TensorFlow 对象检测 API 打印在图像上找到的对象到控制台
TensorFlow Object Detection API print objects found on image to console
我正在尝试 return 使用 TF 对象检测 API 在图像中找到的对象列表。
为此，我使用 print([category_index.get(i) for i in classes[0]]) 打印已找到的对象列表或使用 print(num_detections) 显示找到的对象的数量，但在这两种情况下，它都为我提供了包含 300 个值的列表或者简单地值 [300.] 相应地。
如何才能 return 仅显示图像上的对象？或者如果有什么错误请大家帮忙看看是什么地方出了问题。
我在训练时使用 Faster RCNN 模型配置文件和检查点。确保它真的检测到图像中的几个物体，这里是：
我的代码：
import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfile
from collections import defaultdict
from io import StringIO
from matplotlib import pyplot as plt
from PIL import Image
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util
PATH_TO_CKPT = 'frozen_graph/frozen_inference_graph.pb'
PATH_TO_LABELS = 'object_detection/pascal_label_map.pbtxt'
NUM_CLASSES = 7
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 = 'object_detection/test_images/'
TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.jpg'.format(i)) for i in range(1, 2) ]
IMAGE_SIZE = (12, 8)
with detection_graph.as_default():
with tf.Session(graph=detection_graph) as sess:
sess.run(tf.global_variables_initializer())
img = 1
for image_path in TEST_IMAGE_PATHS:
image = Image.open(image_path)
image_np = load_image_into_numpy_array(image)
# Expand dimensions since the model expects images to have shape: [1, None, None, 3]
image_np_expanded = np.expand_dims(image_np, axis=0)
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
# Each box represents a part of the image where a particular object was detected.
boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
scores = detection_graph.get_tensor_by_name('detection_scores:0')
classes = detection_graph.get_tensor_by_name('detection_classes:0')
num_detections = detection_graph.get_tensor_by_name('num_detections:0')
(boxes, scores, classes, num_detections) = sess.run(
[boxes, scores, classes, num_detections],
feed_dict={image_tensor: image_np_expanded})
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=8)
plt.figure(figsize=IMAGE_SIZE)
plt.imsave('RESULTS/' + str(img) + '.jpg', image_np)
img += 1
# Return found objects
print([category_index.get(i) for i in classes[0]])
print(boxes.shape)
print(num_detections)
结果如下：
[{'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'lucky_strike_red', 'id': 4}]
(1, 300, 4)
[ 300.]
提前感谢您提供任何信息！
更新：
非常感谢所有帮助解决这个问题的人。
下面的代码行正是我所需要的，它为我提供了包含找到的对象的列表，以便我可以对它们进行其他操作。
print [category_index.get(value) for index,value in enumerate(classes[0]) if scores[0,index] > 0.5]
根据函数签名 visualize_boxes_and_labels_on_image_array，您必须设置参数 max_boxes_to_draw、min_score_thresh、
visualize_boxes_and_labels_on_image_array(image,
boxes,
classes,
scores,
category_index,
instance_masks=None,
keypoints=None,
use_normalized_coordinates=False,
max_boxes_to_draw=20,
min_score_thresh=.5,
agnostic_mode=False,
line_thickness=4)
据我所知，您有 300 次检测。 visualize_boxes_and_labels_on_image_array 显示的很少，因为 min_score_thresh=.5（这是默认值）对大多数人来说太高了。
如果你想在输出中添加这样的过滤，你可以这样写：
min_score_thresh = 0.5
print([category_index.get(i) for i in classes[0] if scores[0, i] > min_score_thresh)
您可以更改min_score_thresh以选择您需要的阈值。打印带有类别名称的分数值可能会有用。
尝试将 min_score_thresh 设置为 0。那么您可能会看到 300 次检测。
打开visualization_utils.py并在
之后添加-->print(class_name)
else:
class_name = 'N/A'
display_str = '{}: {}%'.format(
class_name,
int(100*scores[i]))
这将打印检测到的对象
在
之后添加print(class_name)
else:
class_name = 'N/A'
display_str = '{}: {}%'.format(
class_name,
int(100*scores[i]))
in visualization_utils.py 文件打印检测到的对象。
我想知道在哪里添加打印命令来打印时间戳以及输出的准确性百分比。
// this will load the labels and categories along with category index
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)
//to print the identified object do the following :
打印类别而不是类别索引。索引包含数值，类别包含对象的名称。一旦确定了上述阈值，
min_score_thresh = 0.5
