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RealtimeCashewDiseaseDetection

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omartins365 commited on May 20

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893e1ba

1 Parent(s): 68c14eb

Refactor project for real-time object detection using YOLOv10; update README, add main application logic, and implement inference and utility functions.

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Files changed (6) hide show

README.md +8 -7
app.py +74 -4
index.html +340 -0
inference.py +153 -0
requirements.txt +4 -0
utils.py +237 -0

README.md CHANGED Viewed

@@ -1,14 +1,15 @@
 ---
-title: RealtimeCashewDiseaseDetection
-emoji: 📉
-colorFrom: indigo
-colorTo: indigo
 sdk: gradio
-sdk_version: 5.30.0
 app_file: app.py
 pinned: false
 license: mit
-short_description: Realtime Cashew Disease Detection with YoloV10
 ---
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

 ---
+title: Object Detection
+emoji: 📸
+colorFrom: purple
+colorTo: red
 sdk: gradio
+sdk_version: 5.16.0
 app_file: app.py
 pinned: false
 license: mit
+short_description: Use YOLOv10 to detect objects in real-time
+tags: [webrtc, websocket, gradio, secret|TWILIO_ACCOUNT_SID, secret|TWILIO_AUTH_TOKEN]
 ---
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py CHANGED Viewed

@@ -1,7 +1,77 @@
 import gradio as gr
-def greet(name):
-    return "Hello " + name + "!!"
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()

+import json
+from pathlib import Path
+import cv2
 import gradio as gr
+from fastapi import FastAPI
+from fastapi.responses import HTMLResponse
+from fastrtc import Stream, get_twilio_turn_credentials
+from gradio.utils import get_space
+from huggingface_hub import hf_hub_download
+from pydantic import BaseModel, Field
+try:
+    from demo.object_detection.inference import YOLOv10
+except (ImportError, ModuleNotFoundError):
+    from inference import YOLOv10
+cur_dir = Path(__file__).parent
+model_file = hf_hub_download(
+    repo_id="onnx-community/yolov10n", filename="onnx/model.onnx"
+)
+model = YOLOv10(model_file)
+def detection(image, conf_threshold=0.3):
+    image = cv2.resize(image, (model.input_width, model.input_height))
+    print("conf_threshold", conf_threshold)
+    new_image = model.detect_objects(image, conf_threshold)
+    return cv2.resize(new_image, (500, 500))
+stream = Stream(
+    handler=detection,
+    modality="video",
+    mode="send-receive",
+    additional_inputs=[gr.Slider(minimum=0, maximum=1, step=0.01, value=0.3)],
+    rtc_configuration=get_twilio_turn_credentials() if get_space() else None,
+    concurrency_limit=2 if get_space() else None,
+)
+app = FastAPI()
+stream.mount(app)
+@app.get("/")
+async def _():
+    rtc_config = get_twilio_turn_credentials() if get_space() else None
+    html_content = open(cur_dir / "index.html").read()
+    html_content = html_content.replace("__RTC_CONFIGURATION__", json.dumps(rtc_config))
+    return HTMLResponse(content=html_content)
+class InputData(BaseModel):
+    webrtc_id: str
+    conf_threshold: float = Field(ge=0, le=1)
+@app.post("/input_hook")
+async def _(data: InputData):
+    stream.set_input(data.webrtc_id, data.conf_threshold)
+if __name__ == "__main__":
+    import os
+    if (mode := os.getenv("MODE")) == "UI":
+        stream.ui.launch(server_port=7860)
+    elif mode == "PHONE":
+        stream.fastphone(host="0.0.0.0", port=7860)
+    else:
+        import uvicorn
+        uvicorn.run(app, host="0.0.0.0", port=7860)

