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()