app.py

# from fastapi import FastAPI, UploadFile, File
# import cv2
# import torch
# import pandas as pd
# from PIL import Image
# from transformers import AutoImageProcessor, AutoModelForImageClassification
# from tqdm import tqdm
# import json
# import shutil
# from fastapi.middleware.cors import CORSMiddleware
# from fastapi.responses import HTMLResponse

# app = FastAPI()

# # Add CORS middleware to allow requests from localhost:8080 (or any origin you specify)
# app.add_middleware(
#     CORSMiddleware,
#     # allow_origins=["http://localhost:8080"],  # Replace with the URL of your Vue.js app
#     allow_origins=["http://localhost:8080"],  # Replace with the URL of your Vue.js app
#     allow_credentials=True,
#     allow_methods=["*"],  # Allows all HTTP methods (GET, POST, etc.)
#     allow_headers=["*"],  # Allows all headers (such as Content-Type, Authorization, etc.)
# )

# # Charger le processor et le modèle fine-tuné depuis le chemin local
# local_model_path = r'./vit-finetuned-ucf101'
# processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224")
# model = AutoModelForImageClassification.from_pretrained(local_model_path)
# # model = AutoModelForImageClassification.from_pretrained("2nzi/vit-finetuned-ucf101") 
# model.eval()

# # Fonction pour classifier une image
# def classifier_image(image):
#     image_pil = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
#     inputs = processor(images=image_pil, return_tensors="pt")
#     with torch.no_grad():
#         outputs = model(**inputs)
#         logits = outputs.logits
#     predicted_class_idx = logits.argmax(-1).item()
#     predicted_class = model.config.id2label[predicted_class_idx]
#     return predicted_class

# # Fonction pour traiter la vidéo et identifier les séquences de "Surfing"
# def identifier_sequences_surfing(video_path, intervalle=0.5):
#     cap = cv2.VideoCapture(video_path)
#     frame_rate = cap.get(cv2.CAP_PROP_FPS)
#     total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
#     frame_interval = int(frame_rate * intervalle)

#     resultats = []
#     sequences_surfing = []
#     frame_index = 0
#     in_surf_sequence = False
#     start_timestamp = None

#     with tqdm(total=total_frames, desc="Traitement des frames de la vidéo", unit="frame") as pbar:
#         success, frame = cap.read()
#         while success:
#             if frame_index % frame_interval == 0:
#                 timestamp = round(frame_index / frame_rate, 2)  # Maintain precision to the centisecond level
#                 classe = classifier_image(frame)
#                 resultats.append({"Timestamp": timestamp, "Classe": classe})

#                 if classe == "Surfing" and not in_surf_sequence:
#                     in_surf_sequence = True
#                     start_timestamp = timestamp

#                 elif classe != "Surfing" and in_surf_sequence:
#                     # Vérifier l'image suivante pour confirmer si c'était une erreur ponctuelle
#                     success_next, frame_next = cap.read()
#                     next_timestamp = round((frame_index + frame_interval) / frame_rate, 2)
#                     classe_next = None

#                     if success_next:
#                         classe_next = classifier_image(frame_next)
#                         resultats.append({"Timestamp": next_timestamp, "Classe": classe_next})

#                     # Si l'image suivante est "Surfing", on ignore l'erreur ponctuelle
#                     if classe_next == "Surfing":
#                         success = success_next
#                         frame = frame_next
#                         frame_index += frame_interval
#                         pbar.update(frame_interval)
#                         continue
#                     else:
#                         # Sinon, terminer la séquence "Surfing"
#                         in_surf_sequence = False
#                         end_timestamp = timestamp
#                         sequences_surfing.append((start_timestamp, end_timestamp))

#             success, frame = cap.read()
#             frame_index += 1
#             pbar.update(1)

#     # Si on est toujours dans une séquence "Surfing" à la fin de la vidéo
#     if in_surf_sequence:
#         sequences_surfing.append((start_timestamp, round(frame_index / frame_rate, 2)))

