"classify_webcam.py" line 40 image_data = graph_def.get_tensor_by_name('final_result:0')AttributeError:get_tensor_by_name

[vipulsomaiah1296]

`import sys
import modulegraph
import os
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
import copy
import cv2

Disable tensorflow compilation warnings

os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
import tensorflow as tf

def predict(image_data):

predictions = sess.run(softmax_tensor, \
         {'DecodeJpeg/contents:0': image_data})

# Sort to show labels of first prediction in order of confidence
top_k = predictions[0].argsort()[-len(predictions[0]):][::-1]
max_score = 0.0
res = ''
for node_id in top_k:
    human_string = label_lines[node_id]
    score = predictions[0][node_id]
    if score > max_score:
        max_score = score
        res = human_string
return res, max_score

Loads label file, strips off carriage return

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

Unpersists graph from file

with tf.gfile.FastGFile("logs/output_graph.pb", 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
g_in = tf.import_graph_def(graph_def, name='')
with tf.Session() as sess:
# Feed the image_data as input to the graph and get first prediction
image_data = graph_def.get_tensor_by_name('final_result:0')
softmax_tensor = sess.graph.get_tensor_by_name('final_result:0')
c = 0
cap = cv2.VideoCapture(0)
res, score = '', 0.0
i = 0
mem = ''
consecutive = 0
sequence = ''

while True:
    ret, img = cap.read()
    img = cv2.flip(img, 1)
    
    if ret:
        x1, y1, x2, y2 = 100, 100, 300, 300
        img_cropped = img[y1:y2, x1:x2]
        c += 1
        image_data = cv2.imencode('.jpg', img_cropped)[1].tostring()            
        a = cv2.waitKey(1) # waits to see if `esc` is pressed           
        if i == 4:
            res_tmp, score = predict(image_data)
            res = res_tmp
            i = 0
            if mem == res:
                consecutive += 1
            else:
                consecutive = 0
            if consecutive == 2 and res not in ['nothing']:
                if res == 'space':
                    sequence += ' '
                elif res == 'del':
                    sequence = sequence[:-1]
                else:
                    sequence += res
                consecutive = 0
        i += 1
        cv2.putText(img, '%s' % (res.upper()), (100,400), cv2.FONT_HERSHEY_SIMPLEX, 4, (255,255,255), 4)
        cv2.putText(img, '(score = %.5f)' % (float(score)), (100,450), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255))
        mem = res
        cv2.rectangle(img, (x1, y1), (x2, y2), (255,0,0), 2)
        cv2.imshow("img", img)
        img_sequence = np.zeros((200,1200,3), np.uint8)
        cv2.putText(img_sequence, '%s' % (sequence.upper()), (30,30), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255), 2)
        cv2.imshow('sequence', img_sequence)
        
        if a == 27: # when `esc` is pressed
            break

Following line should... <-- This should work fine now

cv2.destroyAllWindows()
cv2.VideoCapture(0).release()`

[someshium]

hi did you were successful in running this project?
mine is taking too much time in training it's more than 8 hours till now?
can you please help. i am taking this repo as a reference.