LittleGuy: Convolutional Model - rollthecloudinc/hedge GitHub Wiki

Main Execution Script

if __name__ == "__main__":
    # Load the pre-trained segmentation model
    model = cv2.dnn.readNetFromTensorflow('deeplabv3.pb')  # Load your chosen model here

    # Define input and output directories
    input_directory = '/data/'         # Directory containing images/videos
    output_directory = '/output/'      # Directory where processed files will be saved

    # Process the entire directory
    print("Starting processing...")
    process_directory(input_directory, output_directory, model)
    print("Processing complete! Check the output directory for results.")

1. Entry Point of the Script

if __name__ == "__main__":

  • This line ensures that the script runs only when it is executed directly (not when it is imported as a module into another script). This is a common Python idiom to organize code.

2. Loading the Pre-Trained Segmentation Model

model = cv2.dnn.readNetFromTensorflow('deeplabv3.pb')

  • **cv2.dnn.readNetFromTensorflow**

    : This function is part of OpenCV's deep neural network (

    dnn

    ) module. It loads a pre-trained TensorFlow model for use in inference.

  • **'deeplabv3.pb'**

    : This is the file path to the pre-trained model in TensorFlow's Protocol Buffer (

    .pb

    ) format. In this case, it likely refers to the DeepLabV3 model, which is commonly used for semantic segmentation tasks (i.e., identifying and labeling regions in images).

  • Once loaded, the

    model

    object can be used to perform segmentation on images or videos.

3. Defining Input and Output Directories

input_directory = '/data/' output_directory = '/output/'

  • **input_directory**

    : This is the folder where the script expects to find the input files (e.g., images or videos) that will be processed.

  • **output_directory**

    : This is the folder where the processed results will be saved after the segmentation is applied.

You would need to ensure that these directories exist and contain the appropriate files.

4. Processing the Directory

print("Starting processing...") process_directory(input_directory, output_directory, model) print("Processing complete! Check the output directory for results.")

  • **print("Starting processing...")**

    : This simply informs the user that the processing is about to begin.

  • **process_directory(input_directory, output_directory, model)**

    :

    • This function (presumably defined elsewhere in your script) is responsible for processing all the files in the

      input_directory

      and saving the results to the

      output_directory

      .

    • The function likely reads each image or video file in the input folder, applies the segmentation model (

      model

      ), and writes the processed output to the output folder.

    • The exact behavior of

      process_directory

      depends on its implementation, which is not shown here.

  • **print("Processing complete! ...")**

    : This message informs the user that the processing has finished and the results are available in the output directory.

Summary

This script:

  1. Loads a pre-trained DeepLabV3 model using OpenCV.
  2. Specifies input and output directories for processing.
  3. Processes all files in the input directory using the segmentation model and saves the results in the output directory.
  4. Provides feedback to the user about the start and completion of the processing.

If you have questions about the

process_directory

Directory Processing Function

def process_directory(input_dir, output_dir, model):
    # Organize output directories
    images_output_dir, videos_output_dir = organize_output(output_dir)

    # Iterate through files in the input directory
    for file_name in os.listdir(input_dir):
        file_path = os.path.join(input_dir, file_name)

        try:
            # Process images
            if file_name.lower().endswith(('.jpg', '.png', '.jpeg')):
                print(f"Processing image: {file_path}")
                process_image(file_path, model, images_output_dir)

            # Process videos
            elif file_name.lower().endswith(('.mp4', '.avi', '.mov', '.mkv')):
                print(f"Processing video: {file_path}")
                process_video(file_path, model, videos_output_dir)

            # Skip unsupported files
            else:
                print(f"Skipping unsupported file: {file_path}")

        except Exception as e:
            print(f"Error processing file {file_path}: {e}")

This function,

process_directory

, is responsible for processing all files in a specified input directory using a segmentation model and saving the results to the output directory. Let’s break it down step by step:

Function Definition

def process_directory(input_dir, output_dir, model):

  • input_dir

    : This is the path to the directory containing the input files (images/videos) to be processed.

  • output_dir

    : This is the path to the directory where processed files will be saved.

  • model

    : This is the pre-trained segmentation model (e.g., the DeepLabV3 model loaded earlier) that will be applied to the files in the input directory.

Step 1: Organize Output Directories

images_output_dir, videos_output_dir = organize_output(output_dir)

  • The function calls

    organize_output(output_dir)

    , which presumably:

    • Creates subdirectories within the

      output_dir

      for images and videos.

    • Returns the paths to these subdirectories (

      images_output_dir

      for processed images, and

      videos_output_dir

      for processed videos).

  • This ensures that processed files are saved in organized locations.

Step 2: Iterate Through Files in the Input Directory

for file_name in os.listdir(input_dir): file_path = os.path.join(input_dir, file_name)

  • os.listdir(input_dir)

    : Lists all files in the

    input_dir

    .

  • os.path.join(input_dir, file_name)

    : Constructs the full file path by combining the directory path and the file name.

  • The loop iterates over each file in the directory for processing.

Step 3: Process Each File

The function then checks the type of each file (image or video) and processes it accordingly:

Process Images

if file_name.lower().endswith(('.jpg', '.png', '.jpeg')): print(f"Processing image: {file_path}") process_image(file_path, model, images_output_dir)

  • file_name.lower().endswith(('.jpg', '.png', '.jpeg'))

    : Checks if the file name ends with common image file extensions (case-insensitive).

  • print(f"Processing image: {file_path}")

    : Prints a message indicating that an image file is being processed.

  • process_image(file_path, model, images_output_dir)

    :

    • This function (likely defined elsewhere) processes the image file using the segmentation model (

      model

      ).

    • The processed image is saved to the

      images_output_dir

      .

Process Videos

elif file_name.lower().endswith(('.mp4', '.avi', '.mov', '.mkv')): print(f"Processing video: {file_path}") process_video(file_path, model, videos_output_dir)

  • file_name.lower().endswith(('.mp4', '.avi', '.mov', '.mkv'))

    : Checks if the file name ends with common video file extensions (case-insensitive).

  • print(f"Processing video: {file_path}")

    : Prints a message indicating that a video file is being processed.

  • process_video(file_path, model, videos_output_dir)

    :

    • This function (likely defined elsewhere) processes the video file using the segmentation model (

      model

      ).

    • The processed video is saved to the

      videos_output_dir

      .

Skip Unsupported Files

else: print(f"Skipping unsupported file: {file_path}")

  • If the file does not match any of the recognized extensions (images or videos), it is skipped.

  • print(f"Skipping unsupported file: {file_path}")

    : Prints a message indicating that

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Certainly! Continuing from where we left off:

Step 4: Skip Unsupported Files (continued)

else: print(f"Skipping unsupported file: {file_path}")

  • If the file is not an image or video (i.e., it doesn't match the specified file extensions), it is ignored.
  • The script provides feedback to the user, indicating that the file was skipped.

