Because we're tracking (busting) free fluid (goo) and I grew up on 80's movies.

• Direct Pixel Mask Tracking:

Instead of reconstructing the mask from tracked points using polygons, track the movement of the entire pixel mask directly. Optical flow can be applied to the entire mask, propagating pixel-level changes across frames.

• Refine the Mask Using Morphology:

After the optical flow step, apply morphological operations to clean up the mask, ensuring it accurately reflects the fluid regions.

• Adaptive Thresholding:

Implement adaptive thresholding to refine the mask after optical flow, ensuring that any noise or small artifacts are removed.

• Quick Implementation: Optical flow provides a fast and straightforward method for propagating annotations.
• Suitable for Proof of Concept: Ideal for validating the feasibility of your approach before investing more resources.
• Next Steps: If successful, consider more advanced methods like keypoint tracking or deep learning for improved accuracy.

• Input: 3-second ultrasound videos from various views, each with free fluid annotated in at least one frame.
• Goal: Propagate the annotations of free fluid from the single annotated frame to all other frames in the video to create fully annotated videos.
• Resources: Access to a reasonably powerful GPU and strong Python skills.

Annotate free fluid in all frames of each video, leveraging existing annotations as a starting point.

1. Optical Flow:

   • Description: Use optical flow to estimate motion between frames and propagate the annotated regions.
   • Tools: OpenCV’s Farneback or Lucas-Kanade optical flow implementations.
   • Advantages: Quick to set up, leverages frame-to-frame continuity, suitable for tracking amorphous regions.
   • Steps:
     1. Load Video: Read the video and the annotated frame.
     2. Compute Optical Flow: Estimate motion vectors between consecutive frames.
     3. Propagate Annotation: Use motion vectors to transform the annotated region across frames.

2. Keypoint Tracking:

   • Description: Detect keypoints within the annotated region and track these points across frames.
   • Tools: OpenCV’s KLT Tracker.
   • Advantages: Tracks specific points within the region, can handle non-rigid motion.
   • Steps:
     1. Initialize Keypoints: Detect keypoints in the annotated frame.
     2. Track Keypoints: Track these keypoints across frames.
     3. Transform Annotation: Update the region based on tracked keypoints.

3. Deep Learning with Unsupervised Learning:

   • Description: Use a pre-trained deep learning model fine-tuned on the specific task of tracking and segmentation.
   • Tools: TensorFlow, PyTorch, models like U-Net for segmentation.
   • Advantages: High accuracy, adaptable to complex motion and appearance changes.
   • Steps:
     1. Fine-Tune Model: Use the annotated frames to fine-tune a segmentation model.
     2. Predict Annotations: Use the model to predict annotations for the remaining frames.

1. Install Dependencies:

   • Install OpenCV if not already installed:

     pip install opencv-python

2. Load Video and Initial Frame:

   import cv2
   import numpy as np
   
   cap = cv2.VideoCapture('path_to_your_video.mp4')
   ret, frame1 = cap.read()
   prvs = cv2.cvtColor(frame1, cv2.COLOR_BGR2GRAY)
   
   # Load the initial annotation (replace with actual loading code)
   initial_annotation = np.array([[x1, y1], [x2, y2]])  # Example format

3. Compute Optical Flow:

   hsv = np.zeros_like(frame1)
   hsv[..., 1] = 255
   
   while cap.isOpened():
       ret, frame2 = cap.read()
       if not ret:
           break
       next = cv2.cvtColor(frame2, cv2.COLOR_BGR2GRAY)
       flow = cv2.calcOpticalFlowFarneback(prvs, next, None, 0.5, 3, 15, 3, 5, 1.2, 0)
       prvs = next
   
       # Convert flow to HSV for visualization (optional)
       mag, ang = cv2.cartToPolar(flow[..., 0], flow[..., 1])
       hsv[..., 0] = ang * 180 / np.pi / 2
       hsv[..., 2] = cv2.normalize(mag, None, 0, 255, cv2.NORM_MINMAX)
       rgb = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
       cv2.imshow('Optical Flow', rgb)
       if cv2.waitKey(1) & 0xFF == ord('q'):
           break
   
   cap.release()
   cv2.destroyAllWindows()

4. Propagate Annotation:

   def propagate_annotation(annotation, flow):
       new_annotation = []
       for point in annotation:
           x, y = point
           dx, dy = flow[int(y), int(x)]
           new_annotation.append([x + dx, y + dy])
       return np.array(new_annotation)
   
   cap = cv2.VideoCapture('path_to_your_video.mp4')
   ret, frame1 = cap.read()
   prvs = cv2.cvtColor(frame1, cv2.COLOR_BGR2GRAY)
   current_annotation = initial_annotation
   
   while cap.isOpened():
       ret, frame2 = cap.read()
       if not ret:
           break
       next = cv2.cvtColor(frame2, cv2.COLOR_BGR2GRAY)
       flow = cv2.calcOpticalFlowFarneback(prvs, next, None, 0.5, 3, 15, 3, 5, 1.2, 0)
       current_annotation = propagate_annotation(current_annotation, flow)
       prvs = next
   
       # Optional: visualize or save the propagated annotation
       for point in current_annotation:
           cv2.circle(frame2, tuple(point), 5, (0, 255, 0), -1)
       cv2.imshow('Annotated Frame', frame2)
       if cv2.waitKey(1) & 0xFF == ord('q'):
           break
   
   cap.release()
   cv2.destroyAllWindows()