index.html ADDED Viewed

	@@ -0,0 +1,340 @@

+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Object Detection</title>
+    <style>
+        body {
+            font-family: system-ui, -apple-system, sans-serif;
+            background: linear-gradient(135deg, #2d2b52 0%, #191731 100%);
+            color: white;
+            margin: 0;
+            padding: 20px;
+            height: 100vh;
+            box-sizing: border-box;
+            display: flex;
+            flex-direction: column;
+            align-items: center;
+            justify-content: center;
+        }
+        .container {
+            width: 100%;
+            max-width: 800px;
+            text-align: center;
+            display: flex;
+            flex-direction: column;
+            align-items: center;
+        }
+        .video-container {
+            width: 100%;
+            max-width: 500px;
+            aspect-ratio: 1/1;
+            background: rgba(255, 255, 255, 0.1);
+            border-radius: 12px;
+            overflow: hidden;
+            box-shadow: 0 8px 32px rgba(0, 0, 0, 0.2);
+            margin: 10px 0;
+        }
+        #video-output {
+            width: 100%;
+            height: 100%;
+            object-fit: cover;
+        }
+        button {
+            background: white;
+            color: #2d2b52;
+            border: none;
+            padding: 12px 32px;
+            border-radius: 24px;
+            font-size: 16px;
+            font-weight: 600;
+            cursor: pointer;
+            transition: all 0.3s ease;
+            box-shadow: 0 4px 12px rgba(0, 0, 0, 0.1);
+        }
+        button:hover {
+            transform: translateY(-2px);
+            box-shadow: 0 6px 16px rgba(0, 0, 0, 0.2);
+        }
+        h1 {
+            font-size: 2.5em;
+            margin-bottom: 0.3em;
+        }
+        p {
+            color: rgba(255, 255, 255, 0.8);
+            margin-bottom: 1em;
+        }
+        .controls {
+            display: flex;
+            flex-direction: column;
+            gap: 12px;
+            align-items: center;
+            margin-top: 10px;
+        }
+        .slider-container {
+            width: 100%;
+            max-width: 300px;
+            display: flex;
+            flex-direction: column;
+            gap: 8px;
+        }
+        .slider-container label {
+            color: rgba(255, 255, 255, 0.8);
+            font-size: 14px;
+        }
+        input[type="range"] {
+            width: 100%;
+            height: 6px;
+            -webkit-appearance: none;
+            background: rgba(255, 255, 255, 0.1);
+            border-radius: 3px;
+            outline: none;
+        }
+        input[type="range"]::-webkit-slider-thumb {
+            -webkit-appearance: none;
+            width: 18px;
+            height: 18px;
+            background: white;
+            border-radius: 50%;
+            cursor: pointer;
+        }
+        /* Add styles for toast notifications */
+        .toast {
+            position: fixed;
+            top: 20px;
+            left: 50%;
+            transform: translateX(-50%);
+            padding: 16px 24px;
+            border-radius: 4px;
+            font-size: 14px;
+            z-index: 1000;
+            display: none;
+            box-shadow: 0 2px 5px rgba(0, 0, 0, 0.2);
+        }
+        .toast.error {
+            background-color: #f44336;
+            color: white;
+        }
+        .toast.warning {
+            background-color: #ffd700;
+            color: black;
+        }
+    </style>
+</head>
+<body>
+    <!-- Add toast element after body opening tag -->
+    <div id="error-toast" class="toast"></div>
+    <div class="container">
+        <h1>Real-time Object Detection</h1>
+        <p>Using YOLOv10 to detect objects in your webcam feed</p>
+        <div class="video-container">
+            <video id="video-output" autoplay playsinline></video>
+        </div>
+        <div class="controls">
+            <div class="slider-container">
+                <label>Confidence Threshold: <span id="conf-value">0.3</span></label>
+                <input type="range" id="conf-threshold" min="0" max="1" step="0.01" value="0.3">
+            </div>
+            <button id="start-button">Start</button>
+        </div>
+    </div>
+    <script>
+        let peerConnection;
+        let webrtc_id;
+        const startButton = document.getElementById('start-button');
+        const videoOutput = document.getElementById('video-output');
+        const confThreshold = document.getElementById('conf-threshold');
+        const confValue = document.getElementById('conf-value');
+        // Update confidence value display
+        confThreshold.addEventListener('input', (e) => {
+            confValue.textContent = e.target.value;
+            if (peerConnection) {
+                updateConfThreshold(e.target.value);
+            }
+        });
+        function updateConfThreshold(value) {
+            fetch('/input_hook', {
+                method: 'POST',
+                headers: {
+                    'Content-Type': 'application/json',
+                },
+                body: JSON.stringify({
+                    webrtc_id: webrtc_id,
+                    conf_threshold: parseFloat(value)
+                })
+            });
+        }
+        function showError(message) {
+            const toast = document.getElementById('error-toast');
+            toast.textContent = message;
+            toast.className = 'toast error';
+            toast.style.display = 'block';
+            // Hide toast after 5 seconds
+            setTimeout(() => {