Step 5: Handle Errors Gracefully

except Exception as e: print(f"Error processing file {file_path}: {e}")

  • If any error occurs while processing a file (either an image or a video), the

    try-except

    block ensures that:

    1. The script does not crash.
    2. The error is logged so the user knows which file caused the issue and what the error was.

  • Exception as e

    : Captures the error message (stored in

    e

    ) and prints it alongside the file path.

Summary of Function Behavior

  1. The

    process_directory

    function organizes the output directory by creating subdirectories for images and videos.

  2. It iterates through all files in the

    input_dir

    , checking their type based on file extensions:

    • If it's an image (

      .jpg

      ,

      .png

      ,

      .jpeg

      ), it calls

      process_image

      .

    • If it's a video (

      .mp4

      ,

      .avi

      ,

      .mov

      ,

      .mkv

      ), it calls

      process_video

      .

    • If it's neither, it skips the file and logs a message.

  3. If an error occurs during processing, the function catches the exception and logs the error, preventing the script from terminating prematurely.

Key Assumptions

  • The functions

    organize_output

    ,

    process_image

    , and

    process_video

    are defined elsewhere in the codebase and handle the actual processing tasks:

    • organize_output(output_dir)

      : Creates and returns paths to subdirectories for saving processed images and videos.

    • process_image(file_path, model, images_output_dir)

      : Applies the segmentation model to an image and saves the result to the

      images_output_dir

      .

    • process_video(file_path, model, videos_output_dir)

      : Applies the segmentation model frame-by-frame to a video and saves the processed video to the

      videos_output_dir

      .

Example Workflow

Let’s walk through an example:

  1. Input Directory (

    input_dir

    ) contains:

    • image1.jpg

      (image)

    • video1.mp4

      (video)

    • document.pdf

      (unsupported file)

  2. Output Directory (

    output_dir

    ) is initially empty.

Execution:

  • The function creates subdirectories for images and videos in

    output_dir

    (e.g.,

    /output/images/

    and

    /output/videos/

    ).

  • The script processes

    image1.jpg

    using

    process_image

    and saves the result to

    /output/images/

    .

  • It processes

    video1.mp4

    using

    process_video

    and saves the result to

    /output/videos/

    .

  • It skips

    document.pdf

    and prints a message:

    Skipping unsupported file: /data/document.pdf

    .

Output:

  • Processed files (

    image1.jpg

    and

    video1.mp4

    ) are saved in their respective subdirectories in

    output_dir

    .

  • The unsupported file (

    document.pdf

    ) is ignored.

Error Handling Example

If an error occurs while processing

image1.jpg

(e.g., file is corrupted), the script logs an error like this:

Error processing file /data/image1.jpg: [error message]

The script continues processing the remaining files (

video1.mp4

, etc.), ensuring robustness.

Video Processing Function

def process_video(video_path, model, videos_output_dir):
    try:
        # Open the video file
        cap = cv2.VideoCapture(video_path)
        if not cap.isOpened():
            print(f"Could not open video: {video_path}")
            return

        # Set up video writer for saving processed output
        output_path = os.path.join(videos_output_dir, os.path.basename(video_path))
        fourcc = cv2.VideoWriter_fourcc(*'mp4v')  # Codec for MP4 output
        fps = int(cap.get(cv2.CAP_PROP_FPS))
        frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
        frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
        out = cv2.VideoWriter(output_path, fourcc, fps, (frame_width, frame_height))

        # Process video frame by frame
        while True:
            ret, frame = cap.read()
            if not ret:
                break

            # Resize and preprocess the frame
            frame_resized = cv2.resize(frame, (512, 512))
            blob = cv2.dnn.blobFromImage(frame_resized, scalefactor=1.0/255, size=(512, 512), mean=(0, 0, 0), swapRB=True, crop=False)
            model.setInput(blob

            # Perform segmentation
            output = model.forward().squeeze().argmax(axis=0)
            navigable_mask = (output == NAVIGABLE_CLASS_ID)

            # Create navigable area visualization
            navigable_area = np.zeros_like(frame_resized)
            navigable_area[navigable_mask] = (0, 255, 0)  # Green for navigable areas
            result = cv2.addWeighted(frame_resized, 0.7, navigable_area, 0.3, 0)

            # Resize the processed frame back to the original video dimensions
            result_resized = cv2.resize(result, (frame_width, frame_height))

            # Write the processed frame to the output video
            out.write(result_resized)

        # Release video resources
        cap.release()
        out.release()
        print(f"Processed and saved video: {output_path}")

    except Exception as e:
        print(f"Error processing video {video_path}: {e}")

process_video

is responsible for processing a video file frame by frame using a segmentation model and saving the processed video to the output directory. Let’s break it down step by step:

Function Definition

def process_video(video_path, model, videos_output_dir):

  • video_path

    : The path to the input video file that needs to be processed.

  • model

    : The pre-trained segmentation model (e.g., DeepLabV3) used to analyze each video frame.

  • videos_output_dir

    : The directory where the processed video will be saved.

Step 1: Open the Video File

cap = cv2.VideoCapture(video_path) if not cap.isOpened(): print(f"Could not open video: {video_path}") return

  • cv2.VideoCapture(video_path)

    : Opens the input video file for reading.

  • cap.isOpened()

    : Checks whether the video file was successfully opened. If not, an error message is printed, and the function exits.

Step 2: Set Up Video Writer

output_path = os.path.join(videos_output_dir, os.path.basename(video_path)) fourcc = cv2.VideoWriter_fourcc(*'mp4v') # Codec for MP4 output fps = int(cap.get(cv2.CAP_PROP_FPS)) frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) out = cv2.VideoWriter(output_path, fourcc, fps, (frame_width, frame_height))

  • output_path

    : Constructs the path for the processed video. The processed video will have the same name as the original but will be saved in

    videos_output_dir

    .

  • cv2.VideoWriter_fourcc(*'mp4v')

    : Specifies the codec for saving the video in MP4 format.

  • cap.get(cv2.CAP_PROP_FPS)

    : Retrieves the frames per second (FPS) of the input video.

  • cap.get(cv2.CAP_PROP_FRAME_WIDTH)

    and

    cap.get(cv2.CAP_PROP_FRAME_HEIGHT)

    : Get the dimensions of the video frames.

  • cv2.VideoWriter(output_path, fourcc, fps, (frame_width, frame_height))

    : Initializes the video writer object (

    out

    ) to save the processed video with the same FPS and dimensions as the original video.

Step 3: Process Video Frame by Frame

while True: ret, frame = cap.read() if not ret: break

  • cap.read()

    : Reads the next frame from the video.

