[英]Tensorflow Object Detection API - Get Coordinates of Boxes
detection_graph = tf.Graph()
with detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(MODEL_PATH, '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')
gameWindow = [0, 0, 200, 300]
while True:
image = np.array(ImageGrab.grab(bbox=(gameWindow[0], gameWindow[1], gameWindow[2], gameWindow[3])))
image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image_expanded = np.expand_dims(image_rgb, 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=8,
min_score_thresh=0.60)
frame = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# print("Made it ")
cv2.imshow('Detect the dumb trees', frame)
if cv2.waitKey(1) == 27:
break
cv2.destroyAllWindows()
I'm trying to get the x1, y1, x2, y2
coordinates of the boxes that the API draws using vis_util.visualize_boxes_and_labels_on_image_array()
我正在尝试使用vis_util.visualize_boxes_and_labels_on_image_array()
获取 API 绘制的框的x1, y1, x2, y2
坐标
I've tried looking into detection_boxes
but i get a bunch of values which I have no idea what they mean.我试过查看detection_boxes
但我得到了一堆我不知道它们是什么意思的值。
Could someone provide me a solution please?有人可以为我提供解决方案吗? Thanks谢谢
The numbers in detection_boxes are [ymin, xmin, ymax, xmax] and they are normalised to the size of your image since "use_normalized_coordinates=True" in your script. detection_boxes 中的数字是 [ymin, xmin, ymax, xmax] 并且由于脚本中的“use_normalized_coordinates=True”,它们被标准化为图像的大小。 Each index in the detection_boxes correspond to the same index in the detection_scores and Detection_classes. detection_boxes 中的每个索引对应于 detection_scores 和 Detection_classes 中的相同索引。 So you have to find what is the object you want at what threshold score in order to get the index for the detection_box.因此,您必须在什么阈值分数下找到您想要的对象,才能获得检测框的索引。 Example:例子:
boxes=[]
for i in range(len(detection_boxes)):
if detection_classes[i]=3 and detection_scores[i]>0.9:
boxes.append(detection_boxes[i])
The score threshold set here is 0.9 and the class i am looking for is 3. Those box that match are stored in an array call boxes.此处设置的分数阈值是 0.9,我要查找的类是 3。那些匹配的框存储在数组调用框中。
This question seems similar to yours: How to find bounding boxes coordinates in Tensorflow Object Detection API这个问题似乎与您的相似: 如何在 Tensorflow 对象检测 API 中找到边界框坐标
And someone has posted a simple code solution.有人发布了一个简单的代码解决方案。
There is a another way, where you can manipulate the visualize_boxes_and_labels_on_image_array() function to return the coordinates Something like:还有另一种方法,您可以在其中操作visualize_boxes_and_labels_on_image_array() 函数来返回坐标,例如:
coordinates_list = []
for box, color in box_to_color_map.items():
ymin, xmin, ymax, xmax = box
height, width, channels = image.shape
ymin = int(ymin*height)
ymax = int(ymax*height)
xmin = int(xmin*width)
xmax = int(xmax*width)
coordinates_list.append([xmin, ymin, xmax, ymax])
return coordinates_list
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