Upload 14 files

.gitattributes

@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+image_ladder.png filter=lfs diff=lfs merge=lfs -text
+image_tyre.png filter=lfs diff=lfs merge=lfs -text
+video.mp4 filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,7 @@
+flagged/
+*.pt
+*.png
+*.jpg
+*.mp4
+*.mkv
+gradio_cached_examples/

app.py

@@ -0,0 +1,326 @@
+from PIL import Image
+import io
+import pandas as pd
+import numpy as np
+
+import gradio as gr
+import cv2
+import requests
+import os
+from ultralytics import YOLO
+
+from ultralytics.utils.plotting import Annotator, colors
+from render import custom_render_result
+
+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"
+        )
+
+def get_image_from_bytes(binary_image: bytes) -> Image:
+    """Convert image from bytes to PIL RGB format
+
+    **Args:**
+        - **binary_image (bytes):** The binary representation of the image
+
+    **Returns:**
+        - **PIL.Image:** The image in PIL RGB format
+    """
+    input_image = Image.open(io.BytesIO(binary_image)).convert("RGB")
+    return input_image
+
+
+def get_bytes_from_image(image: Image) -> bytes:
+    """
+    Convert PIL image to Bytes
+
+    Args:
+    image (Image): A PIL image instance
+
+    Returns:
+    bytes : BytesIO object that contains the image in JPEG format with quality 85
+    """
+    return_image = io.BytesIO()
+    image.save(return_image, format='JPEG', quality=85)  # save the image in JPEG format with quality 85
+    return_image.seek(0)  # set the pointer to the beginning of the file
+    return return_image
+
+
+def transform_predict_to_df(results: list, labeles_dict: dict) -> pd.DataFrame:
+    """
+    Transform predict from yolov8 (torch.Tensor) to pandas DataFrame.
+
+    Args:
+        results (list): A list containing the predict output from yolov8 in the form of a torch.Tensor.
+        labeles_dict (dict): A dictionary containing the labels names, where the keys are the class ids and the values are the label names.
+
+    Returns:
+        predict_bbox (pd.DataFrame): A DataFrame containing the bounding box coordinates, confidence scores and class labels.
+    """
+    # Transform the Tensor to numpy array
+    predict_bbox = pd.DataFrame(results[0].to("cpu").numpy().boxes.xyxy, columns=['xmin', 'ymin', 'xmax', 'ymax'])
+    # Add the confidence of the prediction to the DataFrame
+    predict_bbox['confidence'] = results[0].to("cpu").numpy().boxes.conf
+    # Add the class of the prediction to the DataFrame
+    predict_bbox['class'] = (results[0].to("cpu").numpy().boxes.cls).astype(int)
+    # Replace the class number with the class name from the labeles_dict
+    predict_bbox['name'] = predict_bbox["class"].replace(labeles_dict)
+    return predict_bbox
+
+
+def get_model_predict(model: YOLO, input_image: Image, save: bool = False, image_size: int = 1248, conf: float = 0.5,
+                      augment: bool = False) -> pd.DataFrame:
+    """
+    Get the predictions of a model on an input image.
+
+    Args:
+        model (YOLO): The trained YOLO model.
+        input_image (Image): The image on which the model will make predictions.
+        save (bool, optional): Whether to save the image with the predictions. Defaults to False.
+        image_size (int, optional): The size of the image the model will receive. Defaults to 1248.
+        conf (float, optional): The confidence threshold for the predictions. Defaults to 0.5.
+        augment (bool, optional): Whether to apply data augmentation on the input image. Defaults to False.
+
+    Returns:
+        pd.DataFrame: A DataFrame containing the predictions.
+    """
+    # Make predictions
+    predictions = model.predict(
+        imgsz=image_size,
+        source=input_image,
+        conf=conf,
+        save=save,
+        augment=augment,
+        flipud=0.0,
+        fliplr=0.0,
+        mosaic=0.0,
+    )
+
+    # Transform predictions to pandas dataframe
+    predictions = transform_predict_to_df(predictions, model.model.names)
+    return predictions
+
+
+def get_model_segment(model: YOLO, input_image: Image, save: bool = False, image_size: int = 1248, conf: float = 0.25,
+                      augment: bool = False) -> pd.DataFrame:
+    """
+    Get the predictions of a model on an input image.
+
+    Args:
+        model (YOLO): The trained YOLO model.
+        input_image (Image): The image on which the model will make predictions.
+        save (bool, optional): Whether to save the image with the predictions. Defaults to False.
+        image_size (int, optional): The size of the image the model will receive. Defaults to 1248.
+        conf (float, optional): The confidence threshold for the predictions. Defaults to 0.25.
+        augment (bool, optional): Whether to apply data augmentation on the input image. Defaults to False.
+
+    Returns:
+        pd.DataFrame: A DataFrame containing the predictions.
+    """
+    # Make predictions
+    predictions = model.predict(
+        imgsz=image_size,
+        source=input_image,
+        conf=conf,
+        save=save,
+        augment=augment,
+        flipud=0.0,
+        fliplr=0.0,
+        mosaic=0.0,
+    )
+
+    # Transform predictions to pandas dataframe
+    predictions = transform_predict_to_df(predictions, model.model.names)
+    return predictions
+
+
+################################# BBOX Func #####################################
+
+def add_bboxs_on_img(image: Image, predict: pd.DataFrame()) -> Image:
+    """
+    add a bounding box on the image
+
+    Args:
+    image (Image): input image
+    predict (pd.DataFrame): predict from model
+
+    Returns:
+    Image: image whis bboxs
+    """
+    # Create an annotator object
+    annotator = Annotator(np.array(image))