    • ret

      : A boolean indicating whether the frame was successfully read.

    • frame

      : The actual frame data.

  • If no frame is read (e.g., end of the video is reached), the loop exits.

Step 4: Resize and Preprocess the Frame

frame_resized = cv2.resize(frame, (512, 512)) blob = cv2.dnn.blobFromImage(frame_resized, scalefactor=1.0/255, size=(512, 512), mean=(0, 0, 0), swapRB=True, crop=False) model.setInput(blob)

  • cv2.resize(frame, (512, 512))

    : Resizes the video frame to 512x512 pixels (the input size required by the segmentation model).

  • cv2.dnn.blobFromImage(...)

    : Converts the image into a blob, which is the format the model expects.

    • **`scalef

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Certainly! Let's continue from where we left off:

Step 4 (continued): Preprocess the Frame

blob = cv2.dnn.blobFromImage( frame_resized, scalefactor=1.0/255, size=(512, 512), mean=(0, 0, 0), swapRB=True, crop=False ) model.setInput(blob)

  • cv2.dnn.blobFromImage

    :

    • scalefactor=1.0/255

      : Normalizes pixel values to the range

      [0, 1]

      (originally

      [0, 255]

      ).

    • size=(512, 512)

      : Specifies that the input to the model is a 512x512 image (the resized frame).

    • mean=(0, 0, 0)

      : No mean subtraction is applied (default).

    • swapRB=True

      : Swaps the Red and Blue channels (necessary if the model expects RGB input instead of BGR).

    • crop=False

      : Ensures no cropping is done during resizing.

  • model.setInput(blob)

    : Passes the preprocessed frame (in blob format) as input to the segmentation model.

Step 5: Perform Segmentation

output = model.forward().squeeze().argmax(axis=0) navigable_mask = (output == NAVIGABLE_CLASS_ID)

  • model.forward()

    : Runs the frame through the segmentation model to produce an output.

    • The output is typically a multi-channel tensor where each channel corresponds to a class (e.g., road, vehicle, pedestrian, etc.).

  • .squeeze()

    : Removes unnecessary dimensions from the output tensor.

  • .argmax(axis=0)

    : Finds the class with the highest probability for each pixel in the frame.

    • The result is a 2D array (same width and height as the input) where each pixel contains the ID of the predicted class.

  • navigable_mask = (output == NAVIGABLE_CLASS_ID)

    :

    • NAVIGABLE_CLASS_ID

      : This constant represents the class ID for "navigable areas" (e.g., roads).

    • The condition

      (output == NAVIGABLE_CLASS_ID)

      creates a binary mask where pixels corresponding to the navigable class are

      True

      , and all others are

      False

      .

Step 6: Create a Visualization of the Navigable Area

navigable_area = np.zeros_like(frame_resized) navigable_area[navigable_mask] = (0, 255, 0) # Green for navigable areas result = cv2.addWeighted(frame_resized, 0.7, navigable_area, 0.3, 0)

  • navigable_area = np.zeros_like(frame_resized)

    :

    • Creates an empty (black) image of the same shape as the resized frame.

  • navigable_area[navigable_mask] = (0, 255, 0)

    :

    • Pixels marked as navigable in the mask are colored green (

      (0, 255, 0)

      in BGR format).

  • cv2.addWeighted(...)

    :

    • Blends the original frame (

      frame_resized

      ) and the

      navigable_area

      visualization:

      • 0.7

        : Weight for the original frame.

      • 0.3

        : Weight for the navigable area overlay.

      • 0

        : Scalar added to each sum (not used here).

    • The result is a semi-transparent

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continue

Certainly! Let's continue from where we left off:

Step 6 (continued): Resize the Processed Frame Back to Original Dimensions

result_resized = cv2.resize(result, (frame_width, frame_height))

  • The processed frame (

    result

    ) was resized to 512x512 earlier for the model's input. Now, it is resized back to the original dimensions of the video frame (

    frame_width

    x

    frame_height

    ) so it matches the input video size.

  • cv2.resize(result, (frame_width, frame_height))

    : Resizes the processed frame to the original video dimensions.

Step 7: Write the Processed Frame to the Output Video

out.write(result_resized)

  • out.write(result_resized)

    : Writes the resized processed frame to the output video file (

    out

    ).

  • This adds the processed frame to the final video, which is saved frame by frame.

Step 8: Release Video Resources

cap.release() out.release() print(f"Processed and saved video: {output_path}")

  • cap.release()

    : Releases the input video resource, freeing memory and closing the file.

  • out.release()

    : Releases the output video resource, finalizing the saved video file.

  • print(f"Processed and saved video: {output_path}")

    : Outputs a message indicating that the video was successfully processed and saved to

    output_path

    .

Exception Handling

except Exception as e: print(f"Error processing video {video_path}: {e}")

  • If any error occurs during video processing (e.g., file not found, model fails, or the video codec is unsupported), the

    try-except

    block catches the exception.

  • print(f"Error processing video {video_path}: {e}")

    : Logs the error with details, preventing the script from crashing and allowing it to proceed with other videos.

Summary of Function Behavior

  1. Open the Video:
    • Attempts to open the input video file. If it fails, the function exits with an error message.
  2. Set Up Video Writer:
    • Configures the video writer to save the processed output in the same format, FPS, and dimensions as the input video.
  3. Process Each Frame:
    • Reads each frame from the video.
    • Resizes and preprocesses the frame for the segmentation model.
    • Performs segmentation to identify navigable areas.
    • Creates a visualization by overlaying navigable areas onto the original frame.
    • Resizes the processed frame back to the original dimensions and writes it to the output video.
  4. Finalize Processing:
    • Releases resources (input and output videos) and outputs a success message.
  5. Handle Errors Gracefully:
    • Catches and logs any issues, ensuring robustness.

Example Workflow

Imagine you are processing a video

video1.mp4

located at

/data/videos/

:

Input:

  • Video Path:

    /data/videos/video1.mp4

  • Output Directory:

    /output/videos/

  • Segmentation Model: Pre-trained DeepLabV3 model.

Execution:

  • The function opens

    video1.mp4

    and prepares the output file

    /output/videos/video1.mp4

    .

  • It processes each frame:

    • Resizes the frame to 512x512 for segmentation.
    • Identifies navigable areas (e.g., roads).
    • Creates a green overlay on navigable areas.
    • Resizes the processed frame back to the original size.

  • The processed frames are saved to the output video.

Output:

  • A processed video

    /output/videos/video1.mp4

    is created where each frame highlights navigable areas

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continue

with a semi-transparent green overlay.

