Update app.py

app.py

@@ -1,201 +1,48 @@
-import gradio as gr
-import spaces
-from transformers import AutoModel, AutoTokenizer
+from transformers import AutoTokenizer, AutoModel
 from PIL import Image
-import numpy as np
-import os
-import base64
-import io
-import uuid
-import tempfile
-import time
-import shutil
-from pathlib import Path
-
-tokenizer = AutoTokenizer.from_pretrained('ucaslcl/GOT-OCR2_0', trust_remote_code=True)
-model = AutoModel.from_pretrained('ucaslcl/GOT-OCR2_0', trust_remote_code=True, low_cpu_mem_usage=True, device_map='cuda', use_safetensors=True)
-model = model.eval().cuda()
-
-UPLOAD_FOLDER = "./uploads"
-RESULTS_FOLDER = "./results"
-
-for folder in [UPLOAD_FOLDER, RESULTS_FOLDER]:
-    if not os.path.exists(folder):
-        os.makedirs(folder)
-
-def image_to_base64(image):
-    buffered = io.BytesIO()
-    image.save(buffered, format="PNG")
-    return base64.b64encode(buffered.getvalue()).decode()
-
-@spaces.GPU
-def run_GOT(image, got_mode, fine_grained_mode="", ocr_color="", ocr_box=""):
-    unique_id = str(uuid.uuid4())
-    image_path = os.path.join(UPLOAD_FOLDER, f"{unique_id}.png")
-    result_path = os.path.join(RESULTS_FOLDER, f"{unique_id}.html")
-    
-    shutil.copy(image, image_path)
-    
-    try:
-        if got_mode == "plain texts OCR":
-            res = model.chat(tokenizer, image_path, ocr_type='ocr')
-            return res, None
-        elif got_mode == "format texts OCR":
-            res = model.chat(tokenizer, image_path, ocr_type='format', render=True, save_render_file=result_path)
-        elif got_mode == "plain multi-crop OCR":
-            res = model.chat_crop(tokenizer, image_path, ocr_type='ocr')
-            return res, None
-        elif got_mode == "format multi-crop OCR":
-            res = model.chat_crop(tokenizer, image_path, ocr_type='format', render=True, save_render_file=result_path)
-        elif got_mode == "plain fine-grained OCR":
-            res = model.chat(tokenizer, image_path, ocr_type='ocr', ocr_box=ocr_box, ocr_color=ocr_color)
-            return res, None
-        elif got_mode == "format fine-grained OCR":
-            res = model.chat(tokenizer, image_path, ocr_type='format', ocr_box=ocr_box, ocr_color=ocr_color, render=True, save_render_file=result_path)
-
-        # res_markdown = f"$$ {res} $$"
-        res_markdown = res
-
-        if "format" in got_mode and os.path.exists(result_path):
-            with open(result_path, 'r') as f:
-                html_content = f.read()
-            encoded_html = base64.b64encode(html_content.encode('utf-8')).decode('utf-8')
-            iframe_src = f"data:text/html;base64,{encoded_html}"
-            iframe = f'<iframe src="{iframe_src}" width="100%" height="600px"></iframe>'
-            download_link = f'<a href="data:text/html;base64,{encoded_html}" download="result_{unique_id}.html">Download Full Result</a>'
-            return res_markdown, f"{download_link}<br>{iframe}"
-        else:
-            return res_markdown, None
-    except Exception as e:
-        return f"Error: {str(e)}", None
-    finally:
-        if os.path.exists(image_path):
-            os.remove(image_path)
-
-def task_update(task):
-    if "fine-grained" in task:
-        return [
-            gr.update(visible=True),
-            gr.update(visible=False),
-            gr.update(visible=False),
-        ]
-    else:
-        return [
-            gr.update(visible=False),
-            gr.update(visible=False),
-            gr.update(visible=False),
-        ]
-
-def fine_grained_update(task):
-    if task == "box":
-        return [
-            gr.update(visible=False, value = ""),
-            gr.update(visible=True),
-        ]
-    elif task == 'color':
-        return [
-            gr.update(visible=True),
-            gr.update(visible=False, value = ""),
-        ]
-
-def cleanup_old_files():
-    current_time = time.time()
-    for folder in [UPLOAD_FOLDER, RESULTS_FOLDER]:
-        for file_path in Path(folder).glob('*'):
-            if current_time - file_path.stat().st_mtime > 3600:  # 1 hour
-                file_path.unlink()
-
-title_html = """
-<h2> <span class="gradient-text" id="text">General OCR Theory</span><span class="plain-text">: Towards OCR-2.0 via a Unified End-to-end Model</span></h2>
-<a href="https://huggingface.co/ucaslcl/GOT-OCR2_0">[😊 Hugging Face]</a> 
-<a href="https://arxiv.org/abs/2409.01704">[📜 Paper]</a> 
-<a href="https://github.com/Ucas-HaoranWei/GOT-OCR2.0/">[🌟 GitHub]</a> 
-"""
-
-with gr.Blocks() as demo:
-    gr.HTML(title_html)
-    gr.Markdown("""
-    "🔥🔥🔥This is the official online demo of GOT-OCR-2.0 model!!!"
+import torch
+
+# Charger le modèle GOT-OCR2_0 pour la reconnaissance des plaques d'immatriculation
+ocr_tokenizer = AutoTokenizer.from_pretrained('ucaslcl/GOT-OCR2_0', trust_remote_code=True)
+ocr_model = AutoModel.from_pretrained('ucaslcl/GOT-OCR2_0', trust_remote_code=True, low_cpu_mem_usage=True, device_map='cuda', use_safetensors=True)
+ocr_model.eval().cuda()
+
+# Charger le modèle de suivi et reconnaissance de couleur des véhicules
+vehicle_model = torch.hub.load('ultralytics/yolov5', 'custom', path='sujithvamshi/Real-Time-Vehicle-Tracking-And-Colour-Recognition/best.pt')
+
+# Fonction pour extraire la plaque d'immatriculation avec OCR
+def get_license_plate(image):
+    # Utiliser le modèle GOT-OCR pour extraire le texte (plaque d'immatriculation)
+    image_tensor = ocr_tokenizer(image, return_tensors="pt").input_ids
+    with torch.no_grad():
+        output = ocr_model(image_tensor)
+        plate_text = ocr_tokenizer.decode(output.logits[0], skip_special_tokens=True)
+    return plate_text
+
+# Fonction pour extraire la couleur du véhicule
+def get_vehicle_color(image):
+    # Utiliser le modèle Real-Time-Vehicle-Tracking-And-Colour-Recognition pour obtenir la couleur
+    results = vehicle_model(image)
+    color_info = results.pandas().xyxy[0].color  # Hypothèse: le modèle retourne une info de couleur
+    return color_info
+
+# Fusionner les deux résultats
+def process_image(image_path):
+    image = Image.open(image_path)
     