+                toast.style.display = 'none';
+            }, 5000);
+        }
+        async function setupWebRTC() {
+            const config = __RTC_CONFIGURATION__;
+            peerConnection = new RTCPeerConnection(config);
+            const timeoutId = setTimeout(() => {
+                const toast = document.getElementById('error-toast');
+                toast.textContent = "Connection is taking longer than usual. Are you on a VPN?";
+                toast.className = 'toast warning';
+                toast.style.display = 'block';
+                // Hide warning after 5 seconds
+                setTimeout(() => {
+                    toast.style.display = 'none';
+                }, 5000);
+            }, 5000);
+            try {
+                const stream = await navigator.mediaDevices.getUserMedia({
+                    video: true
+                });
+                stream.getTracks().forEach(track => {
+                    peerConnection.addTrack(track, stream);
+                });
+                peerConnection.addEventListener('track', (evt) => {
+                    if (videoOutput && videoOutput.srcObject !== evt.streams[0]) {
+                        videoOutput.srcObject = evt.streams[0];
+                    }
+                });
+                const dataChannel = peerConnection.createDataChannel('text');
+                dataChannel.onmessage = (event) => {
+                    const eventJson = JSON.parse(event.data);
+                    if (eventJson.type === "error") {
+                        showError(eventJson.message);
+                    } else if (eventJson.type === "send_input") {
+                        updateConfThreshold(confThreshold.value);
+                    }
+                };
+                const offer = await peerConnection.createOffer();
+                await peerConnection.setLocalDescription(offer);
+                await new Promise((resolve) => {
+                    if (peerConnection.iceGatheringState === "complete") {
+                        resolve();
+                    } else {
+                        const checkState = () => {
+                            if (peerConnection.iceGatheringState === "complete") {
+                                peerConnection.removeEventListener("icegatheringstatechange", checkState);
+                                resolve();
+                            }
+                        };
+                        peerConnection.addEventListener("icegatheringstatechange", checkState);
+                    }
+                });
+                webrtc_id = Math.random().toString(36).substring(7);
+                const response = await fetch('/webrtc/offer', {
+                    method: 'POST',
+                    headers: { 'Content-Type': 'application/json' },
+                    body: JSON.stringify({
+                        sdp: peerConnection.localDescription.sdp,
+                        type: peerConnection.localDescription.type,
+                        webrtc_id: webrtc_id
+                    })
+                });
+                const serverResponse = await response.json();
+                if (serverResponse.status === 'failed') {
+                    showError(serverResponse.meta.error === 'concurrency_limit_reached'
+                        ? `Too many connections. Maximum limit is ${serverResponse.meta.limit}`
+                        : serverResponse.meta.error);
+                    stop();
+                    startButton.textContent = 'Start';
+                    return;
+                }
+                await peerConnection.setRemoteDescription(serverResponse);
+                // Send initial confidence threshold
+                updateConfThreshold(confThreshold.value);
+                peerConnection.addEventListener('connectionstatechange', () => {
+                    if (peerConnection.connectionState === 'connected') {
+                        clearTimeout(timeoutId);
+                        const toast = document.getElementById('error-toast');
+                        toast.style.display = 'none';
+                    }
+                });
+            } catch (err) {
+                clearTimeout(timeoutId);
+                console.error('Error setting up WebRTC:', err);
+                showError('Failed to establish connection. Please try again.');
+                stop();
+                startButton.textContent = 'Start';
+            }
+        }
+        function stop() {
+            if (peerConnection) {
+                if (peerConnection.getTransceivers) {
+                    peerConnection.getTransceivers().forEach(transceiver => {
+                        if (transceiver.stop) {
+                            transceiver.stop();
+                        }
+                    });
+                }
+                if (peerConnection.getSenders) {
+                    peerConnection.getSenders().forEach(sender => {
+                        if (sender.track && sender.track.stop) sender.track.stop();
+                    });
+                }
+                setTimeout(() => {
+                    peerConnection.close();
+                }, 500);
+            }
+            videoOutput.srcObject = null;
+        }
+        startButton.addEventListener('click', () => {
+            if (startButton.textContent === 'Start') {
+                setupWebRTC();
+                startButton.textContent = 'Stop';
+            } else {
+                stop();
+                startButton.textContent = 'Start';
+            }
+        });
+    </script>
+</body>
+</html>

inference.py ADDED Viewed

	@@ -0,0 +1,153 @@

+import time
+import cv2
+import numpy as np
+import onnxruntime
+try:
+    from demo.object_detection.utils import draw_detections
+except (ImportError, ModuleNotFoundError):
+    from utils import draw_detections
+class YOLOv10:
+    def __init__(self, path):
+        # Initialize model
+        self.initialize_model(path)
+    def __call__(self, image):
+        return self.detect_objects(image)
+    def initialize_model(self, path):
+        self.session = onnxruntime.InferenceSession(
+            path, providers=onnxruntime.get_available_providers()
+        )
+        # Get model info
+        self.get_input_details()
+        self.get_output_details()
+    def detect_objects(self, image, conf_threshold=0.3):
+        input_tensor = self.prepare_input(image)
+        # Perform inference on the image
+        new_image = self.inference(image, input_tensor, conf_threshold)
+        return new_image
+    def prepare_input(self, image):
+        self.img_height, self.img_width = image.shape[:2]
+        input_img = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+        # Resize input image
+        input_img = cv2.resize(input_img, (self.input_width, self.input_height))
+        # Scale input pixel values to 0 to 1
+        input_img = input_img / 255.0
+        input_img = input_img.transpose(2, 0, 1)
+        input_tensor = input_img[np.newaxis, :, :, :].astype(np.float32)
+        return input_tensor
+    def inference(self, image, input_tensor, conf_threshold=0.3):
+        start = time.perf_counter()
+        outputs = self.session.run(
+            self.output_names, {self.input_names[0]: input_tensor}
+        )
+        print(f"Inference time: {(time.perf_counter() - start) * 1000:.2f} ms")
+        (
+            boxes,
+            scores,
+            class_ids,
+        ) = self.process_output(outputs, conf_threshold)
+        return self.draw_detections(image, boxes, scores, class_ids)
+    def process_output(self, output, conf_threshold=0.3):
+        predictions = np.squeeze(output[0])
+        # Filter out object confidence scores below threshold
+        scores = predictions[:, 4]
+        predictions = predictions[scores > conf_threshold, :]
+        scores = scores[scores > conf_threshold]
+        if len(scores) == 0:
+            return [], [], []
+        # Get the class with the highest confidence
+        class_ids = predictions[:, 5].astype(int)
+        # Get bounding boxes for each object
+        boxes = self.extract_boxes(predictions)
+        return boxes, scores, class_ids
+    def extract_boxes(self, predictions):
+        # Extract boxes from predictions
+        boxes = predictions[:, :4]
+        # Scale boxes to original image dimensions
+        boxes = self.rescale_boxes(boxes)
+        # Convert boxes to xyxy format
+        # boxes = xywh2xyxy(boxes)
+        return boxes
+    def rescale_boxes(self, boxes):
+        # Rescale boxes to original image dimensions
+        input_shape = np.array(
+            [self.input_width, self.input_height, self.input_width, self.input_height]
+        )
+        boxes = np.divide(boxes, input_shape, dtype=np.float32)
+        boxes *= np.array(
+            [self.img_width, self.img_height, self.img_width, self.img_height]
+        )
+        return boxes
+    def draw_detections(
+        self, image, boxes, scores, class_ids, draw_scores=True, mask_alpha=0.4
+    ):
+        return draw_detections(image, boxes, scores, class_ids, mask_alpha)
+    def get_input_details(self):
+        model_inputs = self.session.get_inputs()
+        self.input_names = [model_inputs[i].name for i in range(len(model_inputs))]
+        self.input_shape = model_inputs[0].shape
+        self.input_height = self.input_shape[2]
+        self.input_width = self.input_shape[3]
+    def get_output_details(self):
+        model_outputs = self.session.get_outputs()
+        self.output_names = [model_outputs[i].name for i in range(len(model_outputs))]
+if __name__ == "__main__":
+    import tempfile
+    import requests
+    from huggingface_hub import hf_hub_download
+    model_file = hf_hub_download(
+        repo_id="onnx-community/yolov10s", filename="onnx/model.onnx"
+    )
+    yolov8_detector = YOLOv10(model_file)
+    with tempfile.NamedTemporaryFile(suffix=".jpg", delete=False) as f:
+        f.write(
+            requests.get(
+                "https://live.staticflickr.com/13/19041780_d6fd803de0_3k.jpg"
+            ).content
+        )
+        f.seek(0)
+        img = cv2.imread(f.name)
+    # # Detect Objects
+    combined_image = yolov8_detector.detect_objects(img)
+    # Draw detections
+    cv2.namedWindow("Output", cv2.WINDOW_NORMAL)
+    cv2.imshow("Output", combined_image)
+    cv2.waitKey(0)

requirements.txt ADDED Viewed

	@@ -0,0 +1,4 @@

+fastrtc
+opencv-python
+twilio
+onnxruntime-gpu

utils.py ADDED Viewed

	@@ -0,0 +1,237 @@

+import cv2
+import numpy as np
+class_names = [
+    "person",
+    "bicycle",
+    "car",
+    "motorcycle",
+    "airplane",
+    "bus",
+    "train",
+    "truck",
+    "boat",
+    "traffic light",
+    "fire hydrant",
+    "stop sign",
+    "parking meter",
+    "bench",
+    "bird",
+    "cat",
+    "dog",
+    "horse",
+    "sheep",
+    "cow",
+    "elephant",
+    "bear",
+    "zebra",
+    "giraffe",
+    "backpack",
+    "umbrella",
+    "handbag",
+    "tie",
+    "suitcase",
+    "frisbee",
+    "skis",
+    "snowboard",
+    "sports ball",
+    "kite",
+    "baseball bat",
+    "baseball glove",
+    "skateboard",
+    "surfboard",
+    "tennis racket",
+    "bottle",
+    "wine glass",
+    "cup",
+    "fork",
+    "knife",
+    "spoon",
+    "bowl",
+    "banana",
+    "apple",
+    "sandwich",
+    "orange",
+    "broccoli",
+    "carrot",
+    "hot dog",
+    "pizza",
+    "donut",
+    "cake",
+    "chair",
+    "couch",
+    "potted plant",
+    "bed",
+    "dining table",
+    "toilet",
+    "tv",
+    "laptop",
+    "mouse",
+    "remote",
+    "keyboard",
+    "cell phone",
+    "microwave",
+    "oven",
+    "toaster",
+    "sink",
+    "refrigerator",
+    "book",
+    "clock",
+    "vase",
+    "scissors",
+    "teddy bear",
+    "hair drier",
+    "toothbrush",
+]
+# Create a list of colors for each class where each color is a tuple of 3 integer values
+rng = np.random.default_rng(3)
+colors = rng.uniform(0, 255, size=(len(class_names), 3))
+def nms(boxes, scores, iou_threshold):
+    # Sort by score
+    sorted_indices = np.argsort(scores)[::-1]
+    keep_boxes = []
+    while sorted_indices.size > 0:
+        # Pick the last box
+        box_id = sorted_indices[0]
+        keep_boxes.append(box_id)
+        # Compute IoU of the picked box with the rest
+        ious = compute_iou(boxes[box_id, :], boxes[sorted_indices[1:], :])
+        # Remove boxes with IoU over the threshold
+        keep_indices = np.where(ious < iou_threshold)[0]
+        # print(keep_indices.shape, sorted_indices.shape)
+        sorted_indices = sorted_indices[keep_indices + 1]
+    return keep_boxes
+def multiclass_nms(boxes, scores, class_ids, iou_threshold):
+    unique_class_ids = np.unique(class_ids)
+    keep_boxes = []
+    for class_id in unique_class_ids:
+        class_indices = np.where(class_ids == class_id)[0]
+        class_boxes = boxes[class_indices, :]
+        class_scores = scores[class_indices]
+        class_keep_boxes = nms(class_boxes, class_scores, iou_threshold)
+        keep_boxes.extend(class_indices[class_keep_boxes])
+    return keep_boxes
+def compute_iou(box, boxes):
+    # Compute xmin, ymin, xmax, ymax for both boxes
+    xmin = np.maximum(box[0], boxes[:, 0])
+    ymin = np.maximum(box[1], boxes[:, 1])
+    xmax = np.minimum(box[2], boxes[:, 2])