Key Components and How They Work

Here’s a breakdown of the key components and their respective roles:

1. Input Video Handling (

cv2.VideoCapture

)

  • The

    cv2.VideoCapture

    object is used to read the input video frame by frame.

  • Each frame is processed individually, ensuring compatibility with videos of any length.

  • If the video file cannot be opened (e.g., due to a missing file or unsupported format), the function exits gracefully.

2. Model Inference

  • The frame is resized to 512x512 because the segmentation model expects this input size.

  • Blob Conversion: The frame is converted into a "blob," which is a batch-friendly format for models. The blob ensures pixel values are normalized and channels are ordered correctly.

  • Model Input: The preprocessed frame is passed to the segmentation model (

    model.setInput(blob)

    ), and the model predicts the segmentation mask for the frame.

3. Navigable Area Detection

  • The segmentation output is processed to identify "navigable areas" (or any other class of interest).

  • A binary mask is created where pixels belonging to the "navigable" class are marked as

    True

    .

  • This mask is used to create a green-highlighted visualization of the navigable areas.

4. Frame Overlay and Visualization

  • The original frame is blended with the navigable area visualization using

    cv2.addWeighted

    . The result is a semi-transparent overlay that highlights navigable regions.

5. Output Video Writing (

cv2.VideoWriter

)

  • The processed frame is resized back to the original video dimensions to ensure the output video matches the input.

  • The processed frame is written to the output video file using

    cv2.VideoWriter

    .

6. Resource Management

  • Both the

    VideoCapture

    object (

    cap

    ) and the

    VideoWriter

    object (

    out

    ) are properly released after processing is complete. This ensures that file handles are closed and resources are freed.

7. Exception Handling

  • The function is wrapped in a

    try-except

    block to handle unexpected errors (e.g., issues with the video file or model inference). This ensures the script does not crash if one video fails to process.

Additional Considerations

1. Performance

  • Processing videos frame by frame can be computationally expensive, especially for high-resolution videos or long durations. Consider using:
    • Hardware acceleration (e.g., GPU) for model inference.
    • Batch processing if the model and framework support it.

2. Class-Specific Customization

  • The function currently visualizes only navigable areas using a green overlay. You can modify this to visualize other classes (e.g., pedestrians, vehicles) with different colors by adjusting the class ID and mask logic.

3. Output Format

  • The output video is saved in MP4 format with the

    mp4v

    codec. Ensure the system has the necessary codecs installed. If you need a different format (e.g., AVI), you can adjust the

    fourcc

    codec appropriately.

4. Edge Cases

  • Corrupted or missing frames: Some videos may have corrupted frames. The function can skip such frames by checking

    ret

    and proceeding to the next frame.

  • Unsupported resolutions: If a video has very small or very large frame sizes, resizing to 512x512 might result in loss of detail or distortions.

Summary of Outputs

For an input video

video1.mp4

:

  • Input: Original video with frames of dimensions

    (frame_width, frame_height)

    .

  • Output: A processed video in the same resolution, where each frame has navigable areas highlighted in green.

Example Output Messages

During execution, you might see output messages like: ``

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continue

Certainly! Continuing with the example output messages that the function might produce during execution:

Example Output Messages

  1. When the video is successfully opened and processed:

    Processing video: /data/videos/video1.mp4 Processed and saved video: /output/videos/video1.mp4

    • These messages indicate that the input video was successfully opened, processed frame by frame, and saved to the output directory.

  2. When the video cannot be opened (e.g., file not found or corrupted):

    Could not open video: /data/videos/broken_video.mp4

    • This message indicates that the video file could not be opened, and no further processing was done for this file.

  3. When an error occurs during processing:

    Error processing video /data/videos/video1.mp4: [error message]

    • If an exception is raised (e.g., due to an issue with the model, a codec problem, or insufficient memory), the error is logged with the video path and the exception message.

  4. When the end of the video is reached:

    • No specific message is printed for this since the

      while

      loop naturally breaks when there are no more frames to read.

Key Scenarios

Scenario 1: Successfully Processed Video

Suppose you have a video

road.mp4

in the input directory

/data/videos/

. The processing completes without any issues, and the output video is saved to

/output/videos/

. During execution, you might see:

Processing video: /data/videos/road.mp4 Processed and saved video: /output/videos/road.mp4

Scenario 2: Unsupported Video File

If a video file is corrupted or has an unsupported format, the

cv2.VideoCapture

object may fail to open it. In this case, the function would print:

Could not open video: /data/videos/corrupted_video.mp4

Scenario 3: Error During Frame Processing

If an error occurs while processing a frame (e.g., insufficient memory during model inference), the function logs the error but continues with other videos:

Error processing video /data/videos/road.mp4: OpenCV(4.5.3) error: (-215:Assertion failed) ...

Potential Enhancements

Here are a few ideas to improve the function further:

1. Progress Tracking

  • Add a progress bar or frame count tracker to indicate how far along the processing is for each video.

  • Example (using

    tqdm

    library):

    from tqdm import tqdm total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) for _ in tqdm(range(total_frames), desc="Processing frames"): ret, frame = cap.read() if not ret: break # Process the frame as before

2. Parallel Processing

  • If you are processing multiple videos, you can use multiprocessing to process them in parallel, reducing total execution time.

  • Example (using Python's

    multiprocessing

    module):

    from multiprocessing import Pool video_paths = ['/data/videos/video1.mp4', '/data/videos/video2.mp4'] with Pool(processes=4) as pool: pool.starmap(process_video, [(path, model, videos_output_dir) for path in video_paths])

3. Handle Variable Input Sizes

  • Some videos might not require resizing to 512x512 if the segmentation model supports arbitrary input sizes. You can modify the model to handle dynamic shapes.

4. Add Output Video Metadata

  • You can include metadata (e.g., the processing date, model details) in the output video using OpenCV or a

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Certainly! Let’s continue with the remaining potential enhancements and finalize the function explanation.

4. Add Output Video Metadata

Adding metadata to the output video can be helpful for tracking details about the processing. Unfortunately, OpenCV does not directly support embedding metadata into videos, but you could include metadata indirectly, such as:

  • Saving metadata into a separate text file alongside the processed video.
  • Including metadata as an overlay (text on the video frames).

Example: Overlay Metadata on Frames

font = cv2.FONT_HERSHEY_SIMPLEX text = f"Processed with DeepLabV3 - {fps} FPS" cv2.putText(result_resized, text, (10, 50), font, 1, (255, 255, 255), 2, cv2.LINE_AA)

  • This adds a text overlay to each processed frame, displaying the model name and video FPS.

5. Improve Error Handling

Currently, the function prints error messages when exceptions occur, but you could log errors to a file for better debugging and tracking.