+
+    # sort predict by xmin value
+    predict = predict.sort_values(by=['xmin'], ascending=True)
+
+    # iterate over the rows of predict dataframe
+    for i, row in predict.iterrows():
+        # create the text to be displayed on image
+        text = f"{row['name']}: {int(row['confidence'] * 100)}%"
+        # get the bounding box coordinates
+        bbox = [row['xmin'], row['ymin'], row['xmax'], row['ymax']]
+        # add the bounding box and text on the image
+        annotator.box_label(bbox, text, color=colors(row['class'], True))
+    # convert the annotated image to PIL image
+    return Image.fromarray(annotator.result())
+
+
+################################# Models #####################################
+
+
+def detect_sample_model(input_image: Image) -> pd.DataFrame:
+    """
+    Predict from sample_model.
+    Base on YoloV8
+
+    Args:
+        input_image (Image): The input image.
+
+    Returns:
+        pd.DataFrame: DataFrame containing the object location.
+    """
+    predict = get_model_predict(
+        model=model_sample_detect,
+        input_image=input_image,
+        save=False,
+        image_size=640,
+        augment=False,
+        conf=0.2,
+    )
+    return predict
+    
+def yoloV8_func(image: gr.Image = None,
+                image_size: int = 640,
+                conf_threshold: float = 0.4,
+                iou_threshold: float = 0.5,
+                model_name: str = 'YOLOv8-medium'):
+    """This function performs YOLOv8 object detection on the given image.
+
+    Args:
+        image (gr.Image, optional): Input image to detect objects on. Defaults to None.
+        image_size (int, optional): Desired image size for the model. Defaults to 640.
+        conf_threshold (float, optional): Confidence threshold for object detection. Defaults to 0.4.
+        iou_threshold (float, optional): Intersection over Union threshold for object detection. Defaults to 0.50.
+    """
+    # Load the YOLOv8 model from the 'best.pt' checkpoint
+    # model_path = "best.pt"
+    # model = torch.hub.load('ultralytics/yolov8', 'custom', path='/content/best.pt', force_reload=True, trust_repo=True)
+
+    # Perform object detection on the input image using the YOLOv8 model
+    results = model.predict(image,
+                            conf=conf_threshold,
+                            iou=iou_threshold,
+                            imgsz=image_size)
+
+    # Print the detected objects' information (class, coordinates, and probability)
+    box = results[0].boxes
+    #print("Object type:", box.cls)
+    #print("Coordinates:", box.xyxy)
+    #print("Probability:", box.conf)
+
+    # Render the output image with bounding boxes around detected objects
+    render = custom_render_result(model=model, image=image, result=results[0])
+    return render
+
+model = YOLO('best.pt')
+path  = [['image_tyre.png'], ['image_ladder.png']]
+video_path = [['video.mp4']]
+
+outputs_image = gr.components.Image(label="Output Image")
+
+inputs_image= [
+    gr.components.Image(label="Input Image"),
+    gr.Slider(minimum=320, maximum=1280, step=32, label="Image Size", value=640),
+    gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label="Confidence Threshold",value=0.4, info="Usual value is 0.5"),
+    gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label="IOU Threshold",value=0.5, info="Usual value greater than 0.2"),
+    gr.components.Dropdown(["YOLOv8-nano", "YOLOv8-small", "YOLOv8-medium", "YOLOv8-large", "YOLOv8-xlarge"], value="YOLOv8-medium", label="YOLOv8 Model")
+]
+
+interface_image = gr.Interface(
+    fn=yoloV8_func,
+    inputs=inputs_image,
+    outputs=[outputs_image],
+    title="NonConforming Detector",
+    examples=path,
+    cache_examples=False,
+)
+def show_preds_video(video_path):
+    cap = cv2.VideoCapture(video_path)
+    
+    conf_threshold = 0.4
+    iou_threshold = 0.5
+    image_size = 640
+    
+    while(cap.isOpened()):
+        ret, frame = cap.read()
+        if ret:
+            frame_copy = frame.copy()
+            
+            results = model.predict(frame,
+                            conf=conf_threshold,
+                            iou=iou_threshold,
+                            imgsz=image_size)
+
+            # Print the detected objects' information (class, coordinates, and probability)
+            box = results[0].boxes
+            #print("Object type:", box.cls)
+            #print("Coordinates:", box.xyxy)
+            #print("Probability:", box.conf)
+
+            # Render the output image with bounding boxes around detected objects
+            render = custom_render_result(model=model, image=frame, result=results[0])
+            yield render
+            """
+            outputs = model.predict(source=frame)
+            results = outputs[0].cpu().numpy()
+            for i, det in enumerate(results.boxes.xyxy):
+                cv2.rectangle(
+                    frame_copy,
+                    (int(det[0]), int(det[1])),
+                    (int(det[2]), int(det[3])),
+                    color=(0, 0, 255),
+                    thickness=2,
+                    lineType=cv2.LINE_AA
+                )
+            yield cv2.cvtColor(frame_copy, cv2.COLOR_BGR2RGB)
+            """
+
+inputs_video = [
+    gr.components.Video(label="Input Video"),
+
+]
+outputs_video = [
+    gr.components.Image(label="Output Image"),
+]
+interface_video = gr.Interface(
+    fn=show_preds_video,
+    inputs=inputs_video,
+    outputs=outputs_video,
+    title="NonConforming Video Detector",
+    examples=video_path,
+    cache_examples=False,
+)
+
+gr.TabbedInterface(
+    [interface_image, interface_video],
+    tab_names=['Image inference', 'Video inference']
+).queue().launch()

