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piyush3 commited on Dec 13, 2024

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353d4c7

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1 Parent(s): 416e152

Update app.py

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app.py +210 -112

app.py CHANGED Viewed

@@ -1,121 +1,219 @@
-<!DOCTYPE html>
-<html lang="en">
-<head>
-    <meta charset="UTF-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <title>OSMF Detection</title>
-    <link rel="stylesheet" href="{{ url_for('static', filename='style.css') }}">
-    <link href="https://fonts.googleapis.com/css2?family=Poppins:wght@400;500;600&display=swap" rel="stylesheet">
-</head>
-<body>
-    <div class="container">
-        <h1>OSMF Classification : OcanPredict</h1>
-        <div class="main-content">
-            <div class="left-panel">
-                <div class="upload-section">
-                    <h2>Upload Image</h2>
-                    <div class="file-upload">
-                        <input type="file" id="fileInput" accept="image/*">
-                        <label for="fileInput">Choose File</label>
-                    </div>
-                    <button id="uploadPredict" disabled>Analyze Upload</button>
-                </div>
-                <div class="separator">
-                    <span>OR</span>
-                </div>
-                <div class="camera-section">
-                    <h2>Capture Image</h2>
-                    <video id="video" autoplay playsinline></video>
-                    <canvas id="canvas" style="display: none;"></canvas>
-                    <button id="capture">Capture Image</button>
-                </div>
-            </div>
-            <div class="right-panel">
-                <div class="preview-section">
-                    <h2>Preview</h2>
-                    <img id="preview" style="display: none;">
-                    <button id="predict" disabled>Analyze Capture</button>
-                </div>
-                <div id="result" class="result-container"></div>
-            </div>
-        </div>
-    </div>
-    <script>
-        const video = document.getElementById('video');
-        const canvas = document.getElementById('canvas');
-        const preview = document.getElementById('preview');
-        const captureBtn = document.getElementById('capture');
-        const predictBtn = document.getElementById('predict');
-        const result = document.getElementById('result');
-        const fileInput = document.getElementById('fileInput');
-        const uploadPredict = document.getElementById('uploadPredict');
-        // Access webcam
-        navigator.mediaDevices.getUserMedia({ video: true })
-            .then(stream => {
-                video.srcObject = stream;
-            });
-        // File upload handling
-        fileInput.addEventListener('change', (e) => {
-            const file = e.target.files[0];
-            if (file) {
-                const reader = new FileReader();
-                reader.onload = (e) => {
-                    preview.src = e.target.result;
-                    preview.style.display = 'block';
-                    uploadPredict.disabled = false;
-                    predictBtn.disabled = true;
-                };
-                reader.readAsDataURL(file);
-            }
-        });
-        captureBtn.addEventListener('click', () => {
-            canvas.width = video.videoWidth;
-            canvas.height = video.videoHeight;
-            canvas.getContext('2d').drawImage(video, 0, 0);
-            preview.src = canvas.toDataURL('image/png');
-            preview.style.display = 'block';
-            predictBtn.disabled = false;
-            uploadPredict.disabled = true;
-            fileInput.value = '';
-        });
-        async function predict(formData) {
-            try {
-                const response = await fetch('/predict', {
-                    method: 'POST',
-                    body: formData
-                });
-                const data = await response.json();
-                result.innerHTML = `
-                    <h2>Results:</h2>
-                    <p>Classification: ${data.class}</p>
-                `;
-            } catch (error) {
-                result.innerHTML = 'Error during prediction';
-            }
-        }
-        predictBtn.addEventListener('click', async () => {
-            const blob = await (await fetch(preview.src)).blob();
-            const formData = new FormData();
-            formData.append('image', blob, 'capture.png');
-            await predict(formData);
-        });
-        uploadPredict.addEventListener('click', async () => {
-            const formData = new FormData();
-            formData.append('image', fileInput.files[0]);
-            await predict(formData);
-        });
-    </script>
-</body>
-</html>