Example: Write Errors to a Log File

except Exception as e: error_message = f"Error processing video {video_path}: {e}" print(error_message) with open("error_log.txt", "a") as log_file: log_file.write(error_message + "\n")

  • This writes the error messages to a text file (

    error_log.txt

    ) so you can review all errors after processing.

6. Visualize Multiple Classes

The current implementation highlights only navigable areas with a green overlay. You could extend this to visualize multiple classes (e.g., pedestrians, vehicles) with different colors.

Example: Multi-Class Visualization

`pedestrian_mask = (output == PEDESTRIAN_CLASS_ID) vehicle_mask = (output == VEHICLE_CLASS_ID)

Create overlays for different classes navigable_area[navigable_mask] = (0, 255, 0) # Green for navigable areas navigable_area[pedestrian_mask] = (255, 0, 0) # Blue for pedestrians navigable_area[vehicle_mask] = (0, 0, 255) # Red for vehicles`

  • This adds overlays for pedestrians and vehicles using different colors.

7. Handle Output Directory Organization

If many videos are processed, organizing the output by subfolders (e.g., based on video names or processing dates) can improve usability.

Example: Create Subfolders for Each Video

video_name = os.path.splitext(os.path.basename(video_path))[0] video_output_dir = os.path.join(videos_output_dir, video_name) os.makedirs(video_output_dir, exist_ok=True) output_path = os.path.join(video_output_dir, os.path.basename(video_path))

  • This creates a subfolder for each video inside

    videos_output_dir

    , making it easier to manage processed outputs.

Summary of the Function's Workflow

Here’s a summarized view of how the

process_video

function operates:

  1. Open the Video File:

    • Reads the video file frame by frame using OpenCV (

      cv2.VideoCapture

      ).

  2. Set Up Video Writer:

    • Prepares the video writer (

      cv2.VideoWriter

      ) to save the processed video in MP4 format with the same FPS and dimensions as the original.

  3. Process Frames:
    • Each frame is resized, preprocessed, and passed through the segmentation model.
    • Navigable areas are identified, visualized with overlays, and blended with the original frame.
    • The processed frame is resized back to the original dimensions and saved to the output video.
  4. Release Resources:

    • Releases input (

      cap

      ) and output (

      out

      ) video resources after processing is

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Certainly! Let's wrap up the explanation and provide some additional insights about this function.

Summary of Workflow (continued)

  1. Error Handling:

    • Uses a

      try-except

      block to catch any issues during video processing, such as unsupported video codecs, model errors, or memory issues.

    • Logs errors to the console, and you can further enhance this by saving errors to a log file for debugging.

  2. Output:

    • Saves the processed video with highlighted navigable areas (or other classes) to the specified output directory (

      videos_output_dir

      ).

    • Ensures the output video retains the original resolution, FPS, and format.

Benefits of the Function

  1. Frame-by-Frame Processing:

    • Allows detailed analysis and segmentation on each frame independently, ensuring accuracy.

  2. Model Integration:

    • The function integrates seamlessly with pre-trained segmentation models (like DeepLabV3), making it modular and reusable for other models.

  3. Flexible Output Handling:

    • You can save the processed video in various formats and resolutions, depending on the codec and writer settings.

  4. Robustness:

    • Gracefully handles errors without crashing the script, ensuring reliability even when encountering problematic videos.

  5. Customizable Visualization:

    • The visualization of navigable areas can be extended to include multiple classes or other visual effects, adapting to different use cases.

Potential Use Cases

This function is versatile and can be used in various applications, such as:

  1. Autonomous Driving:

    • Highlighting navigable areas (roads) to assist in navigation and decision-making.
    • Visualizing pedestrians and vehicles for obstacle detection.

  2. Robotics:

    • Helping robots identify paths or zones within an environment for movement.

  3. Surveillance:

    • Analyzing video footage to detect specific classes, such as intruders or restricted areas.

  4. Video Editing:

    • Applying custom overlays (e.g., highlighting areas of interest) to enhance video content.

  5. Research and Prototyping:

    • Testing segmentation models on real-world video footage to evaluate their performance.

Future Improvements

Here are some additional ideas to further improve the

process_video

function:

1. Add Real-Time Processing:

  • If you are working with live video streams (e.g., from a camera), you can modify the function to process frames in real time.

cap = cv2.VideoCapture(0) # Access the camera stream

2. Optimize for Performance:

  • Use GPU acceleration for model inference by leveraging frameworks like TensorFlow, PyTorch, or OpenCV's CUDA support.
  • Example:

cv2.dnn.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA) cv2.dnn.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA)

3. Add Metrics Calculation:

  • Compute metrics (e.g., percentage of navigable area, average frame processing time) for each video and save them to a report.

4. Save Intermediate Results:

  • Save individual frame results (e.g., segmented masks) alongside the processed video for further analysis.

5. Multi-Resolution Support:

  • Make the function adaptable to different input resolutions without requiring resizing to 512x512.

Example Output for a Full Workflow

Input:

  • Video:

    /data/videos/highway.mp4

  • Output Directory:

    /output/videos/

Execution Messages:

Processing video: /data/videos/highway.mp4 Processed and saved video: /output/videos/highway.mp4

Output:

  • Processed video saved at

    /output/videos/highway.mp4

    , with green overlays highlighting navig

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Certainly! Let's finalize the explanation of the full workflow and provide some additional insights into the output and enhancements you can make for your use case.

Final Workflow Summary

Input Video:

  • The video file (

    /data/videos/highway.mp4

    ) contains frames that are processed individually using a pre-trained segmentation model.

Execution:

  • The function:

    1. Opens the video file using

      cv2.VideoCapture

      .

    2. Reads each frame, resizes it to the input dimensions required by the segmentation model (512x512), and preprocesses it into a "blob" format.

    3. Passes the frame through the segmentation model to identify navigable areas and generates a binary mask.

    4. Creates a green overlay for navigable areas and blends it with the original frame.

    5. Resizes the processed frame back to the original resolution of the video and writes it to an output video file using

      cv2.VideoWriter

      .

    6. Releases resources (

      cap

      and

      out

      ) when processing is complete.

Output Video:

  • A processed video (

    /output/videos/highway.mp4

    ) is created, where each frame highlights navigable areas (or other classes) using a semi-transparent green overlay.

Example Use Case: Autonomous Driving

If the function is applied to a video from a dashcam or a simulated driving scenario, the output video could look like this:

  1. Input: A regular video showing a road scene.
  2. Output: The same video with navigable areas (e.g., the road) highlighted in green, assisting in visualizing which parts of the frame are safe for driving.

