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Dockerfile

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+FROM python:3.9-slim
+
+# Set the working directory to /app
+WORKDIR /app
+
+# Copy the requirements file
+COPY requirements.txt .
+
+# Install the dependencies
+RUN pip install -r requirements.txt
+
+# Copy the application code
+COPY . .
+
+# Expose the port
+EXPOSE 8000
+
+# Run the command to start the development server
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

predict.py

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+from fastapi import FastAPI, File, UploadFile, Response
+from fastapi.responses import FileResponse
+from tensorflow.keras.preprocessing.image import img_to_array
+import tensorflow as tf
+import cv2
+import numpy as np
+import os
+from scipy import fftpack
+from scipy import ndimage
+from ultralytics import YOLO
+from PIL import Image
+import io
+
+app = FastAPI()
+uploads_dir = 'uploads'
+
+if not os.path.exists(uploads_dir):
+    os.makedirs(uploads_dir)
+
+# Load the saved models
+segmentation_model_path = 'segmentation_model.h5'
+segmentation_model = tf.keras.models.load_model(segmentation_model_path)
+yolo_model_path = 'best.pt'
+yolo_model = YOLO(yolo_model_path)
+
+def calculate_moisture_and_texture(image):
+    gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    fft_image = fftpack.fft2(gray_image)
+    fft_shifted = fftpack.fftshift(fft_image)
+    magnitude_spectrum = 20 * np.log(np.abs(fft_shifted))
+    height, width = magnitude_spectrum.shape
+    center_x, center_y = width // 2, height // 2
+    radius = min(center_x, center_y) // 2
+    moisture_region = magnitude_spectrum[center_y - radius:center_y + radius, center_x - radius:center_x + radius]
+    moisture_level = np.mean(moisture_region)
+    return moisture_level
+
+def calculate_wound_dimensions(mask):
+    labeled_mask, num_labels = ndimage.label(mask > 0.5) 
+    label_count = np.bincount(labeled_mask.ravel())
+    wound_label = np.argmax(label_count[1:]) + 1  
+    wound_region = labeled_mask == wound_label   
+    rows = np.any(wound_region, axis=1)
+    cols = np.any(wound_region, axis=0)
+    rmin, rmax = np.where(rows)[0][[0, -1]]
+    cmin, cmax = np.where(cols)[0][[0, -1]]
+    length_pixels = rmax - rmin
+    breadth_pixels = cmax - cmin
+    pixel_to_cm_ratio = 0.1
+    length_cm = length_pixels * pixel_to_cm_ratio
+    breadth_cm = breadth_pixels * pixel_to_cm_ratio
+    depth_cm = np.mean(mask[wound_region]) * pixel_to_cm_ratio
+    length_cm = round(length_cm, 3)
+    breadth_cm = round(breadth_cm, 3)
+    depth_cm = round(depth_cm, 3)
+    area_cm2 = length_cm * breadth_cm
+    return length_cm, breadth_cm, depth_cm, area_cm2
+
+@app.post("/analyze_wound")
+async def analyze_wounds(file: UploadFile = File(...)):
+    if file.filename.lower().endswith(('.png', '.jpg', '.jpeg')):
+        contents = await file.read()
+        nparr = np.fromstring(contents, np.uint8)
+        img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+        results = yolo_model(img) 
+        combined_xmin = float('inf')
+        combined_ymin = float('inf')
+        combined_xmax = float('-inf')
+        combined_ymax = float('-inf')
+        for detection in results[0].boxes.xyxy.tolist():
+            xmin, ymin, xmax, ymax = detection
+            combined_xmin = min(combined_xmin, xmin)
+            combined_ymin = min(combined_ymin, ymin)
+            combined_xmax = max(combined_xmax, xmax)
+            combined_ymax = max(combined_ymax, ymax)
+        combined_xmin = int(combined_xmin)
+        combined_ymin = int(combined_ymin)
+        combined_xmax = int(combined_xmax)
+        combined_ymax = int(combined_ymax)
+        combined_img = img[combined_ymin:combined_ymax, combined_xmin:combined_xmax]
+        combined_img_resized = cv2.resize(combined_img, (224, 224))
+        img_array = img_to_array(combined_img_resized) / 255.0
+        img_array = np.expand_dims(img_array, axis=0)
+        output = segmentation_model.predict(img_array)
+        predicted_mask = output[0]
+        mask_overlay = (predicted_mask.squeeze() * 255).astype(np.uint8)
+        mask_overlay_colored = np.zeros((mask_overlay.shape[0], mask_overlay.shape[1], 3), dtype=np.uint8)
+        mask_overlay_colored[mask_overlay > 200] = [255, 0, 0]  # Red
+        mask_overlay_colored[(mask_overlay > 100) & (mask_overlay <= 200)] = [0, 255, 0]  # Green
+        mask_overlay_colored[mask_overlay <= 100] = [0, 0, 255]  # Blue
+        mask_overlay_colored = cv2.resize(mask_overlay_colored, (224, 224))
+        blended_image = cv2.addWeighted(combined_img_resized.astype(np.uint8), 0.6, mask_overlay_colored, 0.4, 0)
+        segmented_image = Image.fromarray(cv2.cvtColor(blended_image, cv2.COLOR_BGR2RGB))
+        img_byte_arr = io.BytesIO()
+        segmented_image.save(img_byte_arr, format='PNG')
+        img_byte_arr.seek(0)
+        length_cm, breadth_cm, depth_cm, area_cm2 = calculate_wound_dimensions(predicted_mask)
+        moisture = calculate_moisture_and_texture(combined_img)
+        response = Response(img_byte_arr.getvalue(), media_type='image/png')
+        response.headers['X-Length-Cm'] = str(length_cm)
+        response.headers['X-Breadth-Cm'] = str(breadth_cm)
+        response.headers['X-Depth-Cm'] = str(depth_cm)
+        response.headers['X-Area-Cm2'] = str(area_cm2)
+        response.headers['X-Moisture'] = str(moisture)
+        return response
+    return {'error': 'Invalid file format'}
+
+if __name__ == '__main__':
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)

requirements.txt

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+fastapi
+uvicorn
+starlette
+anyio
+aiofiles
+python-multipart
+pydantic
+numpy
+opencv-python
+tensorflow