python code for scenario We have a task to detect and/or segment fish food on images. The task is to create a detector to reliably detect these fish food (pellets) on provided video. a clip of the problem where fish pellets appear which we wish to detect. The problem is twofold: 1. Detect them on high-contrast backgrounds (sea background) 1. Detect them on low-contrast backgrounds (overlayed on a fish)

To address the problem of detecting fish food (pellets) in images, particularly on both high-contrast and low-contrast backgrounds, we can use deep learning techniques such as object detection with convolutional neural networks (CNNs). Below is a Python code example using the TensorFlow library to create a fish food detector:

python
import cv2
import numpy as np
import tensorflow as tf
from tensorflow.keras.applications import MobileNetV2
from tensorflow.keras.layers import Conv2D, Dense, Flatten, MaxPooling2D
from tensorflow.keras.models import Sequential
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.losses import BinaryCrossentropy

# Define a function to load and preprocess the image
def preprocess_image(image_path):
    image = cv2.imread(image_path)
    image = cv2.resize(image, (224, 224))  # Resize image to match MobileNetV2 input shape
    image = image / 255.0  # Normalize pixel values to [0, 1]
    return image

# Load pre-trained MobileNetV2 model
base_model = MobileNetV2(input_shape=(224, 224, 3), include_top=False, weights='imagenet')

# Freeze base model layers
base_model.trainable = False

# Add custom head to the base model for binary classification
model = Sequential([
    base_model,
    Conv2D(32, (3, 3), activation='relu'),
    MaxPooling2D((2, 2)),
    Flatten(),
    Dense(64, activation='relu'),
    Dense(1, activation='sigmoid')
])

# Compile the model
model.compile(optimizer=Adam(), loss=BinaryCrossentropy(), metrics=['accuracy'])

# Load trained weights
model.load_weights('fish_food_detector_weights.h5')

# Function to detect fish food in an image
def detect_fish_food(image):
    # Preprocess image
    processed_image = preprocess_image(image)
    # Add batch dimension
    processed_image = np.expand_dims(processed_image, axis=0)
    # Predict probability
    probability = model.predict(processed_image)[0][0]
    # Threshold probability to classify as fish food or not
    if probability >= 0.5:
        return True, probability
    else:
        return False, probability

# Test the detector on sample images
image_paths = ['image1.jpg', 'image2.jpg', 'image3.jpg']
for image_path in image_paths:
    is_fish_food, probability = detect_fish_food(image_path)
    print(f'Image: {image_path}, Fish food detected: {is_fish_food}, Probability: {probability}')

In this code:

• We use the MobileNetV2 pre-trained model as the base model and add a custom head for binary classification of fish food.
• The preprocess_image function is used to load and preprocess the input image.
• The detect_fish_food function takes an image as input, preprocesses it, and then uses the trained model to predict whether fish food is present in the image.
• We load pre-trained weights for the model from a file (fish_food_detector_weights.h5).
• We test the detector on sample images and print the results.

You need to train the model using a dataset containing images with fish food (positive samples) and without fish food (negative samples). After training, save the weights to a file and use them to load the trained model for inference. Additionally, you may need to fine-tune the model and experiment with different architectures to improve performance on both high-contrast and low-contrast backgrounds.

======================================================================

 Here's some Python code to get you started with fish food detection in videos using a Convolutional Neural Network (CNN):

Python

Things to Consider:

• Preprocessing: Implement logic to resize and normalize frames for consistency across video quality and lighting conditions.
• Model Training: Train your CNN model on a dataset containing images and videos with labeled fish food in both high-contrast and low-contrast backgrounds. Consider using data augmentation techniques to improve generalization.
• Prediction Thresholding: Set a confidence threshold to filter out low-probability detections.
• Bounding Boxes: Modify the code to extract bounding boxes around detected fish food for further analysis or tracking.

Addressing High- vs. Low-Contrast Backgrounds:

• Data Augmentation: During training, use data augmentation techniques like random brightness and contrast adjustments to improve model performance on both scenarios.
• Transfer Learning: Consider using a pre-trained model for object detection (e.g., YOLOv8) and fine-tuning it on your specific fish food dataset. These models often have built-in mechanisms for handling variations in contrast.

Additional Tips:

• Explore techniques like background subtraction for handling low-contrast scenarios where fish food overlaps with fish bodies.
• Use libraries like OpenCV for video processing and visualization.
• Continuously evaluate and improve your model's performance as you gather more data.

Remember, this is a starting point. You'll need to adapt and refine the code based on your specific dataset, model architecture, and desired detection accuracy.

