Pothole detector

.gitignore

@@ -0,0 +1,2 @@
+.env
+best.pt

app.py

@@ -1,7 +1,64 @@
 import gradio as gr
+import cv2
+import requests
+import os
 
-def greet(name):
-    return "Hello " + name + "!!"
+from ultralytics import YOLO
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+file_urls = [
+    "https://www.dropbox.com/s/b5g97xo901zb3ds/pothole_example.jpg?dl=1",
+    "https://www.dropbox.com/s/86uxlxxlm1iaexa/pothole_screenshot.png?dl=1",
+    "https://www.dropbox.com/s/7sjfwncffg8xej2/video_7.mp4?dl=1",
+]
+
+
+def download_file(url, save_name):
+    url = url
+    if not os.path.exists(save_name):
+        file = requests.get(url)
+        open(save_name, "wb").write(file.content)
+
+
+for i, url in enumerate(file_urls):
+    if "mp4" in file_urls[i]:
+        download_file(file_urls[i], f"video.mp4")
+    else:
+        download_file(file_urls[i], f"image_{i}.jpg")
+
+model = YOLO("best.pt")
+path = [["image_0.jpg"], ["image_1.jpg"]]
+video_path = [["video.mp4"]]
+
+
+def show_preds_image(image_path):
+    image = cv2.imread(image_path)
+    outputs = model.predict(source=image_path)
+    results = outputs[0].cpu().numpy()
+    for i, det in enumerate(results.boxes.xyxy):
+        cv2.rectangle(
+            image,
+            (int(det[0]), int(det[1])),
+            (int(det[2]), int(det[3])),
+            color=(0, 0, 255),
+            thickness=2,
+            lineType=cv2.LINE_AA,
+        )
+    return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+
+inputs_image = [
+    gr.components.Image(type="filepath", label="Input Image"),
+]
+
+
+outputs_image = [
+    gr.components.Image(type="numpy", label="Output Image"),
+]
+interface_image = gr.Interface(
+    fn=show_preds_image,
+    inputs=inputs_image,
+    outputs=outputs_image,
+    title="Pothole detector",
+    examples=path,
+    cache_examples=False,
+)

requirements.txt

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+# Ultralytics requirements
+# Usage: pip install -r requirements.txt
+ 
+# Base ----------------------------------------
+hydra-core>=1.2.0
+matplotlib>=3.2.2
+numpy>=1.18.5
+opencv-python>=4.1.1
+Pillow>=7.1.2
+PyYAML>=5.3.1
+requests>=2.23.0
+scipy>=1.4.1
+torch>=1.7.0
+torchvision>=0.8.1
+tqdm>=4.64.0
+ultralytics
+ 
+# Logging -------------------------------------
+tensorboard>=2.4.1
+# clearml
+# comet
+ 
+# Plotting ------------------------------------
+pandas>=1.1.4
+seaborn>=0.11.0
+ 
+# Export --------------------------------------
+# coremltools>=6.0  # CoreML export
+# onnx>=1.12.0  # ONNX export
+# onnx-simplifier>=0.4.1  # ONNX simplifier
+# nvidia-pyindex  # TensorRT export
+# nvidia-tensorrt  # TensorRT export
+# scikit-learn==0.19.2  # CoreML quantization
+# tensorflow>=2.4.1  # TF exports (-cpu, -aarch64, -macos)
+# tensorflowjs>=3.9.0  # TF.js export
+# openvino-dev  # OpenVINO export
+ 
+# Extras --------------------------------------
+ipython  # interactive notebook
+psutil  # system utilization
+thop>=0.1.1  # FLOPs computation
+# albumentations>=1.0.3
+# pycocotools>=2.0.6  # COCO mAP
+# roboflow
+ 
+# HUB -----------------------------------------
+GitPython>=3.1.24