add fetch image api

.gitignore

@@ -1 +1,2 @@
-model_final.pth
+**/model_final.pth
+**/__pycache__

app.py

@@ -12,61 +12,16 @@ import gdown
 from skimage import io as skio
 from torchvision.ops import box_iou
 import torch
+from roboflow import Roboflow
+import supervision as sv
+import cv2
+import tempfile
+import os
+import numpy as np
+import requests
 
 # Initialize Flask app
 app = Flask(__name__)
-cfg = None
-# Google Drive file URL
-GDRIVE_MODEL_URL = "https://drive.google.com/uc?id=18aEDo-kWOBhg8mAhnbpFkuM6bmmrBH4E"  # Replace 'your-file-id' with the actual file ID from Google Drive
-LOCAL_MODEL_PATH = "model_final.pth"
-
-
-def download_file_from_google_drive(id, destination):
-    gdown.download(GDRIVE_MODEL_URL, LOCAL_MODEL_PATH, quiet=False)
-
-
-file_id = "18aEDo-kWOBhg8mAhnbpFkuM6bmmrBH4E"
-destination = "model_final.pth"
-download_file_from_google_drive(file_id, destination)
-
-
-# Download model from Google Drive if not already present locally
-def download_model():
-    if not os.path.exists(LOCAL_MODEL_PATH):
-        response = requests.get(GDRIVE_MODEL_URL, stream=True)
-        if response.status_code == 200:
-            with open(LOCAL_MODEL_PATH, "wb") as f:
-                f.write(response.content)
-        else:
-            raise Exception(
-                f"Failed to download model from Google Drive: {response.status_code}"
-            )
-
-
-# Configuration and model setup
-def setup_model(model_path):
-    global cfg
-    cfg = get_cfg()
-    cfg.merge_from_file("config.yaml")  # Update with the config file path
-    cfg.MODEL.WEIGHTS = model_path
-    cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5
-    cfg.MODEL.DEVICE = "cpu"  # Use "cuda" for GPU
-    return DefaultPredictor(cfg)
-
-
-# Ensure model is available
-predictor = setup_model(LOCAL_MODEL_PATH)
-
-# Define expected parts and costs
-expected_parts = ["headlamp", "rear_bumper", "door", "hood", "front_bumper"]
-cost_dict = {
-    "headlamp": 300,
-    "rear_bumper": 250,
-    "door": 200,
-    "hood": 220,
-    "front_bumper": 250,
-    "other": 150,
-}
 
 
 @app.route("/")
@@ -74,68 +29,6 @@ def home():
     return render_template("index.html")
 
 
-@app.route("/upload", methods=["POST"])
-def upload():
-    if "file" not in request.files:
-        return jsonify({"error": "No file uploaded"}), 400
-
-    file = request.files["file"]
-    if file.filename == "":
-        return jsonify({"error": "No file selected"}), 400
-
-    # Load image
-    image = skio.imread(file)
-    image_np = image
-
-    # Run model prediction
-    outputs = predictor(image_np)
-    instances = outputs["instances"].to("cpu")
-    class_names = MetadataCatalog.get(cfg.DATASETS.TEST[0]).thing_classes
-
-    # Extract bounding boxes and class IDs
-    boxes = instances.pred_boxes.tensor.numpy()
-    class_ids = instances.pred_classes.numpy()
-
-    # Filter overlapping boxes using IoU
-    iou_threshold = 0.8
-    keep_indices = []
-    merged_boxes = set()
-
-    for i in range(len(boxes)):
-        if i in merged_boxes:
-            continue
-        keep_indices.append(i)
-        for j in range(i + 1, len(boxes)):
-            if j in merged_boxes:
-                continue
-            iou = box_iou(
-                torch.tensor(boxes[i]).unsqueeze(0), torch.tensor(boxes[j]).unsqueeze(0)
-            ).item()
-            if iou > iou_threshold:
-                merged_boxes.add(j)
-
-    # Calculate total cost based on non-overlapping boxes
-    total_cost = 0
-    damage_details = []
-
-    for idx in keep_indices:
-        class_id = class_ids[idx]
-        damaged_part = (
-            class_names[class_id] if class_id < len(class_names) else "unknown"
-        )
-        if damaged_part not in expected_parts:
-            damaged_part = "other"
-
-        repair_cost = cost_dict.get(damaged_part, cost_dict["other"])
-        total_cost += repair_cost
-
-        damage_details.append({"part": damaged_part, "cost_usd": repair_cost})
-
-    response = {"damages": damage_details, "total_cost": total_cost}
-
-    return jsonify(response)
-
-
 @app.route("/fetch-image", methods=["POST"])
 def fetchImage():
     file = None
@@ -145,58 +38,133 @@ def fetchImage():
         file = io.BytesIO(response.content)
     elif "file" in request.files:
         file = request.files["file"]
+    url = "https://firebasestorage.googleapis.com/v0/b/car-damage-detector-s34rrz.firebasestorage.app/o/users%2FYMd99dt33HaktTWpYp5MM5oYeBE3%2Fuploads%2F1737454072124000.jpg?alt=media&token=9eae79fa-4c06-41a5-9f58-236c39efaac0"
+
+    # File name for saving
+    file_name = "downloaded_image.jpg"
 