best.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:303de6d2dbe1848be8fcc5d40d06ed44cd50d565785f4d96fb9292e48f90e3f4
+size 52010006

render.py

@@ -0,0 +1,63 @@
+import cv2
+import numpy as np
+from sahi.utils.cv import read_image_as_pil,get_bool_mask_from_coco_segmentation
+from sahi.prediction import ObjectPrediction, PredictionScore,visualize_object_predictions
+from PIL import Image
+def custom_render_result(model,image, result,rect_th=2,text_th=2):
+    if model.overrides["task"] not in ["detect", "segment"]:
+        raise ValueError(
+            f"Model task must be either 'detect' or 'segment'. Got {model.overrides['task']}"
+        )
+
+    image = read_image_as_pil(image)
+    np_image = np.ascontiguousarray(image)
+
+    names = model.model.names
+
+    masks = result.masks
+    boxes = result.boxes
+
+    object_predictions = []
+    if boxes is not None:
+        det_ind = 0
+        for xyxy, conf, cls in zip(boxes.xyxy, boxes.conf, boxes.cls):
+            if masks:
+                img_height = np_image.shape[0]
+                img_width = np_image.shape[1]
+                segments = masks.segments
+                segments = segments[det_ind]  # segments: np.array([[x1, y1], [x2, y2]])
+                # convert segments into full shape
+                segments[:, 0] = segments[:, 0] * img_width
+                segments[:, 1] = segments[:, 1] * img_height
+                segmentation = [segments.ravel().tolist()]
+
+                bool_mask = get_bool_mask_from_coco_segmentation(
+                    segmentation, width=img_width, height=img_height
+                )
+                if sum(sum(bool_mask == 1)) <= 2:
+                    continue
+                object_prediction = ObjectPrediction.from_coco_segmentation(
+                    segmentation=segmentation,
+                    category_name=names[int(cls)],
+                    category_id=int(cls),
+                    full_shape=[img_height, img_width],
+                )
+                object_prediction.score = PredictionScore(value=conf)
+            else:
+                object_prediction = ObjectPrediction(
+                    bbox=xyxy.tolist(),
+                    category_name=names[int(cls)],
+                    category_id=int(cls),
+                    score=conf,
+                )
+            object_predictions.append(object_prediction)
+            det_ind += 1
+
+    result = visualize_object_predictions(
+        image=np_image,
+        object_prediction_list=object_predictions,
+        rect_th=rect_th,
+        text_th=text_th,
+    )
+
+    return Image.fromarray(result["image"])

