aravindpai

import cv2
import numpy as np
import imutils

video='28.mp4'

# Create a VideoCapture object and read from input file
# If the input is the camera, pass 0 instead of the video file name
cap = cv2.VideoCapture(video)
cnt=0

aravindpai

from keras import backend as K 
K.clear_session() 
latent_dim = 500 

# Encoder 
encoder_inputs = Input(shape=(max_len_text,)) 
enc_emb = Embedding(x_voc_size, latent_dim,trainable=True)(encoder_inputs) 

#LSTM 1 
encoder_lstm1 = LSTM(latent_dim,return_sequences=True,return_state=True) 

aravindpai

ball_df.dropna(inplace=True)
print(ball_df)

aravindpai

!rm -r ball/*
ball_df = pd.DataFrame(columns=['frame','x','y','w','h'])

for idx in range(len(frames)):
    
    img= cv2.imread('frames/' + frames[idx])
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    gray = cv2.GaussianBlur(gray,(25, 25),0)
    _ , mask = cv2.threshold(gray, 200, 255, cv2.THRESH_BINARY)
    image, contours, _ = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

aravindpai

from sklearn.metrics import classification_report
y_pred = rfc.predict(x_val)
print(classification_report(y_val,y_pred))

aravindpai

from sklearn.ensemble import RandomForestClassifier 
rfc = RandomForestClassifier(max_depth=3) 
rfc.fit(x_tr,y_tr)

aravindpai

from sklearn.model_selection import train_test_split
x_tr,x_val,y_tr,y_val = train_test_split(features,labels, test_size=0.2, stratify=labels,random_state=0)

aravindpai

import os
import cv2
import numpy as np
import pandas as pd
folders=os.listdir('data/')

images=[]
labels= []
for folder in folders:
    files=os.listdir('data/'+folder)

aravindpai

!rm -r patch/*

num=20
cnt=0
for i in range(len(contours)):
    x,y,w,h = cv2.boundingRect(contours[i])
    
    numer=min([w,h])
    denom=max([w,h])
    ratio=numer/denom

aravindpai

img_copy = np.copy(gray)
cv2.drawContours(img_copy, contours, -1, (0,255,0), 3)
plt.imshow(img_copy, cmap='gray')