How to export bounding boxes as .jpg - opencv

Question

for my project I want to save the Bounding Boxes found by the Object Detection API as .jpg for feeding in another CNN for further classification.
Here is my code (derived from EdjeElectronics GitHub):
import os
import cv2
import numpy as np
import tensorflow as tf
import sys
# 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 = '_model_ssd_v2'
IMAGE_NAME = 'image.jpg'
# Grab path to current working directory
CWD_PATH = os.getcwd()
# Path to frozen detection graph .pb file, which contains the model that is used
# for object detection.
PATH_TO_CKPT = os.path.join(CWD_PATH,MODEL_NAME,'frozen_inference_graph.pb')
# Path to label map file
PATH_TO_LABELS = os.path.join(CWD_PATH,'_data','label_map.pbtxt')
# Path to image
PATH_TO_IMAGE = os.path.join(CWD_PATH,"_images",IMAGE_NAME)
# Number of classes the object detector can identify
NUM_CLASSES = 6
# 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')
# 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
image = cv2.imread(PATH_TO_IMAGE)
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')
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=0.3)
# All the results have been drawn on image. Now display the image.
# cv2.imshow('Object detector', cv2.resize(image, (int(2592/2),int(1944/2))))
# # Press any key to close the image
# cv2.waitKey(0)
# # Clean up
# cv2.destroyAllWindows()
cv2.imwrite("C:/tensorflow/models/research/object_detection/_images/test1.jpg", image)
A similar question was asked here but I don´t know how to apply it with the Tensorflow Object Detection API.
Thank You!

Answer 1

I've found the function draw_bounding_boxes_on_image in the vis_util. Try this:
#create a white back ground image with the same shape as image
white_bg_img = 255*np.ones(image.shape, np.uint8)
vis_util.draw_bounding_boxes_on_image(
white_bg_img ,
np.squeeze(boxes),
color='red',
thickness=4)
cv2.imwrite("bounding_boxes.jpg", white_bg_img )
To draw the image within the bounding boxes.
boxes = np.squeeze(boxes)
for i in range(len(boxes)):
ymin = box[i,0]
xmin = box[i,1]
ymax = box[i,2]
xmax = box[i,3]
roi = image[ymin:ymax,xmin:xmax].copy()
cv2.imwrite("box_{}.jpg".format(str(i)), roi)
Save files will be like box_1.jpg, box_2.jpg ...

Answer 2

I followed this link and it worked. Add the following code:
min_score_thresh=0.60
true_boxes = boxes[0][scores[0] > min_score_thresh]
for i in range(true_boxes.shape[0]):
ymin = int(true_boxes[i,0]*height)
xmin = int(true_boxes[i,1]*width)
ymax = int(true_boxes[i,2]*height)
xmax = int(true_boxes[i,3]*width)
roi = image[ymin:ymax,xmin:xmax].copy()
cv2.imwrite("box_{}.jpg".format(str(i)), roi)
Make sure you define true height and width of image.

Answer 3

this will work
enter code here
box = np.squeeze(boxes)
for i in range(len(boxes)):
ymin = (int(box[i,0]*height))
xmin = (int(box[i,1]*width))
ymax = (int(box[i,2]*height))
xmax = (int(box[i,3]*width))
print(ymin,xmin,ymax,xmax)
roi =image[ymin:ymax,xmin:xmax].copy()