Potential Enhancements for Output

1. Overlay Additional Classes

You can expand the visualization to include multiple classes of interest (e.g., pedestrians, vehicles). For example:

pedestrian_mask = (output == PEDESTRIAN_CLASS_ID) vehicle_mask = (output == VEHICLE_CLASS_ID) navigable_area[navigable_mask] = (0, 255, 0) # Green for navigable areas navigable_area[pedestrian_mask] = (255, 0, 0) # Blue for pedestrians navigable_area[vehicle_mask] = (0, 0, 255) # Red for vehicles

This would result in a processed video with different overlays for roads (green), pedestrians (blue), and vehicles (red).

2. Add Real-Time Visualization

If using live camera streams, you can display the processed frames in real-time using:

cv2.imshow("Processed Frame", result_resized) if cv2.waitKey(1) & 0xFF == ord('q'): break # Exit loop on 'q' key press

This makes the function suitable for real-time applications like robotics or autonomous vehicle systems.

3. Save Segmentation Masks

In addition to the processed video, save the raw segmentation masks for each frame. These masks can be useful for training or further analysis:

mask_output_path = os.path.join(videos_output_dir, f"{frame_idx}_mask.png") cv2.imwrite(mask_output_path, output.astype(np.uint8))

This saves the segmentation mask for each frame as an image file.

4. Generate Reports

Create a summary report for each video, including:

  • Total number of frames.
  • Percentage of navigable area in each frame.
  • Average frame processing time. Example:

with open("summary_report.txt", "a") as report: report.write(f"Video: {video_path}\n") report.write(f"Total Frames: {total_frames}\n") report.write(f"Average Processing Time: {avg_time_per_frame} ms\n")

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Certainly! Let’s continue and finalize the explanation with more details about reporting, performance optimizations, and potential improvements for the

process_video

function.

5. Include Visual Metrics in the Output Video

Instead of saving metrics in a separate report, you can overlay them directly onto the processed video for real-time insights. For example:

`# Overlay metrics like frame number and navigable area percentage frame_number = int(cap.get(cv2.CAP_PROP_POS_FRAMES)) navigable_percentage = (navigable_mask.sum() / (512 * 512)) * 100 # Percentage of navigable area

Add text overlay for metrics font = cv2.FONT_HERSHEY_SIMPLEX cv2.putText(result_resized, f"Frame: {frame_number}", (10, 30), font, 1, (255, 255, 255), 2, cv2.LINE_AA) cv2.putText(result_resized, f"Navigable Area: {navigable_percentage:.2f}%", (10, 70), font, 1, (255, 255, 255), 2, cv2.LINE_AA)`

This would add frame-specific information such as the frame number and the percentage of the frame that is navigable directly onto the processed video.

Performance Optimizations

1. Use GPU Acceleration

If your system has a compatible GPU, you can significantly improve performance by leveraging GPU acceleration for the segmentation model. For example:

cv2.dnn.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA) cv2.dnn.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA)

This tells OpenCV to use CUDA (NVIDIA GPU) for model inference, speeding up the processing of each frame.

2. Process Frames in Batches

If the segmentation model supports batch processing, you can process multiple frames at once instead of one frame at a time. This reduces the overhead of repeatedly setting model input and calling

model.forward()

.

3. Downscale for Faster Processing

If high accuracy is not critical, you can downscale the input video frames to a smaller resolution (e.g., 256x256) to speed up processing:

frame_resized = cv2.resize(frame, (256, 256))

This reduces the computational cost of both resizing and model inference.

4. Use Asynchronous Processing

If the I/O and processing are bottlenecks, you can use multithreading or multiprocessing to read frames, process them, and write output simultaneously. For example:

  • Use a producer-consumer pattern to read frames in one thread and process them in another.

  • Use Python’s

    multiprocessing

    to parallelize the processing of multiple videos.

6. Error Recovery and Logging

If a frame or video file causes an error, you can implement better recovery mechanisms to ensure the script continues processing the remaining files.

Example: Skip Faulty Frames

If a single frame causes an issue (e.g., due to corruption), you can skip it without halting the entire video processing:

try: # Process the frame frame_resized = cv2.resize(frame, (512, 512)) # (Remaining processing steps...) except Exception as e: print(f"Error processing frame {frame_number}: {e}") continue

Logging to File

You can log errors and processing details to a file for easier debugging:

with open("process_log.txt", "a") as log_file: log_file.write(f"Processed video: {video_path}\n") log_file.write(f"Error: {error_message}\n")

Example Workflow: Complete Script

Let’s summarize a complete workflow example:

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Certainly! Continuing with the complete script workflow example:

Example Workflow: Complete Script

Input Setup

  1. Input Video:

    /data/videos/highway.mp4

  2. Output Directory:

    /output/videos/

  3. Segmentation Model: Pre-trained DeepLabV3 model loaded earlier.

Execution Steps

  1. The script opens the input video and initializes the output video writer.
  2. Each frame is:
    • Resized to 512x512 for model inference.
    • Processed using the segmentation model to identify navigable areas.
    • Visualized with a green overlay for navigable regions.
    • Resized back to the original resolution and written to the output video.
  3. The script handles errors gracefully:
    • Skips corrupted frames or unsupported videos.
    • Logs errors to a file for debugging.
  4. Once all frames are processed, the input and output video resources are released, and the processed video is saved.

Output

  • A processed video

    /output/videos/highway.mp4

    is generated, where each frame highlights navigable areas with a semi-transparent green overlay.

Final Example Code

Here’s an updated and complete implementation of the

process_video

function with added enhancements:

`import cv2 import numpy as np import os NAVIGABLE_CLASS_ID = 1 # Example: Class ID for navigable areas (modify as needed)
def process_video(video_path, model, videos_output_dir): try: # Open the video file cap = cv2.VideoCapture(video_path) if not cap.isOpened(): print(f"Could not open video: {video_path}") return

Set up video writer for saving processed output output_path = os.path.join(videos_output_dir, os.path.basename(video_path)) fourcc = cv2.VideoWriter_fourcc(*'mp4v') # Codec for MP4 output fps = int(cap.get(cv2.CAP_PROP_FPS)) frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) out = cv2.VideoWriter(output_path, fourcc, fps, (frame_width, frame_height))

Process video frame by frame frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) print(f"Processing video: {video_path} ({frame_count} frames)")

frame_number = 0 while True: ret, frame = cap.read() if not ret: break
try: # Resize and preprocess the frame frame_resized = cv2.resize(frame, (512, 512)) blob = cv2.dnn.blobFromImage( frame_resized, scalefactor=1.0 / 255, size=(512, 512), mean=(0, 0, 0), swapRB=True, crop=False ) model.setInput(blob)

Perform segmentation output = model.forward().squeeze().argmax(axis=0) navigable_mask = (output == NAVIGABLE_CLASS_ID)

