我使用 Faster RCNN 訓練了一個模型，這個模型用于跟隨條帶。
這是我的模型的輸出
我用來獲取此輸出的 python 代碼如下：
import cv2
import numpy as np
import tensorflow as tf
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util
IMAGE = "test6.JPG"
MODEL_NAME = 'D:/object_detection/inference_graph'
PATH_TO_CKPT = "D:/object_detection/inference_graph/frozen_inference_graph.pb"
PATH_TO_LABELS = "D:/object_detection/training/labelmap.pbtxt"
PATH_TO_IMAGE = "D:/object_detection/images/" IMAGE
NUM_CLASSES = 2
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.compat.v1.GraphDef()
with tf.compat.v2.io.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.compat.v1.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')
image = cv2.imread(PATH_TO_IMAGE)
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)
cv2.imshow('Object detector', image)
cv2.waitKey(0)
cv2.destroyAllWindows()
我的目標是到達照片中方框的坐標
為此，我嘗試了：
visulaize = 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=1,
min_score_thresh=0.90)
print(visulaize)
我試過：
perception = (boxes, scores, classes, num) = sess.run(
[detection_boxes, detection_scores, detection_classes, num_detections],
feed_dict={image_tensor: image_expanded})
print(perception)
然后我嘗試了：
n=boxes.shape[0]
for i in range(n):
if not np.any(boxes[i]):
continue
print(boxes[i])
print(np.squeeze(boxes))
最后，我嘗試了以下
x,y,h,w=boxes
print(x,y,h,w)
print(detection_boxes)
print(boxes)
x,y,w,h=detection_boxes
print(x,y,w,h)
print(np.squeenze(boxes))
print(boxes.shape)
但他們都沒有給出令人滿意的結果
我需要你的幫助才能找到盒子的坐標
uj5u.com熱心網友回復：
您需要應用 nms 并對框進行非規范化。
def apply_non_max_suppression(boxes, scores, iou_thresh=.45, top_k=200):
"""Apply non maximum suppression.
# Arguments
boxes: Numpy array, box coordinates of shape (num_boxes, 4)
where each columns corresponds to x_min, y_min, x_max, y_max
scores: Numpy array, of scores given for each box in 'boxes'
iou_thresh : float, intersection over union threshold
for removing boxes.
top_k: int, number of maximum objects per class
# Returns
selected_indices: Numpy array, selected indices of kept boxes.
num_selected_boxes: int, number of selected boxes.
"""
selected_indices = np.zeros(shape=len(scores))
if boxes is None or len(boxes) == 0:
return selected_indices
# x_min = boxes[:, 0]
# y_min = boxes[:, 1]
# x_max = boxes[:, 2]
# y_max = boxes[:, 3]
x_min = boxes[:, 1]
y_min = boxes[:, 0]
x_max = boxes[:, 3]
y_max = boxes[:, 2]
areas = (x_max - x_min) * (y_max - y_min)
remaining_sorted_box_indices = np.argsort(scores)
remaining_sorted_box_indices = remaining_sorted_box_indices[-top_k:]
num_selected_boxes = 0
while len(remaining_sorted_box_indices) > 0:
best_score_args = remaining_sorted_box_indices[-1]
selected_indices[num_selected_boxes] = best_score_args
num_selected_boxes = num_selected_boxes 1
if len(remaining_sorted_box_indices) == 1:
break
remaining_sorted_box_indices = remaining_sorted_box_indices[:-1]
best_x_min = x_min[best_score_args]
best_y_min = y_min[best_score_args]
best_x_max = x_max[best_score_args]
best_y_max = y_max[best_score_args]
remaining_x_min = x_min[remaining_sorted_box_indices]
remaining_y_min = y_min[remaining_sorted_box_indices]
remaining_x_max = x_max[remaining_sorted_box_indices]
remaining_y_max = y_max[remaining_sorted_box_indices]
inner_x_min = np.maximum(remaining_x_min, best_x_min)
inner_y_min = np.maximum(remaining_y_min, best_y_min)
inner_x_max = np.minimum(remaining_x_max, best_x_max)
inner_y_max = np.minimum(remaining_y_max, best_y_max)
inner_box_widths = inner_x_max - inner_x_min
inner_box_heights = inner_y_max - inner_y_min
inner_box_widths = np.maximum(inner_box_widths, 0.0)
inner_box_heights = np.maximum(inner_box_heights, 0.0)
intersections = inner_box_widths * inner_box_heights
remaining_box_areas = areas[remaining_sorted_box_indices]
best_area = areas[best_score_args]
unions = remaining_box_areas best_area - intersections
intersec_over_union = intersections / unions
intersec_over_union_mask = intersec_over_union <= iou_thresh
remaining_sorted_box_indices = remaining_sorted_box_indices[
intersec_over_union_mask]
return selected_indices.astype(int), num_selected_boxes
def denormalize_box(box, image_shape):
"""Scales corner box coordinates from normalized values to image dimensions.
# Arguments
box: Numpy array containing corner box coordinates.
image_shape: List of integers with (height, width).
# Returns
returns: box corner coordinates in image dimensions
"""
# x_min, y_min, x_max, y_max = box[:4]
y_min, x_min, y_max, x_max = box[:4]
height, width = image_shape
x_min = int(x_min * width)
y_min = int(y_min * height)
x_max = int(x_max * width)
y_max = int(y_max * height)
# return [x_min, y_min, x_max, y_max]
return [y_min, x_min, y_max, x_max]
(boxes, scores, classes, num) = sess.run(
[detection_boxes, detection_scores, detection_classes, num_detections],
feed_dict={image_tensor: image_expanded})
conf_threshold = 0.5
nms_threshold = 0.45
image_shape = image.shape[:2]
# Filtering the boxes based on conf_threshold
filtered_scores = [scores[0][i] for i in np.where(scores[0] > conf_threshold)]
filtered_boxes = [boxes[0][i] for i in np.where(scores[0] > conf_threshold)]
filtered_classes = [classes[0][i] for i in np.where(scores[0] > conf_threshold)]
if len(filtered_scores[0]) != 0:
# NMS thresholding
indices, count = apply_non_max_suppression(filtered_boxes[0], filtered_scores[0], nms_threshold, 200)
selected_indices = indices[:count]
## Getting the final boxes
final_boxes = filtered_boxes[0][selected_indices]
final_scores = filtered_scores[0][selected_indices]
final_classes = filtered_classes[0][selected_indices]
final_boxes = [denormalize_box(box, image_shape) for box in final_boxes]
轉載請註明出處，本文鏈接：https://www.uj5u.com/qukuanlian/431842.html
標籤：张量流 图像处理 计算机视觉 物体检测 更快的rcnn
上一篇：TypeError：<tf.Tensor...具有型別<class'tensorflow.python.framework.ops.EagerTensor'>，但