requirements.txt

@@ -0,0 +1,476 @@
+# 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
+
+
+absl-py==1.4.0
+addict==2.4.0
+adjustText==0.8
+aiofiles==23.1.0
+aiohttp==3.8.4
+aiosignal==1.3.1
+alabaster==0.7.13
+albumentations==1.3.1
+alembic==1.13.0
+alibi-detect==0.11.4
+altair==5.0.1
+annotated-types==0.5.0
+antlr4-python3-runtime==4.9.3
+anyio==3.7.1
+anylabeling==0.3.3
+appdirs==1.4.4
+argon2-cffi==21.3.0
+argon2-cffi-bindings==21.2.0
+arrow==1.2.3
+astor==0.8.1
+astroid==2.15.6
+asttokens==2.2.1
+astunparse==1.6.3
+async-timeout==4.0.2
+attrs==23.1.0
+av==10.0.0
+Babel==2.12.1
+backcall==0.2.0
+bbox==0.9.4
+bbox-visualizer==0.1.0
+bce-python-sdk==0.8.87
+beautifulsoup4==4.12.2
+bleach==6.0.0
+blinker==1.6.2
+boto3==1.28.5
+botocore==1.31.5
+build==0.10.0
+cachetools==5.3.1
+catalogue==2.0.9
+certifi==2023.5.7
+cffi==1.15.1
+charset-normalizer==3.2.0
+click==8.1.5
+clip-ea==1.0
+cloudpickle==2.2.1
+clusteval==2.2.1
+clustimage==1.5.20
+cmake==3.26.4
+colorama==0.4.6
+coloredlogs==15.0.1
+colorgram.py==1.2.0
+colorlog==6.7.0
+colormap==1.0.4
+colourmap==1.1.16
+comm==0.1.3
+commonmark==0.9.1
+contourpy==1.1.0
+convcolors==2.2.0
+coverage==5.3.1
+cryptography==41.0.7
+cycler==0.11.0
+Cython==3.0.0
+darkdetect==0.8.0
+dask==2023.7.1
+datazets==0.1.9
+debugpy==1.6.7
+decorator==4.4.2
+decord==0.6.0
+DeepImageSearch==2.5
+deffcode==0.2.5
+defusedxml==0.7.1
+Deprecated==1.2.14
+dill==0.3.7
+distfit==1.7.3
+distributed==2023.7.1
+dm-tree==0.1.8
+docutils==0.17.1
+easydev==0.12.1
+echo1-coco-split==0.1.5
+efficientnet==1.0.0
+einops==0.3.2
+encord==0.1.103
+exceptiongroup==1.1.2
+executing==1.2.0
+extcolors==1.0.0
+faiss-cpu==1.7.4
+fastapi==0.100.0
+fastjsonschema==2.17.1
+ffmpegio-core==0.8.3
+ffmpy==0.3.1
+filelock==3.12.2
+fire==0.5.0
+Flask==2.3.2
+flask-babel==3.1.0
+flatbuffers==23.5.26
+flip-data==0.2.1
+fonttools==4.41.0
+fqdn==1.5.1
+frozenlist==1.4.0
+fsspec==2023.6.0
+ftfy==6.1.3
+funcy==1.18
+future==0.18.3
+gast==0.4.0
+gitdb==4.0.10
+GitPython==3.1.32
+google-auth==2.22.0
+google-auth-oauthlib==1.0.0
+google-pasta==0.2.0
+gradio==3.37.0
+gradio_client==0.2.10
+greenlet==3.0.2
+-e git+https://github.com/IDEA-Research/GroundingDINO@60d796825e1266e56f7e4e9e00e88de662b67bd3#egg=groundingdino
+grpcio==1.56.0
+h11==0.14.0
+h5py==3.9.0
+httpcore==0.17.3
+httptools==0.6.1
+httpx==0.24.1
+huggingface-hub==0.16.4
+humanfriendly==10.0
+hydra-core==1.3.2
+idna==3.4
+ijson==3.2.3
+image-classifiers==1.0.0
+image-quality==1.2.7
+ImageHash==4.3.1
+imageio==2.31.1
+imageio-ffmpeg==0.4.8
+imagesize==1.4.1
+imgaug==0.4.0
+imgviz==1.7.2
+importlib-metadata==6.8.0
+importlib-resources==6.1.0
+imutils==0.5.4
+inquirerpy==0.3.4
+ipykernel==6.24.0
+ipython==8.14.0
+ipython-genutils==0.2.0
+ipywidgets==8.0.7
+ismember==1.0.2
+isoduration==20.11.0
+isort==5.12.0
+itsdangerous==2.1.2
+jaraco.classes==3.3.0
+jax==0.4.14
+jedi==0.18.2
+Jinja2==3.1.2
+jmespath==1.0.1
+joblib==1.3.1
+json-tricks==3.16.1
+jsonpointer==2.4
+jsonschema==4.18.4
+jsonschema-specifications==2023.6.1
+jupyter==1.0.0
+jupyter-bbox-widget==0.5.0
+jupyter-console==6.6.3
