OpenCv

# Import libraries
import cv2
import os
import numpy as np


# Reading the video
vidcap = cv2.VideoCapture('cutvideo.mp4')
success, image = vidcap.read()
count = 0
success = True
idx = 0

# Read the video frame by frame
while success:
    # converting into hsv image
    hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
    # green range
    lower_green = np.array([31, 114, 74])
    upper_green = np.array([64, 215, 255])
    # blue range
    lower_blue = np.array([47, 0, 0])
    upper_blue = np.array([137, 197, 255])

    # Red range
    lower_red = np.array([0, 149, 95])
    upper_red = np.array([11, 255, 255])

    # white range
    lower_white = np.array([25, 100, 199])
    upper_white = np.array([140, 158, 255])

    # Define a mask ranging from lower to uppper
    mask = cv2.inRange(hsv, lower_green, upper_green)
    # Do masking
    res = cv2.bitwise_and(image, image, mask=mask)
    # convert to hsv to gray
    res_bgr = cv2.cvtColor(res, cv2.COLOR_HSV2BGR)
    res_gray = cv2.cvtColor(res, cv2.COLOR_BGR2GRAY)

    # Defining a kernel to do morphological operation in threshold image to
    # get better output.
    kernel = np.ones((13, 13), np.uint8)
    thresh = cv2.threshold(res_gray, 127, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
    thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)

    # find contours in threshold image
    contours,hierachy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)


    prev = 0
    font = cv2.FONT_HERSHEY_SIMPLEX

    for c in contours:
        x, y, w, h = cv2.boundingRect(c)

        # Detect players
        if (h >= (1.5) * w):
            if (w > 15 and h >= 15):
                idx = idx + 1
                player_img = image[y:y + h, x:x + w]
                player_hsv = cv2.cvtColor(player_img, cv2.COLOR_BGR2HSV)
                # If player has blue jersy
                mask1 = cv2.inRange(player_hsv, lower_blue, upper_blue)
                res1 = cv2.bitwise_and(player_img, player_img, mask=mask1)
                res1 = cv2.cvtColor(res1, cv2.COLOR_HSV2BGR)
                res1 = cv2.cvtColor(res1, cv2.COLOR_BGR2GRAY)
                nzCount = cv2.countNonZero(res1)
                # If player has red jersy
                mask2 = cv2.inRange(player_hsv, lower_red, upper_red)
                res2 = cv2.bitwise_and(player_img, player_img, mask=mask2)
                res2 = cv2.cvtColor(res2, cv2.COLOR_HSV2BGR)
                res2 = cv2.cvtColor(res2, cv2.COLOR_BGR2GRAY)
                nzCountred = cv2.countNonZero(res2)

                if (nzCount >= 20):
                    # Mark blue jersy players as france
                    cv2.putText(image, 'Chelsea', (x - 2, y - 2), font, 0.8, (255, 0, 0), 2, cv2.LINE_AA)
                    cv2.rectangle(image, (x, y), (x + w, y + h), (255, 0, 0), 3)
                else:
                    pass
                if (nzCountred >= 20):
                    # Mark red jersy players as belgium
                    cv2.putText(image, str(x), (x - 2, y - 2), font, 0.8, (0, 0, 255), 2, cv2.LINE_AA)
                    cv2.rectangle(image, (x, y), (x + w, y + h), (0, 0, 255), 3)
                else:
                    pass
        if ((h >= 1 and w >= 1) and (h <= 30 and w <= 30)):
            player_img = image[y:y + h, x:x + w]

            player_hsv = cv2.cvtColor(player_img, cv2.COLOR_BGR2HSV)
            # white ball  detection
            mask1 = cv2.inRange(player_hsv, lower_white, upper_white)
            res1 = cv2.bitwise_and(player_img, player_img, mask=mask1)
            res1 = cv2.cvtColor(res1, cv2.COLOR_HSV2BGR)
            res1 = cv2.cvtColor(res1, cv2.COLOR_BGR2GRAY)
            nzCount = cv2.countNonZero(res1)

            if (nzCount >= 3):
                # detect football
                cv2.putText(image, 'pilka', (x - 2, y - 2), font, 0.8, (0, 255, 0), 2, cv2.LINE_AA)
                cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 3)

    cv2.imwrite("./Cropped/frame%d.jpg" % count, res)
    print
    'Read a new frame: ', success  # save frame as JPEG file
    count += 1
    cv2.imshow('Match Detection', image)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
    success, image = vidcap.read()
    contour, hierachy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    print(len(contour))



vidcap.release()
cv2.destroyAllWindows()