+    # Download the image
+    response = requests.get(url)
+
+    # Save the image to the current directory
+    if response.status_code == 200:
+        with open(file_name, "wb") as file:
+            file.write(response.content)
+        print(f"Image downloaded and saved as {file_name}")
+    else:
+        print(f"Failed to download image. Status code: {response.status_code}")
     # Load image
-    image = io.imread(file)
-    image_np = image
+    image = cv2.imread(file_name)
+
+    rf = Roboflow(api_key="LqD8Cs4OsoK8seO3CPkf")
+
+    project_parts = rf.workspace().project("car-parts-segmentation")
+    model_parts = project_parts.version(2).model
+
+    project_damage = rf.workspace().project("car-damage-detection-ha5mm")
+    model_damage = project_damage.version(1).model
+
+    # Run the damage detection model
+    result_damage = model_damage.predict(
+        file_name,
+        confidence=40,
+    ).json()
+
+    # Extract detections from the result
+    detections_damage = sv.Detections.from_inference(result_damage)
+
+    # Read the input image
+
+    # Annotate damaged areas of the car
+    mask_annotator = sv.MaskAnnotator()
+    annotated_image_damage = mask_annotator.annotate(
+        scene=image, detections=detections_damage
+    )
+
+    # Create a temporary directory to save outputs
+    temp_dir = tempfile.mkdtemp()
+
+    # Define a repair cost dictionary (per part)
+    repair_cost_dict = {
+        "wheel": 100,  # Base cost for wheel
+        "door": 200,  # Base cost for door
+        "hood": 300,  # Base cost for hood
+        "front_bumper": 250,  # Base cost for bumper
+        "trunk": 200,
+        "front_glass": 150,
+        "back_left_door": 200,
+        "left_mirror": 20,
+        "back_glass": 150,
+    }
+
+    # Initialize total cost
+    total_cost = 0
 
-    # Run model prediction
-    outputs = predictor(image_np)
-    instances = outputs["instances"].to("cpu")
-    class_names = MetadataCatalog.get(cfg.DATASETS.TEST[0]).thing_classes
+    # Ensure coordinate processing is done in chunks of 4
+    coordinates = list(map(int, detections_damage.xyxy.flatten()))
+    num_damages = (
+        len(coordinates) // 4
+    )  # Each damage has 4 coordinates (x1, y1, x2, y2)
 
-    # Extract bounding boxes and class IDs
-    boxes = instances.pred_boxes.tensor.numpy()
-    class_ids = instances.pred_classes.numpy()
+    # Iterate through damages
+    for i in range(num_damages):
+        x1, y1, x2, y2 = coordinates[i * 4 : (i + 1) * 4]
 
-    # Filter overlapping boxes using IoU
-    iou_threshold = 0.8
-    keep_indices = []
-    merged_boxes = set()
+        # Ensure the coordinates are within image bounds
+        x1, y1 = max(0, x1), max(0, y1)
+        x2, y2 = min(image.shape[1], x2), min(image.shape[0], y2)
 
-    for i in range(len(boxes)):
-        if i in merged_boxes:
+        # Crop the damaged region
+        cropped_damage = image[y1:y2, x1:x2]
+
+        # Check if the cropped region is valid
+        if cropped_damage.size == 0:
+            print(f"Skipping empty crop for damage region {i + 1}")
             continue
-        keep_indices.append(i)
-        for j in range(i + 1, len(boxes)):
-            if j in merged_boxes:
-                continue
-            iou = box_iou(
-                torch.tensor(boxes[i]).unsqueeze(0), torch.tensor(boxes[j]).unsqueeze(0)
-            ).item()
-            if iou > iou_threshold:
-                merged_boxes.add(j)
-
-    # Calculate total cost based on non-overlapping boxes
-    total_cost = 0
-    damage_details = []
 
