Soccer Analysis Model

This model is designed to perform basic analysis on soccer video footage. It includes several functions for processing frames and extracting relevant information from the video.

Prerequisites

Make sure you have the following dependencies installed:

• cv2 (OpenCV)
• matplotlib
• numpy

Usage

Frame Extraction

To extract frames from a video, you can use the following code snippet:

import cv2

frame_counter = 0
step_size = 100
cap = cv2.VideoCapture("video.mp4")

while True:
    ret, frame = cap.read()

    if not ret:
        break

    if frame_counter % step_size == 0:
        cv2.imwrite(f"frame_{frame_counter}.jpg", frame)

    frame_counter += 1

Replace "video.mp4" with the path to your video file. This code will save frames at regular intervals defined by step_size (in this example, every 100 frames).

Player Detection

The find_blobs function can be used to detect blobs (connected components) in a binary image. Here's an example usage:

import numpy as np

def find_blobs(image, min_blob_size=5):
    # Function implementation...

image = cv2.imread('frame_500.jpg')
img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)

# Define lower and upper thresholds for blob detection
lower_red = np.array([0, 103, 174])
upper_red = np.array([180, 255, 255])

# Create binary image based on thresholding
img_red = cv2.inRange(img_hsv, lower_red, upper_red)

# Find blob coordinates
blob_coordinates_red = find_blobs(img_red)

# Visualize the blobs on the original image
circle_color = (0, 0, 255)
circle_radius = 50
for coord in blob_coordinates_red:
    x, yn = coord
    y = yn + 180
    cv2.circle(img_rgb, (x, y), circle_radius, circle_color, 2)

# Display the image with detected blobs
plt.imshow(img_rgb)

Sub-Grid Analysis

The model includes functionality for analyzing a specific sub-grid within an image. Here's an example usage:

def analyze_sub_grid(img_cut_original, blob_coordinates_blue, blob_coordinates_red):
    # Function implementation...

img = cv2.imread('frame_500.jpg')

# Perform necessary preprocessing on the image

grid_size = (3, 4)
row = 1
col = 2

# Extract the specified sub-grid from the original image
sub_grid = img_cut_original[start_y:end_y, start_x:end_x]

# Analyze the sub-grid to count blue and red team members
num_labels_blue, num_labels_red = analyze_sub_grid(sub_grid, blob_coordinates_blue, blob_coordinates_red)

# Display the sub-grid image
plt.imshow(sub_grid)
plt.show()

print("Number of blue team members in the sub-grid:", num_labels_blue)
print("Number of red team members in the sub-grid:", num_labels_red)

Make sure to replace the relevant variables (img_cut_original, blob_coordinates_blue, blob_coordinates_red, etc.) with your own data.

Region Labeling

The model includes a function for detecting ellipses in an image. Here's an example usage:

def find_longest_vertical_line(region):
    # Function implementation...

img = cv2.imread('frame_200.jpg')

# Perform necessary preprocessing on the image

lower = np.array([0, 0, 168])
upper = np.array([172, 255,

 255])

region = cv2.inRange(img_cut, lower, upper)

# Find the coordinates of the longest vertical line in the region
coord = find_longest_vertical_line(region)
x, yn = coord
y = yn + 150

# Draw an ellipse at the detected coordinates
image = cv2.ellipse(img_rgb, (x, y), (300, 80), 0, 0, 360, (0, 0, 255), 5)

# Display the image with the detected ellipse
plt.imshow(img_rgb)

Again, make sure to replace the relevant variables (img, img_cut, img_rgb, etc.) with your own data.

Conclusion

This soccer analysis model provides basic functionalities for frame extraction, blob detection, sub-grid analysis, and ellipse detection in soccer video footage. Feel free to customize and enhance the model based on your specific needs and requirements. Enjoy analyzing your soccer videos!