python - 将边界框提取为.jpg

Question

提取检测到的对象以及边界框，并将其另存为磁盘上的图像。

我已经掌握了Edge Electronics的代码，并成功地训练和测试了该模型。我在图像上看到了边框。

import os
import cv2
import numpy as np
import tensorflow as tf
import sys
from glob import glob
import glob
import csv
from PIL import Image
import json

sys.path.append("..")

# Import utilites
from utils import label_map_util
from utils import visualization_utils as vis_util

MODEL_NAME = 'inference_graph'

CWD_PATH = os.getcwd()

PATH_TO_CKPT = os.path.join(CWD_PATH,MODEL_NAME,'frozen_inference_graph.pb')

PATH_TO_LABELS = os.path.join(CWD_PATH,'training','labelmap.pbtxt')

PATH_TO_IMAGE = list(glob.glob("C:\\new_multi_cat\\models\\research\\object_detection\\img_test\\*jpeg"))

NUM_CLASSES = 3

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)

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)

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 paths in range(len(PATH_TO_IMAGE)):
    image = cv2.imread(PATH_TO_IMAGE[paths])
    image_expanded = np.expand_dims(image, axis=0)

    (boxes, scores, classes, num) = sess.run([detection_boxes, detection_scores, detection_classes, num_detections],feed_dict={image_tensor: image_expanded})


    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=4,
    min_score_thresh=0.80)


    white_bg_img = 255*np.ones(PATH_TO_IMAGE[paths].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)

    boxes = np.squeeze(boxes)
    for i in range(len(boxes)):
        box[0]=box[0]*height
        box[1]=box[1]*width
        box[2]=box[2]*height
        box[3]=box[3]*width
    roi = image[box[0]:box[2],box[1]:box[3]].copy()
    cv2.imwrite("box_{}.jpg".format(str(i)), roi)

这是我得到的错误:

Traceback (most recent call last):   File "objd_1.py", line
75, in <module>
     white_bg_img = 255*np.ones(PATH_TO_IMAGE[paths].shape, np.uint8) AttributeError: 'str' object has no attribute 'shape' 

我进行了很多搜索，但无法确定代码中的错误。为什么我无法将检测到的区域提取为图像？

Answer 1

您尝试从文件名而不是图像中获取shape。更换

white_bg_img = 255*np.ones(PATH_TO_IMAGE[paths].shape, np.uint8)

至

white_bg_img = 255*np.ones(image.shape, np.uint8)

编辑:更正的代码

import os
import cv2
import numpy as np
import tensorflow as tf
import sys
from glob import glob
import glob
import csv
from PIL import Image
import json

sys.path.append("..")

# Import utilites
from utils import label_map_util
from utils import visualization_utils as vis_util

MODEL_NAME = 'inference_graph'

CWD_PATH = os.getcwd()

PATH_TO_CKPT = os.path.join(CWD_PATH,MODEL_NAME,'frozen_inference_graph.pb')

PATH_TO_LABELS = os.path.join(CWD_PATH,'training','labelmap.pbtxt')

PATH_TO_IMAGE = list(glob.glob("C:\\new_multi_cat\\models\\research\\object_detection\\img_test\\*jpeg"))

NUM_CLASSES = 3

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)

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)

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 paths in range(len(PATH_TO_IMAGE)):
    image = cv2.imread(PATH_TO_IMAGE[paths])
    image_expanded = np.expand_dims(image, axis=0)

    (boxes, scores, classes, num) = sess.run([detection_boxes, detection_scores, detection_classes, num_detections],feed_dict={image_tensor: image_expanded})


    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=4,
    min_score_thresh=0.80)


    white_bg_img = 255*np.ones(image.shape, np.uint8)
    vis_util.draw_bounding_boxes_on_image_array(
    white_bg_img ,
    np.squeeze(boxes),
    color='red',
    thickness=4)
    cv2.imwrite("bounding_boxes.jpg", white_bg_img)

    boxes = np.squeeze(boxes)
    for i in range(len(boxes)):
        box[0]=box[0]*height
        box[1]=box[1]*width
        box[2]=box[2]*height
        box[3]=box[3]*width
    roi = image[box[0]:box[2],box[1]:box[3]].copy()
    cv2.imwrite("box_{}.jpg".format(str(i)), roi)