-    for idx in keep_indices:
-        class_id = class_ids[idx]
-        damaged_part = (
-            class_names[class_id] if class_id < len(class_names) else "unknown"
-        )
-        if damaged_part not in expected_parts:
-            damaged_part = "other"
+        # Save the cropped damaged area
+        damage_image_path = os.path.join(temp_dir, f"damage_image_{i}.png")
+        cv2.imwrite(damage_image_path, cropped_damage)
+
+        # Run the parts detection model on the cropped damage
+        result_parts = model_parts.predict(damage_image_path, confidence=15).json()
+        detections_parts = sv.Detections.from_inference(result_parts)
+
+        # Calculate repair cost for each detected part
+        for part in result_parts["predictions"]:
+            part_name = part["class"]
+            damage_area = part["width"] * part["height"]
+            cropped_area = (x2 - x1) * (y2 - y1)
+            damage_percentage = (damage_area / cropped_area) * 100
+
+            # Lookup cost and add to total
+            base_cost = repair_cost_dict.get(
+                part_name, 0
+            )  # Default to 0 if part not in dict
+            repair_cost = (damage_percentage / 100) * base_cost
+            total_cost += repair_cost
+
+            print(
+                f"Damage {i + 1} - {part_name}: {damage_percentage:.2f}% damaged, Cost: ${repair_cost:.2f}"
+            )
 
-        repair_cost = cost_dict.get(damaged_part, cost_dict["other"])
-        total_cost += repair_cost
+        # Annotate and save the result
+        part_annotator = sv.LabelAnnotator()
+        annotated_parts_image = part_annotator.annotate(
+            scene=cropped_damage, detections=detections_parts
+        )
+        annotated_parts_path = os.path.join(temp_dir, f"annotated_parts_{i}.png")
+        cv2.imwrite(annotated_parts_path, annotated_parts_image)
 
-        damage_details.append({"part": damaged_part, "cost_usd": repair_cost})
+    # Save the overall annotated image
+    annotated_image_path = os.path.join(temp_dir, "annotated_image_damage.png")
+    cv2.imwrite(annotated_image_path, annotated_image_damage)
 
-    response = {"damages": damage_details, "total_cost": total_cost}
+    # Return the total cost in the specified format
+    result = {"total_cost": total_cost}
+    print(result)
 
-    return jsonify(response)
+    return jsonify(result)
 
 
 if __name__ == "__main__":

requirements.txt

@@ -13,3 +13,7 @@ Pillow
 opencv-python
 uvicorn
 scikit-image
+roboflow 
+supervision 
+opencv-python
+requests