Create navigable area visualization navigable_area = np.zeros_like(frame_resized) navigable_area[navigable_mask] = (0, 255, 0) # Green for navigable areas result = cv2.addWeighted(frame_resized, 0.7, navigable_area, 0.3, 0)

Resize the processed frame back to the original video dimensions result_resized = cv2.resize(result, (frame_width, frame_height))

Overlay frame metadata (optional) font = cv2.FONT_HERSHEY_SIMPLEX

navigable_percentage = (navigable_mask.sum() /`

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Certainly! Let’s continue and finalize the complete function implementation:

Final Example Code (continued)

`navigable_percentage = (navigable_mask.sum() / (512 * 512)) * 100 cv2.putText(result_resized, f"Frame: {frame_number}", (10, 30), font, 1, (255, 255, 255), 2, cv2.LINE_AA) cv2.putText(result_resized, f"Navigable Area: {navigable_percentage:.2f}%", (10, 70), font, 1, (255, 255, 255), 2, cv2.LINE_AA)

Write the processed frame to the output video out.write(result_resized) frame_number += 1

except Exception as frame_error: print(f"Error processing frame {frame_number} in video {video_path}: {frame_error}") continue

Release video resources cap.release() out.release() print(f"Processed and saved video: {output_path}")

except Exception as e: print(f"Error processing video {video_path}: {e}") with open("error_log.txt", "a") as log_file: log_file.write(f"Error processing video {video_path}: {e}\n")`

Key Enhancements in the Final Code

  1. Frame Metadata Overlay:

    • Displays the frame number and the percentage of navigable area directly on each processed frame.
    • Helps visualize frame-by-frame metrics in the video output.

  2. Error Handling for Frames:

    • If an error occurs while processing an individual frame (e.g., due to invalid data), the function skips the frame and continues processing the rest of the video.
    • Logs specific frame errors to the console for debugging.

  3. Video Error Logging:

    • If an error occurs at the video level (e.g., unsupported format, codec issue), it is logged to

      error_log.txt

      to preserve details for troubleshooting.

  4. Progress Tracking:

    • Prints the total number of frames in the video and the current frame being processed, giving the user a sense of progress.

  5. Robust Resource Management:

    • Ensures that video resources (

      cap

      and

      out

      ) are released even if errors occur, preventing resource leaks.

Example Output

Here’s what you might see during execution:

Console Output

Processing video: /data/videos/highway.mp4 (500 frames) Processed and saved video: /output/videos/highway.mp4

If an error occurs during frame processing:

Error processing frame 25 in video /data/videos/highway.mp4: OpenCV(4.5.3) error: ...

If the video cannot be processed:

Error processing video /data/videos/corrupted_video.mp4: [error details]

Log File (

error_log.txt

)

Error processing video /data/videos/corrupted_video.mp4: Could not open file. Error processing frame 25 in video /data/videos/highway.mp4: OpenCV(4.5.3) error: ...

Sample Visualization

The output video (

/output/videos/highway.mp4

) would look like this:

  1. Overlayed Navigable Areas:

    • Green areas highlight navigable regions on the road.
    • The rest of the frame remains unaltered.

  2. Frame Metadata:

    • Frame number and navigable area percentage are displayed at the top-left corner.

Further Enhancements

  1. Parallel Video Processing:

    • Use Python's

      multiprocessing

      to process multiple videos simultaneously to reduce overall runtime.

  2. **Support for Variable

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Certainly! Let’s finalize the additional enhancements and conclude the explanation.

Further Enhancements (continued)

1. Parallel Video Processing

If you have multiple videos to process, you can use Python's

multiprocessing

module to process them in parallel. This reduces the total runtime significantly, especially for long videos.

Example: Using

multiprocessing

from multiprocessing import Pool def process_videos_in_parallel(video_paths, model, videos_output_dir): with Pool(processes=4) as pool: # Adjust the number of processes based on your system pool.starmap(process_video, [(video_path, model, videos_output_dir) for video_path in video_paths])

  • Pool.starmap

    : Allows passing multiple arguments to the

    process_video

    function for each video file.

  • This will process up to 4 videos at a time (or more, depending on the number of processes specified).

2. Support for Variable Input Sizes

Currently, the function resizes all frames to 512x512 before passing them to the segmentation model. If the model supports dynamic input sizes, you can adapt the function to work with the original video resolution instead of resizing.

Example: Avoid Fixed Resizing

frame_resized = cv2.resize(frame, (frame_width, frame_height)) blob = cv2.dnn.blobFromImage(frame_resized, scalefactor=1.0/255, size=(frame_width, frame_height), mean=(0, 0, 0), swapRB=True, crop=False) model.setInput(blob)

This approach avoids resizing to 512x512, ensuring the output segmentation mask matches the original resolution directly.

3. Provide Detailed Reports

Generate a summary report for each video detailing:

  • Total frames processed.
  • Average percentage of navigable area across all frames.
  • Total processing time for the video.
Example: Save Report to File

`import time start_time = time.time()

Process the video as before...

end_time = time.time() total_time = end_time - start_time with open("summary_report.txt", "a") as report: report.write(f"Video: {video_path}\n") report.write(f"Total Frames: {frame_number}\n") report.write(f"Average Navigable Area: {avg_navigable_percentage:.2f}%\n") report.write(f"Total Processing Time: {total_time:.2f} seconds\n")`

4. Add Real-Time Output Display

If you want to visualize the processed video while it’s being created, you can display the processed frames in a window:

Example: Real-Time Frame Display

cv2.imshow("Processed Frame", result_resized) if cv2.waitKey(1) & 0xFF == ord('q'): # Press 'q' to quit break

This allows you to monitor the processing in real time. After processing is complete, you can close the display window:

cv2.destroyAllWindows()

5. Save Individual Frame Outputs

In addition to saving the processed video, you can save each processed frame as an image for further analysis or debugging.

Example: Save Frames as Images

frame_output_path = os.path.join(videos_output_dir, f"{frame_number:04d}.png") cv2.imwrite(frame_output_path, result_resized)

This will save each processed frame as an image file in the output directory, numbered sequentially.

Final Thoughts

The

process_video

function is a robust and modular solution for applying a segmentation model to video files. With the enhancements discussed, you can make it suitable for various scenarios, such as:

  • Real-time applications like robotics and autonomous driving

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Certainly! Let’s finalize the explanation and summarize everything to provide a complete picture of the

process_video

function and its potential applications.

Final Thoughts (continued)

With the enhancements discussed, the

process_video

function becomes a versatile tool for handling video segmentation tasks. Its modular design allows it to be adapted for a wide variety of applications, such as:

Applications

  1. Autonomous Driving:

    • Highlight navigable areas (roads) in dashcam footage or simulated driving scenarios.
    • Detect pedestrians or vehicles for obstacle avoidance.