+from fastapi import FastAPI, File, UploadFile, HTTPException, WebSocket, WebSocketDisconnect
+from fastapi.middleware.cors import CORSMiddleware
+import os
+import tempfile
+from numpy import asarray
+from ultralytics import YOLO
+import base64
+import shutil
+from fastapi.responses import JSONResponse
+import json
+import cv2
+import mediapipe as mp
+import pandas as pd
+import numpy as np
+import multiprocessing
+app = FastAPI()
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+distance_value = multiprocessing.Value('i', 0)
+def start_lidar_process():
+    import lidar_dist
+    process = multiprocessing.Process(target=lidar_dist.lidar_process, args=(distance_value,))
+    process.start()
+    return process
+lidar_process = start_lidar_process()
+def get_single_image_name(directory_path):
+    image_names = [file for file in os.listdir(directory_path) if file.lower().endswith(('.png', '.jpg', '.jpeg', '.gif'))]
+    return image_names[0]
+mp_face_mesh = mp.solutions.face_mesh
+face_mesh = mp_face_mesh.FaceMesh(static_image_mode=True, max_num_faces=1)
+data = pd.read_csv('/home/chait/Desktop/Proj/backend/data1.csv')
+osmf = YOLO('/home/chait/Desktop/Proj/backend/best.pt', task="classify")
+calculus = YOLO('/home/chait/Desktop/Proj/backend/calculus.pt', task="classify")
+calculus_inst = YOLO('/home/chait/Desktop/Proj/backend/calculus-inst.pt', task="segment")
+gingivitis = YOLO('/home/chait/Desktop/Proj/backend/gingivitis.pt', task="classify")
+phenotype = YOLO('/home/chait/Desktop/Proj/backend/phenotype.pt', task="classify")
+@app.get("/")
+def read_root():
+    return {"Hello": "World"}
+@app.post('/opening')
+def opening(file: UploadFile = None):
+    if file is None:
+        raise HTTPException(status_code=400, detail="No file provided")
+    try:
+        if file:
+            print(f"Lidar Distance: {distance_value.value}")
+            with tempfile.NamedTemporaryFile(delete=False, suffix='.png') as temp_file:
+                temp_file.write(file.file.read())
+            image = cv2.imread(temp_file.name)
+            image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+            results = face_mesh.process(image)
+            if results.multi_face_landmarks:
+                for face_landmarks in results.multi_face_landmarks:
+                    lower_lip_center = (
+                            int(face_landmarks.landmark[13].x * image.shape[1]),
+                            int(face_landmarks.landmark[13].y * image.shape[0])
+                        )
+                    upper_lip_center = (
+                            int(face_landmarks.landmark[14].x * image.shape[1]),
+                            int(face_landmarks.landmark[14].y * image.shape[0])
+                        )
+                    cv2.circle(image, lower_lip_center, 2, (0, 255, 0), -1)
+                    cv2.circle(image, upper_lip_center, 2, (0, 255, 0), -1)
+                    dist = np.linalg.norm(np.array(upper_lip_center) - np.array(lower_lip_center))
+                    if dist > 2:
+                        index = (data["Lidar Distance"] - distance_value.value).abs().idxmin()
+                        multiplication_factor = data.iloc[index]["Multiplication Factor"]
+                        actual_length = dist * multiplication_factor * 10
+                        text = f"Actual Distance: {actual_length} cm"
+                        cv2.putText(image, text, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2, cv2.LINE_AA)
+                    else:
+                        text = "Mouth is Closed"
+                        cv2.putText(image, text, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2, cv2.LINE_AA)
+        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+        _, buffer = cv2.imencode('.jpg', image)
+        encoded_image = base64.b64encode(buffer).decode('utf-8')
+        os.remove(temp_file.name)
+        result = {'status': 'success', 'generatedImage': encoded_image, 'opening': actual_length}
+        return JSONResponse(content=result)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+@app.post('/osmf')
+async def osmf_detection(file: UploadFile = None):
+    if file is None:
+        raise HTTPException(status_code=400, detail="No file provided")
+    if file:
+        print(file.filename.lower())
+    try:
+        if file:
+            with tempfile.NamedTemporaryFile(delete=False, suffix='.png') as temp_file:
+                temp_file.write(file.file.read())
+            results = osmf.predict(source=temp_file.name, conf=0.2, save=True)
+            predict = osmf(temp_file.name)
+            js = predict[0].tojson()
+            predict_dict = json.loads(js)
+            name = predict_dict[0]["name"]
+            confidence = predict_dict[0]["confidence"]
+            os.remove(temp_file.name)
+            image_name = get_single_image_name('/home/chait/Desktop/runs/classify/predict')
+            with open('/home/chait/Desktop/runs/classify/predict/'+image_name, "rb") as image_file:
+                encoded_image = base64.b64encode(image_file.read()).decode('utf-8')
+            result = {'status': 'success', 'generatedImage': encoded_image, 'class': name, 'conf': confidence}
+            shutil.rmtree('/home/chait/Desktop/runs')
+            return JSONResponse(content=result)
+        else:
+            return{'status': 'error with file'}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+@app.post('/calculus')
+async def calculus_class(file: UploadFile = None):
+    if file is None:
+        raise HTTPException(status_code=400, detail="No file provided")
+    if file:
+        print(file.filename.lower())
+    try:
+        if file:
+            with tempfile.NamedTemporaryFile(delete=False, suffix='.png') as temp_file:
+                temp_file.write(file.file.read())
+            predict = calculus(temp_file.name)
+            js = predict[0].tojson()
+            predict_dict = json.loads(js)
+            name = predict_dict[0]["name"]
+            confidence = predict_dict[0]["confidence"]
+            predict[0].save('/home/chait/Desktop/Proj/backend/calculus-class.jpg')
+            if name == "Calculus":
+                instance = calculus_inst(source=temp_file.name)
+                instance[0].save('/home/chait/Desktop/Proj/backend/calculus-inst.jpg')
+                with open('/home/chait/Desktop/Proj/backend/calculus-inst.jpg', "rb") as image_file:
+                    encoded_image = base64.b64encode(image_file.read()).decode('utf-8')
+                result = { 'status': 'success', 'generatedImage': encoded_image, 'class': name, 'conf': confidence}
+                os.remove('./calculus-inst.jpg')
+            else:
+                with open('/home/chait/Desktop/Proj/backend/calculus-class.jpg', "rb") as image_file:
+                    encoded_image = base64.b64encode(image_file.read()).decode('utf-8')
+                result = {'status': 'success', 'generatedImage': encoded_image, 'class': name, 'conf': confidence}
+            os.remove('/home/chait/Desktop/Proj/backend/calculus-class.jpg')
+            os.remove(temp_file.name)
+            return JSONResponse(content=result)
+        else:
+            return{'status': 'error with file'}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+@app.post('/gingivitis')
+async def gingivitis_class(file: UploadFile = None):
+    if file is None:
+        raise HTTPException(status_code=400, detail="No file provided")
+    if file:
+        print(file.filename.lower())
+    try:
+        if file:
+            with tempfile.NamedTemporaryFile(delete=False, suffix='.png') as temp_file:
+                temp_file.write(file.file.read())
+            results = gingivitis.predict(source=temp_file.name, conf=0.2, save=True)
+            predict = gingivitis(temp_file.name)
+            js = predict[0].tojson()
+            predict_dict = json.loads(js)
+            name = predict_dict[0]["name"]
+            confidence = predict_dict[0]["confidence"]
+            os.remove(temp_file.name)
+            image_name = get_single_image_name('/home/chait/Desktop/runs/classify/predict')
+            with open('/home/chait/Desktop/runs/classify/predict/'+image_name, "rb") as image_file:
+                encoded_image = base64.b64encode(image_file.read()).decode('utf-8')
+            result = {'status': 'success', 'generatedImage': encoded_image, 'class': name, 'conf': confidence}
+            shutil.rmtree('/home/chait/Desktop/runs')
+            return JSONResponse(content=result)
+        else:
+            return{'status': 'error with file'}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+@app.post('/phenotype')
+async def phenotype_class(file: UploadFile = None):
+    if file is None:
+        raise HTTPException(status_code=400, detail="No file provided")
+    if file:
+        print(file.filename.lower())
+    try:
+        if file:
+            with tempfile.NamedTemporaryFile(delete=False, suffix='.png') as temp_file:
+                temp_file.write(file.file.read())
+            results = phenotype.predict(source=temp_file.name, conf=0.5, save=True)
+            predict = phenotype(temp_file.name)
+            js = predict[0].tojson()
+            predict_dict = json.loads(js)
+            name = predict_dict[0]["name"]
+            confidence = predict_dict[0]["confidence"]
+            os.remove(temp_file.name)
+            image_name = get_single_image_name('/home/chait/Desktop/runs/classify/predict')
+            with open('/home/chait/Desktop/runs/classify/predict/'+image_name, "rb") as image_file:
+                encoded_image = base64.b64encode(image_file.read()).decode('utf-8')
+            result = {'status': 'success', 'generatedImage': encoded_image, 'class': name, 'conf': confidence}
+            shutil.rmtree('/home/chait/Desktop/runs')
+            return JSONResponse(content=result)
+        else:
+            return{'status': 'error with file'}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app)