temp.py

@@ -0,0 +1,159 @@
+from flask import Flask, request, jsonify, render_template
+from detectron2.config import get_cfg
+from detectron2.engine import DefaultPredictor
+from detectron2.data import MetadataCatalog
+from detectron2.utils.visualizer import Visualizer, ColorMode
+import numpy as np
+from PIL import Image
+import io
+import os
+import requests
+import gdown
+from skimage import io as skio
+from torchvision.ops import box_iou
+import torch
+
+# Initialize Flask app
+app = Flask(__name__)
+cfg = None
+# Google Drive file URL
+GDRIVE_MODEL_URL = "https://drive.google.com/uc?id=18aEDo-kWOBhg8mAhnbpFkuM6bmmrBH4E"  # Replace 'your-file-id' with the actual file ID from Google Drive
+LOCAL_MODEL_PATH = "model_final.pth"
+
+
+def download_file_from_google_drive(id, destination):
+    gdown.download(GDRIVE_MODEL_URL, LOCAL_MODEL_PATH, quiet=False)
+
+
+file_id = "18aEDo-kWOBhg8mAhnbpFkuM6bmmrBH4E"
+destination = "model_final.pth"
+download_file_from_google_drive(file_id, destination)
+
+
+# Download model from Google Drive if not already present locally
+def download_model():
+    if not os.path.exists(LOCAL_MODEL_PATH):
+        response = requests.get(GDRIVE_MODEL_URL, stream=True)
+        if response.status_code == 200:
+            with open(LOCAL_MODEL_PATH, "wb") as f:
+                f.write(response.content)
+        else:
+            raise Exception(
+                f"Failed to download model from Google Drive: {response.status_code}"
+            )
+
+
+# Configuration and model setup
+def setup_model(model_path):
+    global cfg
+    cfg = get_cfg()
+    cfg.merge_from_file("config.yaml")  # Update with the config file path
+    cfg.MODEL.WEIGHTS = model_path
+    cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5
+    cfg.MODEL.DEVICE = "cpu"  # Use "cuda" for GPU
+    return DefaultPredictor(cfg)
+
+
+# Ensure model is available
+predictor = setup_model(LOCAL_MODEL_PATH)
+
+# Define expected parts and costs
+expected_parts = ["headlamp", "rear_bumper", "door", "hood", "front_bumper"]
+cost_dict = {
+    "headlamp": 300,
+    "rear_bumper": 250,
+    "door": 200,
+    "hood": 220,
+    "front_bumper": 250,
+    "other": 150,
+}
+
+
+@app.route("/")
+def home():
+    return render_template("index.html")
+
+
+@app.route("/upload", methods=["POST"])
+def upload():
+    if "file" not in request.files:
+        return jsonify({"error": "No file uploaded"}), 400
+
+    file = request.files["file"]
+    if file.filename == "":
+        return jsonify({"error": "No file selected"}), 400
+
+    # Load image
+    image = skio.imread(file)
+    image_np = image
+
+    # Run model prediction
+    outputs = predictor(image_np)
+    instances = outputs["instances"].to("cpu")
+    class_names = MetadataCatalog.get(cfg.DATASETS.TEST[0]).thing_classes
+
+    # Extract bounding boxes and class IDs
+    boxes = instances.pred_boxes.tensor.numpy()
+    class_ids = instances.pred_classes.numpy()
+
+    # Filter overlapping boxes using IoU
+    iou_threshold = 0.8
+    keep_indices = []
+    merged_boxes = set()
+
+    for i in range(len(boxes)):
+        if i in merged_boxes:
+            continue
+        keep_indices.append(i)
+        for j in range(i + 1, len(boxes)):
+            if j in merged_boxes:
+                continue
+            iou = box_iou(
+                torch.tensor(boxes[i]).unsqueeze(0), torch.tensor(boxes[j]).unsqueeze(0)
+            ).item()
+            if iou > iou_threshold:
+                merged_boxes.add(j)
+
+    # Calculate total cost based on non-overlapping boxes
+    total_cost = 0
+    damage_details = []
+
+    for idx in keep_indices:
+        class_id = class_ids[idx]
+        damaged_part = (
+            class_names[class_id] if class_id < len(class_names) else "unknown"
+        )
+        if damaged_part not in expected_parts:
+            damaged_part = "other"
+
+        repair_cost = cost_dict.get(damaged_part, cost_dict["other"])
+        total_cost += repair_cost
+
+        damage_details.append({"part": damaged_part, "cost_usd": repair_cost})
+
+    response = {"damages": damage_details, "total_cost": total_cost}
+
+    return jsonify(response)
+
+
+@app.route("/fetch-image", methods=["POST"])
+def fetchImage():
+    file = None
+    if "url" in request.form:
+        url = request.form["url"]
+        response = requests.get(url)
+        file = io.BytesIO(response.content)
+    elif "file" in request.files:
+        file = request.files["file"]
+
+    # Load image
+    image = skio.imread(file)
+    image_np = image
+
+    
+
+    return jsonify(response)
+
+
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=7860)

