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numplate_recognition_detection.py
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import numpy as np
import os
import sys
import tensorflow as tf
from matplotlib import pyplot as plt
from PIL import Image
import cv2
import pytesseract
from custom_plate import allow_needed_values as anv
from custom_plate import do_image_conversion as dic
get_ipython().magic('matplotlib inline')
sys.path.append("..")
from utils import label_map_util
from utils import visualization_utils as vis_util
MODEL_NAME = 'numplate'
PATH_TO_CKPT = MODEL_NAME + '/graph-200000/frozen_inference_graph.pb'
PATH_TO_LABELS = os.path.join('training', 'object-detection.pbtxt')
NUM_CLASSES = 1
detection_graph = tf.Graph()
with detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
category_index = label_map_util.create_category_index(categories)
def load_image_into_numpy_array(image):
(im_width, im_height) = image.size
return np.array(image.getdata()).reshape(
(im_height, im_width, 3)).astype(np.uint8)
PATH_TO_TEST_IMAGES_DIR = 'png_tesseract/test_ram'
TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.jpg'.format(i)) for i in range(5, 6) ]
IMAGE_SIZE = (12, 8)
TEST_DHARUN=os.path.join('numplate')
count = 0
with detection_graph.as_default():
with tf.Session(graph=detection_graph) as sess:
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
num_detections = detection_graph.get_tensor_by_name('num_detections:0')
for image_path in TEST_IMAGE_PATHS:
image = Image.open(image_path)
image_np = load_image_into_numpy_array(image)
image_np_expanded = np.expand_dims(image_np, axis=0)
(boxes, scores, classes, num) = sess.run(
[detection_boxes, detection_scores, detection_classes, num_detections],
feed_dict={image_tensor: image_np_expanded})
ymin = boxes[0,0,0]
xmin = boxes[0,0,1]
ymax = boxes[0,0,2]
xmax = boxes[0,0,3]
(im_width, im_height) = image.size
(xminn, xmaxx, yminn, ymaxx) = (xmin * im_width, xmax * im_width, ymin * im_height, ymax * im_height)
cropped_image = tf.image.crop_to_bounding_box(image_np, int(yminn), int(xminn),int(ymaxx - yminn), int(xmaxx - xminn))
img_data = sess.run(cropped_image)
count = 0
filename = dic.yo_make_the_conversion(img_data, count)
pytesseract.tesseract_cmd = '/home/tensorflow-cuda/dharun_custom/models/research/object_detection/tessdata/'
text = pytesseract.image_to_string(Image.open(filename),lang=None)
print('CHARCTER RECOGNITION : ',anv.catch_rectify_plate_characters(text))
vis_util.visualize_boxes_and_labels_on_image_array(
image_np,
np.squeeze(boxes),
np.squeeze(classes).astype(np.int32),
np.squeeze(scores),
category_index,
use_normalized_coordinates=True,
line_thickness=5)
plt.figure(figsize=IMAGE_SIZE)
# plt.imshow(img_data)
# plt.show()