coord.shape
时间: 2024-06-13 15:04:05 浏览: 19
引用和引用[2]中的代码都使用了numpy数组coord,而coord.shape是numpy数组的一个属性,用于返回数组的维度。具体来说,它返回一个元组,其中包含数组的行数和列数(如果是二维数组)。因此,如果coord是一个二维数组,那么coord.shape将返回一个形如(h, w)的元组,其中h是数组的行数,w是数组的列数。在引用和引用中,coord.shape被用来获取数组的行数和列数,以便在循环中遍历数组的每个元素。
相关问题
以下代码有什么错误,怎么修改: import tensorflow.compat.v1 as tf tf.disable_v2_behavior() from PIL import Image import matplotlib.pyplot as plt import input_data import model import numpy as np import xlsxwriter num_threads = 4 def evaluate_one_image(): workbook = xlsxwriter.Workbook('formatting.xlsx') worksheet = workbook.add_worksheet('My Worksheet') with tf.Graph().as_default(): BATCH_SIZE = 1 N_CLASSES = 4 image = tf.cast(image_array, tf.float32) image = tf.image.per_image_standardization(image) image = tf.reshape(image, [1, 208, 208, 3]) logit = model.cnn_inference(image, BATCH_SIZE, N_CLASSES) logit = tf.nn.softmax(logit) x = tf.placeholder(tf.float32, shape=[208, 208, 3]) logs_train_dir = 'log/' saver = tf.train.Saver() with tf.Session() as sess: print("从指定路径中加载模型...") ckpt = tf.train.get_checkpoint_state(logs_train_dir) if ckpt and ckpt.model_checkpoint_path: global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1] saver.restore(sess, ckpt.model_checkpoint_path) print('模型加载成功, 训练的步数为: %s' % global_step) else: print('模型加载失败,checkpoint文件没找到!') prediction = sess.run(logit, feed_dict={x: image_array}) max_index = np.argmax(prediction) workbook.close() def evaluate_images(test_img): coord = tf.train.Coordinator() threads = tf.train.start_queue_runners(coord=coord) for index,img in enumerate(test_img): image = Image.open(img) image = image.resize([208, 208]) image_array = np.array(image) tf.compat.v1.threading.Thread(target=evaluate_one_image, args=(image_array, index)).start() coord.request_stop() coord.join(threads) if __name__ == '__main__': test_dir = 'data/test/' import glob import xlwt test_img = glob.glob(test_dir + '*.jpg') evaluate_images(test_img)
在代码中,我注意到了几个错误:
1. 在导入`input_data`和`model`模块时,代码中没有找到这两个模块的导入语句。
2. `evaluate_one_image`函数中的`image_array`参数未定义。
3. 在`evaluate_images`函数中,`tf.train.Coordinator()`和`tf.train.start_queue_runners()`函数之间缺少了一个空行。
4. 在调用`evaluate_one_image`函数时,传递的参数应该是`(image_array, index)`而不是`(image_array, index)`。
以下是修改后的代码:
```python
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
from PIL import Image
import matplotlib.pyplot as plt
import input_data
import model
import numpy as np
import xlsxwriter
num_threads = 4
def evaluate_one_image(image_array, index):
workbook = xlsxwriter.Workbook('formatting.xlsx')
worksheet = workbook.add_worksheet('My Worksheet')
with tf.Graph().as_default():
BATCH_SIZE = 1
N_CLASSES = 4
image = tf.cast(image_array, tf.float32)
image = tf.image.per_image_standardization(image)
image = tf.reshape(image, [1, 208, 208, 3])
logit = model.cnn_inference(image, BATCH_SIZE, N_CLASSES)
logit = tf.nn.softmax(logit)
x = tf.placeholder(tf.float32, shape=[208, 208, 3])
logs_train_dir = 'log/'
saver = tf.train.Saver()
with tf.Session() as sess:
print("从指定路径中加载模型...")
ckpt = tf.train.get_checkpoint_state(logs_train_dir)
if ckpt and ckpt.model_checkpoint_path:
global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
saver.restore(sess, ckpt.model_checkpoint_path)
print('模型加载成功, 训练的步数为: %s' % global_step)
else:
print('模型加载失败,checkpoint文件没找到!')
prediction = sess.run(logit, feed_dict={x: image_array})
max_index = np.argmax(prediction)
workbook.close()
def evaluate_images(test_img):
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
for index, img in enumerate(test_img):
image = Image.open(img)
image = image.resize([208, 208])
image_array = np.array(image)
tf.compat.v1.threading.Thread(target=evaluate_one_image, args=(image_array, index)).start()
coord.request_stop()
coord.join(threads)
if __name__ == '__main__':
test_dir = 'data/test/'
import glob
import xlwt
test_img = glob.glob(test_dir + '*.jpg')
evaluate_images(test_img)
```
请注意,我只修复了我在代码中发现的错误,并没有对其他部分进行修改。如果还有其他错误或问题,请提供更多信息,以便我能够更好地帮助你。
results = [] if polygon.shape[0] > 0: for label, score, seg in zip(class_ids, conf, polygon): # 将多边形的点坐标转换为列表形式 points = [[float(coord[0]), float(coord[1])] for coord in contour.squeeze().tolist()] if len(contour) > 0 else [] results.append({ "confidence": str(score), "label": labels.get(label, "unknown"), "points": points, "type": "polygon" 检查一下
代码中的 `points` 部分在使用 `contour` 变量时出现错误。根据之前的代码,应该使用 `seg` 变量来转换多边形的点坐标。以下是修改后的代码:
```python
results = []
if polygon.shape[0] > 0:
for label, score, seg in zip(class_ids, conf, polygon):
# 将多边形的点坐标转换为列表形式
points = [[float(coord[0]), float(coord[1])] for coord in seg]
results.append({
"confidence": str(score),
"label": labels.get(label, "unknown"),
"points": points,
"type": "polygon"
})
```
现在,代码将正确地将多边形的点坐标转换为浮点数类型,并将其作为 `points` 字段的值添加到结果中。
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