+jupyter-events==0.6.3
+jupyter_client==8.3.0
+jupyter_core==5.3.1
+jupyter_server==2.7.0
+jupyter_server_terminals==0.4.4
+jupyterlab-pygments==0.2.2
+jupyterlab-widgets==3.0.8
+jupyterlab_executor==2023.1.1
+keras==2.15.0
+Keras-Applications==1.0.8
+keras-tqdm==2.0.1
+keyring==24.2.0
+kiwisolver==1.4.4
+labelme==5.2.1
+lazy-object-proxy==1.9.0
+lazy_loader==0.3
+libclang==16.0.6
+libsvm==3.23.0.4
+lightning-utilities==0.9.0
+linkify-it-py==2.0.2
+llvmlite==0.40.1
+locket==1.0.0
+loguru==0.6.0
+Mako==1.3.0
+Markdown==3.4.3
+markdown-it-py==2.2.0
+MarkupSafe==2.1.3
+matplotlib==3.7.2
+matplotlib-inline==0.1.6
+mccabe==0.7.0
+mdit-py-plugins==0.3.3
+mdurl==0.1.2
+mistune==3.0.1
+ml-dtypes==0.2.0
+more-itertools==9.1.0
+moviepy==1.0.3
+mpmath==1.3.0
+msgpack==1.0.5
+multidict==6.0.4
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+pandera==0.15.2
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+prettytable==3.8.0
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+prompt-toolkit==3.0.39
+protobuf==3.20.3
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+ptyprocess==0.7.0
+pure-eval==0.2.2
+pyasn1==0.5.0
+pyasn1-modules==0.3.0
+pybboxes==0.1.6
+pyclay-annotation-utils @ https://github.com/cm107/annotation_utils/archive/development.zip#sha256=fbab99536104fe62a02d3113af8c8a62a10242aaf4dfe6cd818e34f2d199e0c9
+pyclay-common-utils @ https://github.com/cm107/common_utils/archive/master.zip#sha256=9b2c664c8aa339a81edc087586d7cafa87c3b9a5aa91a13922e356ece148db4a
+pyclay-logger @ https://github.com/cm107/logger/archive/master.zip#sha256=cddf54ebec6ecedd65ee832afc76f500dad8fea1bc44619318147d047568348b
+pyclay-streamer @ https://github.com/cm107/streamer/archive/master.zip#sha256=8f63126e1c965a649d80972b2e7b7932252be144b0985a117c3a85bd3c1a56ba
+pycocotools==2.0.6
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+rarfile==4.0
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+rich==12.6.0
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+rsa==4.9
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+segment-anything @ git+https://github.com/facebookresearch/segment-anything.git@6fdee8f2727f4506cfbbe553e23b895e27956588
+segmentation-models==1.0.1
+semantic-version==2.10.0
+Send2Trash==1.8.2
+Shapely==1.8.5.post1
+six==1.16.0
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+sphinxcontrib-devhelp==1.0.2
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+split-folders==0.5.1
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+static-ffmpeg==2.5
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+torch==2.0.1+cu117
+torchaudio==2.0.2+cu117
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+typer==0.6.1
+types-cachetools==5.3.0.7
+types-pytz==2022.7.1.2
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+tzdata==2023.3
+uc-micro-py==1.0.2
+ultralytics==8.0.136
+umap-learn==0.5.5
+uri-template==1.3.0
+urllib3==1.26.18
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+video2images==1.3
+visualdl==2.5.3
+watchdog==2.3.1
+watchfiles==0.21.0
+wcwidth==0.2.12
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