  2. Robotics:

    • Enable robots to identify paths or zones in a controlled environment for safe navigation.

  3. Surveillance and Security:

    • Analyze video footage to detect restricted areas or intruders.
    • Highlight classes like vehicles or people for monitoring activities.

  4. Video Editing and Post-Processing:

    • Add overlays to video content for educational, research, or entertainment purposes.

  5. Research and Development:

    • Test and evaluate segmentation models on real-world video data.
    • Use the processed outputs for training datasets or performance benchmarking.

  6. Live Applications:

    • Process camera streams in real-time for applications like automated inspections or live event monitoring.

Key Features

The

process_video

function provides:

  1. Frame-by-Frame Processing:
    • Ensures detailed analysis of each frame using the segmentation model.
  2. Error Handling:
    • Gracefully handles errors at both frame and video levels, ensuring the script continues running for other files.
  3. Customizable Visualization:
    • Provides overlays for navigable areas, and can easily be extended to visualize multiple classes.
  4. Output Management:
    • Saves processed videos while maintaining the original resolution, FPS, and format.
  5. Logging and Reporting:
    • Logs errors and generates detailed reports for debugging and performance tracking.

Potential Enhancements

Beyond the current implementation, there are several ways to extend the function:

  1. Parallel Processing:
    • Use multiprocessing to process multiple videos simultaneously.
  2. Real-Time Visualization:
    • Display processed frames in real-time for live applications.
  3. Frame-Level Output:
    • Save individual frames as images for further analysis or training datasets.
  4. Dynamic Input Sizes:
    • Adapt the function to handle videos without fixed resizing, ensuring compatibility with models that support dynamic input dimensions.
  5. Performance Optimizations:
    • Use GPU acceleration (via CUDA) or batch processing to speed up inference.

Sample Script Workflow

Here’s an example of how the full workflow might look when applied to multiple videos:

Input Directory:

/data/videos/

contains:

  • video1.mp4

  • video2.mp4

  • corrupted_video.avi

Output Directory:

/output/videos/

Execution:

  1. The script processes each video one by one, skipping unsupported or corrupted files.
  2. During processing:

    • Each frame is analyzed, navigable areas are highlighted, and metadata (e.g., frame number, navigable area percentage) is overlayed.

    • Errors are logged to an

      error_log.txt

      file.

Output:

  1. Processed videos saved to

    /output/videos/

    :

    • video1.mp4

    • video2.mp4

  2. Errors logged for unsupported files:

    • corrupted_video.avi

Final Example Output

Console Output:

`Processing video: /data/videos/video1.mp4 (300 frames) Processed and saved video: /output/videos/video1.mp4

Processing video: /data/videos/video2.mp4 (500 frames) Processed and saved video: /output/videos/video2.mp4

Could not open`

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Certainly! Let’s complete the final example output and summarize everything for clarity.

Final Example Output (continued)

Console Output (continued):

Could not open video: /data/videos/corrupted_video.avi Error processing video /data/videos/corrupted_video.avi: File not found or unsupported format.

Log File (

error_log.txt

):

Error processing video /data/videos/corrupted_video.avi: File not found or unsupported format.

Generated Files:

  1. Processed Videos:

    • /output/videos/video1.mp4

      :

      • Each frame highlights navigable areas with a green overlay.
      • Metadata (frame number, navigable area percentage) is displayed on the top-left corner.

    • /output/videos/video2.mp4

      :

      • Similarly processed as

        video1.mp4

        .

  2. Error Log:

    • error_log.txt

      contains details of videos that could not be processed (e.g., corrupted files).

  3. Optional Frame Outputs:

    • If implemented, individual processed frames are saved as PNG files in

      /output/videos/

      .

Key Takeaways

Strengths of the Function:

  1. Modularity:
    • Handles individual frames independently, making it adaptable to various segmentation models and use cases.
  2. Robust Error Handling:
    • Skips unsupported files and faulty frames while continuing processing without crashing.
  3. Customizable Visualizations:
    • Easily extendable to visualize multiple classes (e.g., pedestrians, vehicles) with overlays.
  4. Detailed Output:
    • Saves processed videos and logs errors for debugging.

Areas for Improvement

  1. Performance:
    • GPU acceleration or parallel processing for faster video segmentation.
  2. Dynamic Input Handling:
    • Support variable input sizes for models that don’t require fixed dimensions.
  3. Enhanced Reporting:
    • Generate detailed reports summarizing processing metrics (e.g., average navigable area percentage, processing time).

Conclusion

The

process_video

function is a powerful tool for performing video segmentation tasks using pre-trained deep learning models. Its versatility makes it suitable for a wide range of applications, from autonomous driving to video editing and research. By implementing the suggested enhancements, you can further optimize its performance, usability, and output quality.

Process Image

def process_image(image_path, model, images_output_dir):
    try:
        # Read the image
        frame = cv2.imread(image_path)
        if frame is None:
            print(f"Could not read image: {image_path}")
            return

        # Resize and preprocess the image
        frame_resized = cv2.resize(frame, (512, 512))
        blob = cv2.dnn.blobFromImage(frame_resized, scalefactor=1.0/255, size=(512, 512), mean=(0, 0, 0), swapRB=True, crop=False)
        model.setInput(blob)

        # Perform segmentation
        output = model.forward().squeeze().argmax(axis=0)
        navigable_mask = (output == NAVIGABLE_CLASS_ID)

        # Create navigable area visualization
        navigable_area = np.zeros_like(frame_resized)
        navigable_area[navigable_mask] = (0, 255, 0)  # Green for navigable areas
        result = cv2.addWeighted(frame_resized, 0.7, navigable_area, 0.3, 0)

        # Save the processed image
        output_path = os.path.join(images_output_dir, os.path.basename(image_path))
        cv2.imwrite(output_path, result)
        print(f"Processed and saved image: {output_path}")

    except Exception as e:
        print(f"Error processing image {image_path}: {e}")

Directory Organization

import os
import cv2
import numpy as np

# Load the pre-trained segmentation model
model = cv2.dnn.readNetFromTensorflow('deeplabv3.pb')

# Define class ID for navigable areas
# @todo: missing

# Input and Output directories
input_directory = '/data/'         # Directory containing images/videos
output_directory = '/output/'      # Directory where processed files will be saved

# Define navigable class ID (replace with your model's class ID for "ground" or "safe navigation")
NAVIGABLE_CLASS_ID = 0

# Create organized output directories for images and videos
def organize_output(output_dir):
    images_output_dir = os.path.join(output_dir, "images")
    videos_output_dir = os.path.join(output_dir, "videos")
    os.makedirs(images_output_dir, exist_ok=True)
    os.makedirs(videos_output_dir, exist_ok=True)
    return images_output_dir, videos_output_dir