#     cap.release()
#     dataframe_sequences = pd.DataFrame(sequences_surfing, columns=["Début", "Fin"])
#     return dataframe_sequences

# # Fonction pour convertir les séquences en format JSON
# def convertir_sequences_en_json(dataframe):
#     events = []
#     blocks = []
#     for idx, row in dataframe.iterrows():
#         block = {
#             "id": f"Surfing{idx + 1}",
#             "start": round(row["Début"], 2),
#             "end": round(row["Fin"], 2)
#         }
#         blocks.append(block)
#     event = {
#         "event": "Surfing",
#         "blocks": blocks
#     }
#     events.append(event)
#     return events

# @app.post("/analyze_video/")
# async def analyze_video(file: UploadFile = File(...)):
#     with open("uploaded_video.mp4", "wb") as buffer:
#         shutil.copyfileobj(file.file, buffer)

#     dataframe_sequences = identifier_sequences_surfing("uploaded_video.mp4", intervalle=1)
#     json_result = convertir_sequences_en_json(dataframe_sequences)
#     return json_result

# @app.get("/", response_class=HTMLResponse)
# async def index():
#     return (
#         """
#         <html>
#             <body>
#                 <h1>Hello world!</h1>
#                 <p>This `/` is the most simple and default endpoint.</p>
#                 <p>If you want to learn more, check out the documentation of the API at 
#                 <a href='/docs'>/docs</a> or 
#                 <a href='https://2nzi-video-sequence-labeling.hf.space/docs' target='_blank'>external docs</a>.
#                 </p>
#             </body>
#         </html>
#         """
#     )


# # Lancer l'application avec uvicorn (command line)
# # uvicorn main:app --reload
# # http://localhost:8000/docs#/
# # (.venv) PS C:\Users\antoi\Documents\Work_Learn\Labeling-Deploy\FastAPI> uvicorn main:app --host 0.0.0.0 --port 8000 --workers 1


from fastapi import FastAPI, UploadFile, File
import cv2
import torch
import pandas as pd
from PIL import Image
from transformers import AutoImageProcessor, AutoModelForImageClassification
from tqdm import tqdm
import json
import shutil
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import HTMLResponse

app = FastAPI()

# Add CORS middleware to allow requests from localhost:8080 (or any origin you specify)
app.add_middleware(
    CORSMiddleware,
    # allow_origins=["http://localhost:8080"],  # Replace with the URL of your Vue.js app
    allow_origins=["http://localhost:8080"],  # Replace with the URL of your Vue.js app
    allow_credentials=True,
    allow_methods=["*"],  # Allows all HTTP methods (GET, POST, etc.)
    allow_headers=["*"],  # Allows all headers (such as Content-Type, Authorization, etc.)
)

# Charger le processor et le modèle fine-tuné depuis le chemin local
local_model_path = r'./vit-finetuned-ucf101'
processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224")
model = AutoModelForImageClassification.from_pretrained(local_model_path)
# model = AutoModelForImageClassification.from_pretrained("2nzi/vit-finetuned-ucf101") 
model.eval()

# Fonction pour classifier une image
def classifier_image(image):
    image_pil = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
    inputs = processor(images=image_pil, return_tensors="pt")
    with torch.no_grad():
        outputs = model(**inputs)
        logits = outputs.logits
    predicted_class_idx = logits.argmax(-1).item()
    predicted_class = model.config.id2label[predicted_class_idx]
    return predicted_class