-    ### Demo Guidelines
-    You need to upload your image below and choose one mode of GOT, then click "Submit" to run GOT model. More characters will result in longer wait times.
-    - **plain texts OCR & format texts OCR**: The two modes are for the image-level OCR.
-    - **plain multi-crop OCR & format multi-crop OCR**: For images with more complex content, you can achieve higher-quality results with these modes.
-    - **plain fine-grained OCR & format fine-grained OCR**: In these modes, you can specify fine-grained regions on the input image for more flexible OCR. Fine-grained regions can be coordinates of the box, red color, blue color, or green color.
-    """)
+    # 1. Extraire la plaque d'immatriculation
+    license_plate = get_license_plate(image)
     
-    with gr.Row():
-        with gr.Column():
-            image_input = gr.Image(type="filepath", label="upload your image")
-            task_dropdown = gr.Dropdown(
-                choices=[
-                    "plain texts OCR",
-                    "format texts OCR",
-                    "plain multi-crop OCR",
-                    "format multi-crop OCR",
-                    "plain fine-grained OCR",
-                    "format fine-grained OCR",
-                ],
-                label="Choose one mode of GOT",
-                value="plain texts OCR"
-            )
-            fine_grained_dropdown = gr.Dropdown(
-                choices=["box", "color"],
-                label="fine-grained type",
-                visible=False
-            )
-            color_dropdown = gr.Dropdown(
-                choices=["red", "green", "blue"],
-                label="color list",
-                visible=False
-            )
-            box_input = gr.Textbox(
-                label="input box: [x1,y1,x2,y2]",
-                placeholder="e.g., [0,0,100,100]",
-                visible=False
-            )
-            submit_button = gr.Button("Submit")
-        
-        with gr.Column():
-            ocr_result = gr.Textbox(label="GOT output")
-
-    with gr.Column():
-        gr.Markdown("**If you choose the mode with format, the mathpix result will be automatically rendered as follows:**")
-        html_result = gr.HTML(label="rendered html", show_label=True)
-    
-    gr.Examples(
-        examples=[
-            ["assets/coco.jpg", "plain texts OCR", "", "", ""],
-            ["assets/en_30.png", "plain texts OCR", "", "", ""],
-            ["assets/eq.jpg", "format texts OCR", "", "", ""],
-            ["assets/table.jpg", "format texts OCR", "", "", ""],
-            ["assets/giga.jpg", "format multi-crop OCR", "", "", ""],
-            ["assets/aff2.png", "plain fine-grained OCR", "box", "", "[409,763,756,891]"],
-            ["assets/color.png", "plain fine-grained OCR", "color", "red", ""],
-        ],
-        inputs=[image_input, task_dropdown, fine_grained_dropdown, color_dropdown, box_input],
-        outputs=[ocr_result, html_result],
-        fn=run_GOT,
-        label="examples",
-    )
-
-    task_dropdown.change(
-        task_update,
-        inputs=[task_dropdown],
-        outputs=[fine_grained_dropdown, color_dropdown, box_input]
-    )
-    fine_grained_dropdown.change(
-        fine_grained_update,
-        inputs=[fine_grained_dropdown],
-        outputs=[color_dropdown, box_input]
-    )
+    # 2. Extraire la couleur du véhicule
+    vehicle_color = get_vehicle_color(image)
     
-    submit_button.click(
-        run_GOT,
-        inputs=[image_input, task_dropdown, fine_grained_dropdown, color_dropdown, box_input],
-        outputs=[ocr_result, html_result]
-    )
-
-if __name__ == "__main__":
-    cleanup_old_files()
-    demo.launch()
+    # 3. Retourner la fusion des résultats
+    return {
+        "license_plate": license_plate,
+        "vehicle_color": vehicle_color
+    }
+
+# Exemple d'utilisation
+image_path = "path_to_your_image.jpg"
+result = process_image(image_path)
+print(f"Plaque d'immatriculation: {result['license_plate']}, Couleur du véhicule: {result['vehicle_color']}")