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Object_detection_image.py
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Object_detection_image.py
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# coding=utf-8
######## Image Object Detection Using Tensorflow-trained Classifier #########
#
# Author: Evan Juras
# Date: 1/15/18
# Description:
# This program uses a TensorFlow-trained classifier to perform object detection.
# It loads the classifier uses it to perform object detection on an image.
# It draws boxes and scores around the objects of interest in the image.
## Some of the code is copied from Google's example at
## https://github.com/tensorflow/models/blob/master/research/object_detection/object_detection_tutorial.ipynb
## and some is copied from Dat Tran's example at
## https://github.com/datitran/object_detector_app/blob/master/object_detection_app.py
## but I changed it to make it more understandable to me.
# Import packages
import os
import cv2
import numpy as np
import tensorflow as tf
import sys
import json
import ast
import urllib.parse
# import matplotlib
# matplotlib.use('TkAgg')
# import matplotlib.pyplot as plt
# This is needed since the notebook is stored in the object_detection folder.
sys.path.append("..")
# Import utilites
from utils import label_map_util
from utils import visualization_utils as vis_util
# Name of the directory containing the object detection module we're using
MODEL_NAME = 'inference_graph'
IMAGE_NAME = 'test_pot/4.jpg'
# Grab path to current working directory
CWD_PATH = os.getcwd()
IMAGE_SHOW = False
PATH_TO_CKPT = sys.argv[1]
PATH_TO_LABELS = sys.argv[2]
PATH_TO_IMAGE = sys.argv[3]
labelNameMap = {}
# Path to frozen detection graph .pb file, which contains the model that is used
# for object detection.
if PATH_TO_CKPT is None:
PATH_TO_CKPT = os.path.join(CWD_PATH, MODEL_NAME, 'frozen_inference_graph.pb')
# Path to label map file
if PATH_TO_LABELS is None:
PATH_TO_LABELS = os.path.join(CWD_PATH, 'training', 'labelmap.pbtxt')
# Path to image
if PATH_TO_IMAGE is None:
PATH_TO_IMAGE = os.path.join(CWD_PATH, IMAGE_NAME)
# Number of classes the object detector can identify
if sys.argv[4] is None:
NUM_CLASSES = 6
else:
NUM_CLASSES = int(sys.argv[4])
if sys.argv[5] is not None:
loaded = urllib.parse.unquote(sys.argv[5])
loaded = json.loads(loaded)
labelNameMap = loaded
# Load the label map.
# Label maps map indices to category names, so that when our convolution
# network predicts `5`, we know that this corresponds to `king`.
# Here we use internal utility functions, but anything that returns a
# dictionary mapping integers to appropriate string labels would be fine
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)
# Load the Tensorflow model into memory.
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='')
sess = tf.Session(graph=detection_graph)
# Define input and output tensors (i.e. data) for the object detection classifier
# Input tensor is the image
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
# Output tensors are the detection boxes, scores, and classes
# Each box represents a part of the image where a particular object was detected
detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
# Each score represents level of confidence for each of the objects.
# The score is shown on the result image, together with the class label.
detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
# Number of objects detected
num_detections = detection_graph.get_tensor_by_name('num_detections:0')
IMAGE_PATHS = []
PATH_TO_IMAGE_DIR = PATH_TO_IMAGE
if not os.path.isfile(PATH_TO_IMAGE):
for filename in os.listdir(PATH_TO_IMAGE):
if filename.lower().endswith((".jpg", ".jpeg", ".png", ".bmp")):
IMAGE_PATHS.append(os.path.join(PATH_TO_IMAGE, filename))
else:
IMAGE_PATHS.append(PATH_TO_IMAGE)
PATH_TO_IMAGE_DIR = os.path.dirname(PATH_TO_IMAGE)
for IMAGE_PATH in IMAGE_PATHS:
# Load image using OpenCV and
# expand image dimensions to have shape: [1, None, None, 3]
# i.e. a single-column array, where each item in the column has the pixel RGB value
print('path####### {0}'.format(IMAGE_PATH))
image = cv2.imread(IMAGE_PATH)
image_expanded = np.expand_dims(image, axis=0)
# Perform the actual detection by running the model with the image as input
(boxes, scores, classes, num) = sess.run(
[detection_boxes, detection_scores, detection_classes, num_detections],
feed_dict={image_tensor: image_expanded})
# Draw the results of the detection (aka 'visulaize the results')
# print('image params ',labelNameMap,category_index)
for key in category_index:
_label = category_index[key]
_name = _label['name']
if _name in labelNameMap:
_label['name'] = labelNameMap[_name]
print('image params after', category_index)
v_res = vis_util.visualize_boxes_and_labels_on_image_array(
image,
np.squeeze(boxes),
np.squeeze(classes).astype(np.int32),
np.squeeze(scores),
category_index,
use_normalized_coordinates=True,
line_thickness=8,
min_score_thresh=0.60)
image_rec_res = []
if len(scores) > 0:
print('v_res 识别数量: ', scores[0])
for idx in range(0, len(scores[0])):
if scores[0][idx] > 0.60:
image_rec_res.append(
{'score': float(scores[0][idx]), 'id': int(classes[0][idx]),
'name': category_index[classes[0][idx]]['name']})
# print('v_res 识别数量: ', scores, classes, num, image_rec_res)
# All the results have been drawn on image. Now display the image.
FULL_NAME = IMAGE_PATH.split("/")
SHOW_NAME = FULL_NAME[-1]
with open(os.path.join(PATH_TO_IMAGE_DIR, 'result', '{0}.out'.format(SHOW_NAME)), 'w', encoding='utf8') as f:
json.dump(image_rec_res, f, ensure_ascii=False)
# plt.figure(SHOW_NAME)
# plt.imshow(image)
if IMAGE_SHOW:
cv2.imshow(SHOW_NAME, image)
else:
write_path = os.path.join(PATH_TO_IMAGE_DIR, 'result', SHOW_NAME)
print('valid write path: {0}'.format(write_path))
cv2.imwrite(write_path, image)
# Press any key to close the image
if IMAGE_SHOW:
while (1):
c = cv2.waitKey(0)
print('loop wait esc')
if c == 27:
print('will close！！！！')
cv2.destroyAllWindows()
break
# # Clean up
# cv2.destroyAllWindows()