print([category.get(1)] for i in classes[0] if scores[0, i] > min_score_thresh)
这将打印识别的类别。
我正在尝试 return 使用 TF 对象检测 API 在图像中找到的对象列表。
为此，我使用 print([category_index.get(i) for i in classes[0]]) 打印已找到的对象列表或使用 print(num_detections) 显示找到的对象的数量，但在这两种情况下，它都为我提供了包含 300 个值的列表或者简单地值 [300.] 相应地。
如何才能 return 仅显示图像上的对象？或者如果有什么错误请大家帮忙看看是什么地方出了问题。
我在训练时使用 Faster RCNN 模型配置文件和检查点。确保它真的检测到图像中的几个物体，这里是：
我的代码：
import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfile
from collections import defaultdict
from io import StringIO
from matplotlib import pyplot as plt
from PIL import Image
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util
PATH_TO_CKPT = 'frozen_graph/frozen_inference_graph.pb'
PATH_TO_LABELS = 'object_detection/pascal_label_map.pbtxt'
NUM_CLASSES = 7
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 = 'object_detection/test_images/'
TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.jpg'.format(i)) for i in range(1, 2) ]
IMAGE_SIZE = (12, 8)
with detection_graph.as_default():
with tf.Session(graph=detection_graph) as sess:
sess.run(tf.global_variables_initializer())
img = 1
for image_path in TEST_IMAGE_PATHS:
image = Image.open(image_path)
image_np = load_image_into_numpy_array(image)
# Expand dimensions since the model expects images to have shape: [1, None, None, 3]
image_np_expanded = np.expand_dims(image_np, axis=0)
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
# Each box represents a part of the image where a particular object was detected.
boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
scores = detection_graph.get_tensor_by_name('detection_scores:0')
classes = detection_graph.get_tensor_by_name('detection_classes:0')
num_detections = detection_graph.get_tensor_by_name('num_detections:0')
(boxes, scores, classes, num_detections) = sess.run(
[boxes, scores, classes, num_detections],
feed_dict={image_tensor: image_np_expanded})
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=8)
plt.figure(figsize=IMAGE_SIZE)
plt.imsave('RESULTS/' + str(img) + '.jpg', image_np)
img += 1
# Return found objects
print([category_index.get(i) for i in classes[0]])
print(boxes.shape)
print(num_detections)
结果如下：
[{'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'lucky_strike_red', 'id': 4}]
(1, 300, 4)
[ 300.]
提前感谢您提供任何信息！
更新：
非常感谢所有帮助解决这个问题的人。 下面的代码行正是我所需要的，它为我提供了包含找到的对象的列表，以便我可以对它们进行其他操作。
print [category_index.get(value) for index,value in enumerate(classes[0]) if scores[0,index] > 0.5]
根据函数签名 visualize_boxes_and_labels_on_image_array，您必须设置参数 max_boxes_to_draw、min_score_thresh、
visualize_boxes_and_labels_on_image_array(image,
boxes,
classes,
scores,
category_index,
instance_masks=None,
keypoints=None,
use_normalized_coordinates=False,
max_boxes_to_draw=20,
min_score_thresh=.5,
agnostic_mode=False,
line_thickness=4)
据我所知，您有 300 次检测。 visualize_boxes_and_labels_on_image_array 显示的很少，因为 min_score_thresh=.5（这是默认值）对大多数人来说太高了。
如果你想在输出中添加这样的过滤，你可以这样写：
min_score_thresh = 0.5
print([category_index.get(i) for i in classes[0] if scores[0, i] > min_score_thresh)
您可以更改min_score_thresh以选择您需要的阈值。打印带有类别名称的分数值可能会有用。
尝试将 min_score_thresh 设置为 0。那么您可能会看到 300 次检测。
打开visualization_utils.py并在
之后添加-->print(class_name)
else:
class_name = 'N/A'
display_str = '{}: {}%'.format(
class_name,
int(100*scores[i]))
这将打印检测到的对象
在
之后添加print(class_name)
else:
class_name = 'N/A'
display_str = '{}: {}%'.format(
class_name,
int(100*scores[i]))
in visualization_utils.py 文件打印检测到的对象。
我想知道在哪里添加打印命令来打印时间戳以及输出的准确性百分比。
// this will load the labels and categories along with category index
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)
//to print the identified object do the following :
打印类别而不是类别索引。索引包含数值，类别包含对象的名称。一旦确定了上述阈值，
min_score_thresh = 0.5
print([category.get(1)] for i in classes[0] if scores[0, i] > min_score_thresh)
这将打印识别的类别。