Question

我目前正在研究一个tensorflow图像处理项目。标识了三类对象，分别标记为1（公共汽车），2（汽车）和3（货车）。检测到的输出对象/ objetcs作为“ numpy.ndarray”类型的输出给出。以下是我使用的代码。

# Import packages
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 = 'inference_graph'

# 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,'training','labelmap.pbtxt')

# Number of classes the object detector can identify
NUM_CLASSES = 3

## 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')

# Initialize webcam feed
video = cv2.VideoCapture(0)
ret = video.set(3,1280)
ret = video.set(4,720)

while(True):

    # Acquire frame and expand frame 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
    ret, frame = video.read()
    frame_expanded = np.expand_dims(frame, 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: frame_expanded})
    s_class = classes[scores > 0.6]
    print(s_class)

    # Draw the results of the detection (aka 'visulaize the results')
    vis_util.visualize_boxes_and_labels_on_image_array(
        frame,
        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)

    # All the results have been drawn on the frame, so it's time to display 
it.
     cv2.imshow('Object detector', frame)

    # Press 'q' to quit
    if cv2.waitKey(1) == ord('q'):
        break

# Clean up
video.release()
cv2.destroyAllWindows()

在此代码中，

s_class = classes[scores > 0.6]
print(s_class)

添加

只是为了可视化输出。输出是numpy.ndarray类型的输出，打印为，

>>[2.]    #car is detected
  [3. 2. 1.]    #all three objects(van,car,bus) are detected
  [1. 2. 3.]    #all three objects(bus,car,van) are detected
  [1. 2. 3.]
  [1. 2. 3.]
  [2. 1. 3.]
  [2. 1. 3.]
  [2. 1. 3.]
  [2. 1. 3.]
  [2. 3. 1.]
  [2. 3. 1.]
  []              #nothing is detected
  [2.]            #only second object(car) is detected
  []
  [1.]            #only first object(bus) is detected

并继续...对象未按有组织的顺序在数组中表示（例如：-[1. 2. 3.]或[2. 1. 3。] ... etc-它改变位置）。如果检测到对象2（car）和/或对象3（van），则需要打印“小”，而当检测到对象1（总线）时，则需要打印“大”。同样，检测到对象1（公共汽车）与对象2（汽车）和/或对象3（货车）时，应打印“小”。我尝试过，

if (s_class==1):
    print("large")
elif (s_class==2 or s_class==3):
    print("small")
elif (s_class==1 and (s_class==2 or s_class==3)):
    print("small")
elif (s_class==1 and s_class==2 and s_class==3 ):
    print("small")

这不起作用，因为我比较numpy.ndarray的方式可能是错误的。我怎样才能将此numpy.ndarray与以上条件进行比较以打印所需的输出？

Answer 1

假设s_class是一个或多个数组，以下代码根据子数组是否仅包含1来消除空的子数组并报告“大”或“小”：

[("large" if s.max() < 1.5 else "small") for s in s_class if s.any()]

如果要报告空子数组的某些内容而不是简单地丢弃它们，请添加另一个子句：

[(("large" if s.max() < 1.5 else "small") 
                 if s.any() else "empty") for s in s_class]

无法将“ numpy.ndarray”类型的输出与“ if”语句进行比较

1 个答案: