Added some files

.gitignore

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+flagged/
+*.pt
+*.png
+*.jpg
+*.JPG
+*.mp4
+*.mkv
+gradio_cached_examples/

app.py

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+import gradio as gr
+import cv2 
+import requests
+import os 
+import random
+
+from ultralytics import YOLO
+
+file_urls = [
+    'https://www.dropbox.com/scl/fi/34yt1vrl4mc4n9ujdf9gm/all_76.jpg?rlkey=f7b6nq478r2m9yahcalzjzif5&dl=1',
+    'https://www.dropbox.com/scl/fi/lns6cewinp7rgf3v2g1n8/all_5.jpg?rlkey=20zvut81b829k9lg5yk8ve99z&dl=1',
+    'https://www.dropbox.com/scl/fi/13jr2f1znuzulmsyabl2f/long3.jpg?rlkey=jeyriw5a8c0t42e7y2986y53m&dl=1',
+    'https://www.dropbox.com/scl/fi/nglwcza7msjo1vu4kw27r/pot4.jpg?rlkey=1ynm35b4j100ta0p5g3fx7hx4&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'], ['image_2.jpg'], ['image_3.jpg']]
+
+path = [['IMG_7612.JPG'], ['IMG_7678.JPG'], ['all_33.jpg'], ['all_80.jpg'], 
+    ['DSC02813.JPG'], ['DSC02373.JPG']]
+
+
+# path  = [['sc_1_0 (1) (1).JPG'], ['sc_1_0 (16) (1).JPG'], 
+#         ['sc_1_0 (18) (1).JPG'], ['sc_1_0 (18).JPG']]
+
+video_path = [['VID-20230809-WA0021.mp4'], ['VID-20230809-WA0022.mp4'], 
+                ['VID-20230809-WA0024.mp4'], ['VID-20230809-WA0032.mp4']]
+
+classes = ['alligator_cracking', 'longitudinal_cracking', 'potholes', 'ravelling']
+
+def show_preds_image(image_path):
+    image = cv2.imread(image_path)
+    outputs = model.predict(source=image_path, agnostic_nms=True, conf=0.25, iou=0.4, imgsz=1024)
+    results = outputs[0].cpu().numpy()
+
+    re_boxes = results.boxes.data.tolist()
+
+    class_colors = {1 : (95, 255, 54), 2: (242, 210, 100), 3: (96, 7, 70), 4:(221, 59, 41)}
+    random.seed(42)
+    # class_colors = [(random.randint(0, 255), random.randint(0, 255), random.randint(0, 255)) for _ in range(4)]
+
+    for i, det in enumerate(results.boxes.xyxy):
+        x1, y1, x2, y2 = int(det[0]),  int(det[1]),  int(det[2]),  int(det[3])
+
+        class_label = int(re_boxes[i][-1])
+        rectangle_color = class_colors.get(class_label)
+        # rectangle_color = class_colors[class_label]
+        text_color = rectangle_color
+        cv2.rectangle(
+            image,
+            (int(det[0]), int(det[1])),
+            (int(det[2]), int(det[3])),
+            color=rectangle_color,
+            thickness=3,
+            lineType=cv2.LINE_AA
+        )
+
+        text_position = (x1, y1+100)
+        conf = re_boxes[i][-2]
+        class_name = classes[class_label]
+        # class_label = class_name.split('_')[0] + '\n' + class_name.split('_')[1] if '_' in class_name else class_name
+        cv2.putText(image, classes[class_label] + f' = {round(conf, 2)}',
+            text_position, cv2.FONT_HERSHEY_SIMPLEX, 1.5, text_color, 3)
+        
+    
+    # print(class_ids)
+    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="Asphalt Road Pavement Distresses Detector",
+    examples=path,
+    cache_examples=False,
+        description= 'This is a demo app that takes in images or videos of Asphalt pavement surfaces and \
+         \n detects the following pavement distresses: \
+         \n    \
+         \n Alligator cracking \
+        \n Longitudinal cracking \
+        \n Potholes \
+        \n Ravelling \
+        \n \
+        \n This is specifically for Inference and educational purpose.\
+        \n \
+        \n The model might ocassionaly give false outputs'
+    )
+
+def show_preds_video(video_path):
+    cap = cv2.VideoCapture(video_path)
+    while(cap.isOpened()):
+        ret, frame = cap.read()
+        if ret:
+            frame_copy = frame.copy()
+            outputs = model.predict(source=frame, agnostic_nms=True, conf=0.25, iou=0.4, imgsz=1024)
+            results = outputs[0].cpu().numpy()
+            re_boxes = results.boxes.data.tolist()
+
+            class_colors = {1 : (95, 255, 54), 2: (242, 210, 100), 3: (96, 7, 70), 4:(221, 59, 41)}
+            random.seed(42)
+            # class_colors = [(random.randint(0, 255), random.randint(0, 255), random.randint(0, 255)) for _ in range(4)]
+            
+            for i, det in enumerate(results.boxes.xyxy):
+                x1, y1, x2, y2 = int(det[0]),  int(det[1]),  int(det[2]),  int(det[3])
+
+                class_label = int(re_boxes[i][-1])
+                rectangle_color = class_colors.get(class_label)
+                # rectangle_color = class_colors[class_label]
+                text_color = rectangle_color
+
+                cv2.rectangle(
+                    frame_copy,
+                    (int(det[0]), int(det[1])),
+                    (int(det[2]), int(det[3])),
+                    color=rectangle_color,
+                    thickness=2,
+                    lineType=cv2.LINE_AA
+                )
+
+                
+                text_position = (x1, y1+100)
+                conf = re_boxes[i][-2]
+                class_name = classes[class_label]
+                # class_label = class_name.split('_')[0] + '\n' + class_name.split('_')[1] if '_' in class_name else class_name
+                cv2.putText(frame_copy, classes[class_label] + f' = {round(conf, 2)}',
+                text_position, cv2.FONT_HERSHEY_SIMPLEX, 1.5, text_color, 3)
+
+            yield cv2.cvtColor(frame_copy, cv2.COLOR_BGR2RGB)
+ 
+inputs_video = [
+    gr.components.Video(type="filepath", label="Input Video"),
+ 
+]
+outputs_video = [
+    gr.components.Image(type="numpy", label="Output Video"),
+]
+interface_video = gr.Interface(
+    fn=show_preds_video,
+    inputs=inputs_video,
+    outputs=outputs_video,
+    title="Asphalt Road Pavement Distresses Detector",
+    examples=video_path,
+    cache_examples=False,
+    # live=True
+)
+gr.TabbedInterface(
+    [interface_image, interface_video],
+    tab_names=['Image inference', 'Video inference'],
+).queue().launch()

best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d9e59dae4ba88de0caa60558da6873bc12460e12757e356e25200035c1071dc4
+size 22527918

requirements.txt

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+# Ultralytics requirements
+# Usage: pip install -r requirements.txt
+
+# Base ----------------------------------------
+matplotlib>=3.2.2
+numpy>=1.22.2 # pinned by Snyk to avoid a vulnerability
+opencv-python>=4.6.0
+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
+
+# Logging -------------------------------------
+# tensorboard>=2.13.0
+# dvclive>=2.12.0
+# clearml
+# comet
+
+# Plotting ------------------------------------
+pandas>=1.1.4
+seaborn>=0.11.0
+
+# Export --------------------------------------
+# coremltools>=7.0.b1  # CoreML export
+# onnx>=1.12.0  # ONNX export
+# onnxsim>=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)
+# tflite-support
+# tensorflowjs>=3.9.0  # TF.js export
+# openvino-dev>=2023.0  # OpenVINO export
+
+# Extras --------------------------------------
+psutil  # system utilization
+py-cpuinfo  # display CPU info
+# thop>=0.1.1  # FLOPs computation
+# ipython  # interactive notebook
+# albumentations>=1.0.3  # training augmentations
+# pycocotools>=2.0.6  # COCO mAP
+# roboflow