+    ymax = np.minimum(box[3], boxes[:, 3])
+    # Compute intersection area
+    intersection_area = np.maximum(0, xmax - xmin) * np.maximum(0, ymax - ymin)
+    # Compute union area
+    box_area = (box[2] - box[0]) * (box[3] - box[1])
+    boxes_area = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
+    union_area = box_area + boxes_area - intersection_area
+    # Compute IoU
+    iou = intersection_area / union_area
+    return iou
+def xywh2xyxy(x):
+    # Convert bounding box (x, y, w, h) to bounding box (x1, y1, x2, y2)
+    y = np.copy(x)
+    y[..., 0] = x[..., 0] - x[..., 2] / 2
+    y[..., 1] = x[..., 1] - x[..., 3] / 2
+    y[..., 2] = x[..., 0] + x[..., 2] / 2
+    y[..., 3] = x[..., 1] + x[..., 3] / 2
+    return y
+def draw_detections(image, boxes, scores, class_ids, mask_alpha=0.3):
+    det_img = image.copy()
+    img_height, img_width = image.shape[:2]
+    font_size = min([img_height, img_width]) * 0.0006
+    text_thickness = int(min([img_height, img_width]) * 0.001)
+    # det_img = draw_masks(det_img, boxes, class_ids, mask_alpha)
+    # Draw bounding boxes and labels of detections
+    for class_id, box, score in zip(class_ids, boxes, scores):
+        color = colors[class_id]
+        draw_box(det_img, box, color)  # type: ignore
+        label = class_names[class_id]
+        caption = f"{label} {int(score * 100)}%"
+        draw_text(det_img, caption, box, color, font_size, text_thickness)  # type: ignore
+    return det_img
+def draw_box(
+    image: np.ndarray,
+    box: np.ndarray,
+    color: tuple[int, int, int] = (0, 0, 255),
+    thickness: int = 2,
+) -> np.ndarray:
+    x1, y1, x2, y2 = box.astype(int)
+    return cv2.rectangle(image, (x1, y1), (x2, y2), color, thickness)
+def draw_text(
+    image: np.ndarray,
+    text: str,
+    box: np.ndarray,
+    color: tuple[int, int, int] = (0, 0, 255),
+    font_size: float = 0.001,
+    text_thickness: int = 2,
+) -> np.ndarray:
+    x1, y1, x2, y2 = box.astype(int)
+    (tw, th), _ = cv2.getTextSize(
+        text=text,
+        fontFace=cv2.FONT_HERSHEY_SIMPLEX,
+        fontScale=font_size,
+        thickness=text_thickness,
+    )
+    th = int(th * 1.2)
+    cv2.rectangle(image, (x1, y1), (x1 + tw, y1 - th), color, -1)
+    return cv2.putText(
+        image,
+        text,
+        (x1, y1),
+        cv2.FONT_HERSHEY_SIMPLEX,
+        font_size,
+        (255, 255, 255),
+        text_thickness,
+        cv2.LINE_AA,
+    )
+def draw_masks(
+    image: np.ndarray, boxes: np.ndarray, classes: np.ndarray, mask_alpha: float = 0.3
+) -> np.ndarray:
+    mask_img = image.copy()
+    # Draw bounding boxes and labels of detections
+    for box, class_id in zip(boxes, classes):
+        color = colors[class_id]
+        x1, y1, x2, y2 = box.astype(int)
+        # Draw fill rectangle in mask image
+        cv2.rectangle(mask_img, (x1, y1), (x2, y2), color, -1)  # type: ignore
+    return cv2.addWeighted(mask_img, mask_alpha, image, 1 - mask_alpha, 0)