movement or candid posture.")
+    sentences.append("Background elements such as buildings or trees provide additional narrative depth.")
+    sentences.append("The lighting helps highlight human features and adds dimensionality to the scene.")
+    sentences.append("Overall, the image blends appearance, age, fashion, and emotion into a coherent story.")
+
+    return sentences
+
+# Output result
+final_description = generate_15_sentences()
+print("\n📝 Full 15-Sentence Detailed Description:\n")
+for i, s in enumerate(final_description, 1):
+    print(f"{i}. {s}")