===============================

Here's Python code using OpenCV and TensorFlow (optional) to get you started with fish food detection in videos, considering both high-contrast and low-contrast backgrounds:

1. Preprocessing and Background Subtraction (Optional):

Python

import cv2

def preprocess_image(frame):
  # Grayscale conversion (optional)
  gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

  # Background subtraction (optional, adjust parameters for your scenario)
  # Background modeling is crucial for low-contrast scenarios
  fgbg = cv2.createBackgroundSubtractorMOG2()
  fgmask = fgbg.apply(gray)

  # Apply thresholding (optional)
  ret, thresh = cv2.threshold(fgmask, 127, 255, cv2.THRESH_BINARY)

  return thresh

Use code with caution.

2. Object Detection with Contours:

Python

def detect_fish_food(frame):
  # Preprocess the image (call preprocess_image if needed)
  preprocessed_image = preprocess_image(frame)

  # Find contours
  contours, _ = cv2.findContours(preprocessed_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

  # Filter contours based on area, circularity, etc. (adjust parameters for your food size)
  fish_food = []
  for cnt in contours:
    area = cv2.contourArea(cnt)
    if 100 < area < 500:  # Adjust area range based on food size
      perimeter = cv2.arcLength(cnt, True)
      circularity = 4 * np.pi * area / (perimeter * perimeter)
      if circularity > 0.8:  # Adjust circularity threshold for food shape
        fish_food.append(cnt)

  # Draw bounding boxes around detected food (optional)
  for cnt in fish_food:
    x, y, w, h = cv2.boundingRect(cnt)
    cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)

  return frame, fish_food

Use code with caution.

3. TensorFlow Object Detection (Optional - More Advanced):

Python

# Replace with your pre-trained model and label (fish_food)
model = tf.saved_model.load("path/to/your/model")

def detect_fish_food_tf(frame):
  # Preprocess the image (resize, etc. based on model requirements)
  preprocessed_image = preprocess_image_for_model(frame)

  # Make predictions with the TensorFlow model
  predictions = model(preprocessed_image)
  boxes = predictions["detection_boxes"].numpy()
  scores = predictions["detection_scores"].numpy()

  # Filter detections based on confidence score (adjust threshold)
  fish_food = []
  for box, score in zip(boxes, scores):
    if score > 0.7:  # Adjust confidence threshold
      x_min, y_min, x_max, y_max = box
      fish_food.append((x_min, y_min, x_max, y_max))

  # Draw bounding boxes around detected food (optional)
  for x_min, y_min, x_max, y_max in fish_food:
    cv2.rectangle(frame, (int(x_min), int(y_min)), (int(x_max), int(y_max)), (0, 255, 0), 2)

  return frame, fish_food

Use code with caution.

4. Video Processing:

Python

cap = cv2.VideoCapture("path/to/your/video.mp4")

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

  # Choose detection method (comment out the other)
  # frame, fish_food = detect_fish_food(frame)
  frame, fish_food = detect_fish_food_tf(frame)

  # Display the frame with detections
  cv2.imshow("Fish Food Detection", frame)

  # Exit on 'q' key press
  if cv2.waitKey(1) == ord("q"):
    break

cap.release()
cv2.destroyAllWindows()

Use code with caution.

Things to Consider:

• Background Subtraction: Background subtraction (optional step) can be crucial for low-contrast scenarios where fish food overlaps

=================================================

Here's some Python code to get you started with fish food detection in videos using object detection with TensorFlow/Keras:

Python

Things to Consider:

• Model Training: You'll need to train a fish food detection model on a dataset containing images with fish food on high-contrast (sea background) and low-contrast (overlayed on fish) backgrounds. Tools like TensorFlow Object Detection API (OD API) can be helpful for this process.
• Data Augmentation: Augmenting your training data with techniques like flipping, rotation, and color jittering can improve model performance in handling variations in lighting and background conditions.
• Threshold Adjustment: The confidence score threshold determines how likely a detection is considered a true positive. Adjust it based on your model's performance and acceptable false positive rate.
• Post-processing: You might need to add additional filtering or logic depending on your specific needs (e.g., minimum size threshold for detected objects).

Additional Tips:

• Explore pre-trained object detection models like YOLOv8 or SSD that might be able to detect fish food with fine-tuning on your specific data.
• Consider using transfer learning from pre-trained models on similar tasks (e.g., food object detection) to improve performance.

Remember, this is a starting point. You'll need to adapt and improve the code based on your specific dataset, model, and desired performance metrics.

Sources

1. github.com/Aanandi-Goel/face-reco