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import tensorflow as tf
import cv2

fn_list=[]

# Capture video from file

cap = cv2.VideoCapture('test_lego.wmv')
image_path = '/home/shorav/tfClassifier/new_capture.jpg'

label_lines = [line.rstrip() for line
                  in tf.gfile.GFile("final_labels.txt")]

with tf.gfile.FastGFile("./final_graph.pb", 'rb') as f:
    graph_def = tf.GraphDef()                
    graph_def.ParseFromString(f.read())
    _ = tf.import_graph_def(graph_def, name='')
    Frame_number=0

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

    if ret == True:
        Frame_number+=1

        fn_list.append(Frame_number)

        print "frame number is :", Frame_number


        gray = cv2.cvtColor(frame,cv2.COLOR_RGB2GRAY)
        ret,thresh= cv2.threshold(gray,100,255,cv2.THRESH_BINARY)
        contours, hierarchy = cv2.findContours(thresh.copy(),cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
        c = max(contours, key = cv2.contourArea)
        x,y,w,h = cv2.boundingRect(c)
        font = cv2.FONT_HERSHEY_SIMPLEX


        #cv2.drawContours(gray,contours,-1, (0,255,255), 2)
        for contour in contours:
            [x,y,w,h] = cv2.boundingRect(contour)
            cv2.rectangle(gray,(x,y),(x+w,y+h),(255,0,255),2)


            cv2.putText(frame,'Show the text',(100,50), font, 1, (0,255,0), 4,cv2.CV_AA)    
            cv2.imshow('frame',gray)
        if (Frame_number%10==0):
            cv2.imwrite('new_capture.jpg',frame)
            image_data = tf.gfile.FastGFile(image_path, 'rb').read()
            with tf.Session() as sess:
                    softmax_tensor = sess.graph.get_tensor_by_name('final_result:0')
                    predictions = sess.run(softmax_tensor, \
                             {'DecodeJpeg/contents:0': image_data})
                    top_k = predictions[0].argsort()[-len(predictions[0]):][::-1]
                    score_results=[]
                    final_label=[]
                    for node_id in top_k:
                        human_string = label_lines[node_id]
                        score = predictions[0][node_id]
                        print('%s (score = %.5f)' % (human_string, score))
                        score_results.append(score)
                        final_label.append(human_string)

            if cv2.waitKey(50) & 0xFF == ord('q'):
                break


    else:
         break
cap.release()
cv2.destroyAllWindows()