# Fonction pour traiter la vidéo et identifier les séquences de "Surfing"
def identifier_sequences_surfing(video_path, intervalle=0.5):
    cap = cv2.VideoCapture(video_path)
    frame_rate = cap.get(cv2.CAP_PROP_FPS)
    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
    frame_interval = int(frame_rate * intervalle)

    resultats = []
    sequences_surfing = []
    frame_index = 0
    in_surf_sequence = False
    start_timestamp = None

    with tqdm(total=total_frames, desc="Traitement des frames de la vidéo", unit="frame") as pbar:
        success, frame = cap.read()
        while success:
            if frame_index % frame_interval == 0:
                timestamp = round(frame_index / frame_rate, 2)  # Maintain precision to the centisecond level
                classe = classifier_image(frame)
                resultats.append({"Timestamp": timestamp, "Classe": classe})

                if classe == "Surfing" and not in_surf_sequence:
                    in_surf_sequence = True
                    start_timestamp = timestamp

                elif classe != "Surfing" and in_surf_sequence:
                    # Vérifier l'image suivante pour confirmer si c'était une erreur ponctuelle
                    success_next, frame_next = cap.read()
                    next_timestamp = round((frame_index + frame_interval) / frame_rate, 2)
                    classe_next = None

                    if success_next:
                        classe_next = classifier_image(frame_next)
                        resultats.append({"Timestamp": next_timestamp, "Classe": classe_next})

                    # Si l'image suivante est "Surfing", on ignore l'erreur ponctuelle
                    if classe_next == "Surfing":
                        success = success_next
                        frame = frame_next
                        frame_index += frame_interval
                        pbar.update(frame_interval)
                        continue
                    else:
                        # Sinon, terminer la séquence "Surfing"
                        in_surf_sequence = False
                        end_timestamp = timestamp
                        sequences_surfing.append((start_timestamp, end_timestamp))

            success, frame = cap.read()
            frame_index += 1
            pbar.update(1)

    # Si on est toujours dans une séquence "Surfing" à la fin de la vidéo
    if in_surf_sequence:
        sequences_surfing.append((start_timestamp, round(frame_index / frame_rate, 2)))

    cap.release()
    dataframe_sequences = pd.DataFrame(sequences_surfing, columns=["Début", "Fin"])
    return dataframe_sequences

# Fonction pour convertir les séquences en format JSON
def convertir_sequences_en_json(dataframe):
    events = []
    blocks = []
    for idx, row in dataframe.iterrows():
        block = {
            "id": f"Surfing{idx + 1}",
            "start": round(row["Début"], 2),
            "end": round(row["Fin"], 2)
        }
        blocks.append(block)
    event = {
        "event": "Surfing",
        "blocks": blocks
    }
    events.append(event)
    return events


import os
import tempfile

@app.post("/analyze_video/")
async def analyze_video(file: UploadFile = File(...)):
    # Utiliser tempfile pour créer un fichier temporaire
    with tempfile.NamedTemporaryFile(delete=False, suffix=".mp4", dir="/tmp") as tmp:
        shutil.copyfileobj(file.file, tmp)
        tmp_path = tmp.name

    # Analyser la vidéo
    dataframe_sequences = identifier_sequences_surfing(tmp_path, intervalle=1)
    json_result = convertir_sequences_en_json(dataframe_sequences)

    # Supprimer le fichier temporaire après utilisation
    os.remove(tmp_path)

    return {"filename": file.filename, "result": json_result}

@app.get("/", response_class=HTMLResponse)
async def index():
    return (
        """
        <html>
            <body>
                <h1>Hello world!</h1>
                <p>This `/` is the most simple and default endpoint.</p>
                <p>If you want to learn more, check out the documentation of the API at 
                <a href='/docs'>/docs</a> or 
                <a href='https://2nzi-video-sequence-labeling.hf.space/docs' target='_blank'>external docs</a>.
                </p>
            </body>
        </html>
        """
    )


# Lancer l'application avec uvicorn (command line)
# uvicorn main:app --reload
# http://localhost:8000/docs#/
# (.venv) PS C:\Users\antoi\Documents\Work_Learn\Labeling-Deploy\FastAPI> uvicorn main:app --host 0.0.0.0 --port 8000 --workers 1