templates/index.html

@@ -1,91 +1,110 @@
 <!DOCTYPE html>
 <html lang="en">
-<head>
-    <meta charset="UTF-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <title>Upload File</title>
-    <style>
-        body {
-            font-family: Arial, sans-serif;
-            text-align: center;
-            margin-top: 50px;
-        }
-        #preview {
-            margin-top: 20px;
-            max-width: 500px;
-            max-height: 500px;
-            display: none;
-        }
-    </style>
-</head>
-<body>
-    <h1>Vehicle Damage Detection</h1>
-    <form id="uploadForm" enctype="multipart/form-data">
-        <label for="file">Upload an image:</label>
-        <input type="file" id="file" name="file" accept="image/*" required>
-        <br><br>
-        <img id="preview" alt="Image Preview">
-        <br><br>
-        <button type="submit">Upload and Analyze</button>
-    </form>
-    <p id="response"></p>
+	<head>
+		<meta charset="UTF-8" />
+		<meta
+			name="viewport"
+			content="width=device-width, initial-scale=1.0"
+		/>
+		<title>Upload File</title>
+		<style>
+			body {
+				font-family: Arial, sans-serif;
+				text-align: center;
+				margin-top: 50px;
+			}
+			#preview {
+				margin-top: 20px;
+				max-width: 500px;
+				max-height: 500px;
+				display: none;
+			}
+		</style>
+	</head>
+	<body>
+		<h1>Vehicle Damage Detection</h1>
+		<form
+			id="uploadForm"
+			enctype="multipart/form-data"
+		>
+			<label for="file">Upload an image:</label>
+			<input
+				type="file"
+				id="file"
+				name="file"
+				accept="image/*"
+				required
+			/>
+			<br /><br />
+			<img
+				id="preview"
+				alt="Image Preview"
+			/>
+			<br /><br />
+			<button type="submit">Upload and Analyze</button>
+		</form>
+		<p id="response"></p>
 
-    <script>
-        const fileInput = document.getElementById('file');
-        const preview = document.getElementById('preview');
-        const uploadForm = document.getElementById('uploadForm');
-        const responseElement = document.getElementById('response');
+		<script>
+			const fileInput = document.getElementById("file");
+			const preview = document.getElementById("preview");
+			const uploadForm = document.getElementById("uploadForm");
+			const responseElement = document.getElementById("response");
 
-        // Preview the selected image
-        fileInput.addEventListener('change', function () {
-            const file = fileInput.files[0];
-            if (file) {
-                const reader = new FileReader();
-                reader.onload = function (e) {
-                    preview.src = e.target.result;
-                    preview.style.display = 'block';
-                };
-                reader.readAsDataURL(file);
-            } else {
-                preview.style.display = 'none';
-            }
-        });
+			// Preview the selected image
+			fileInput.addEventListener("change", function () {
+				const file = fileInput.files[0];
+				if (file) {
+					const reader = new FileReader();
+					reader.onload = function (e) {
+						preview.src = e.target.result;
+						preview.style.display = "block";
+					};
+					reader.readAsDataURL(file);
+				} else {
+					preview.style.display = "none";
+				}
+			});
 
-        // Handle form submission
-        uploadForm.addEventListener('submit', async function (event) {
-            event.preventDefault();
+			// Handle form submission
+			uploadForm.addEventListener("submit", async function (event) {
+				event.preventDefault();
 
-            const formData = new FormData();
-            formData.append('file', fileInput.files[0]);
+				const formData = new FormData();
+				formData.append("file", fileInput.files[0]);
 
-            responseElement.textContent = 'Uploading and analyzing...';
+				responseElement.textContent = "Uploading and analyzing...";
 
-            try {
-                const response = await fetch('/upload', {
-                    method: 'POST',
-                    body: formData
-                });
+				try {
+					const response = await fetch("/fetch-image", {
+						method: "POST",
+						body: formData,
+					});
 
-                if (response.ok) {
-                    const result = await response.json();
-                    responseElement.innerHTML = `
+					if (response.ok) {
+						const result = await response.json();
+						responseElement.innerHTML = `
                         <strong>Analysis Result:</strong><br>
                         Total Cost: $${result.total_cost}<br>
                         <ul>
-                            ${result.damages.map(damage => `
+                            ${result.damages
+															.map(
+																(damage) => `
                                 <li>
                                     Part: ${damage.part}, Area: ${damage.area_pixels} pixels, Cost: $${damage.cost_usd}
                                 </li>
-                            `).join('')}
+                            `
+															)
+															.join("")}
                         </ul>
                     `;
-                } else {
-                    responseElement.textContent = 'Error: Unable to analyze the image.';
-                }
-            } catch (error) {
-                responseElement.textContent = 'Error: ' + error.message;
-            }
-        });
-    </script>
-</body>
-</html>
+					} else {
+						responseElement.textContent = "Error: Unable to analyze the image.";
+					}
+				} catch (error) {
+					responseElement.textContent = "Error: " + error.message;
+				}
+			});
+		</script>
+	</body>
+</html>