逐行分析以下代码: def read_stop_line(self): features = load_data.load_json_file(self.all_Line_path)["features"] stop_polygon = [] pedestrian_crossing_polygon = [] for feature in features: if feature["properties"]["subtype"] == 101 or \ feature["properties"]["subtype"] == 105 or \ feature["properties"]["subtype"] == 104: my_polygon = MyPolygon(coordinates=feature["geometry"]["coordinates"][0], properties=feature["properties"]) my_polygon.temporary_properties = {} tree_polygon = my_polygon.polygon tree_polygon.id = feature["properties"]["id"] stop_polygon.append(tree_polygon) stop_line = LineString(fit_polygon(tree_polygon)) my_polygon.temporary_properties["line_string"] = stop_line self.stop_polygon[tree_polygon.id] = my_polygon elif feature["properties"]["subtype"] == 102: my_polygon = MyPolygon(coordinates=feature["geometry"]["coordinates"][0], properties=feature["properties"]) my_polygon.temporary_properties = {} tree_polygon = my_polygon.polygon tree_polygon.id = feature["properties"]["id"] pedestrian_crossing_polygon.append(tree_polygon) self.pedestrian_crossing[tree_polygon.id] = my_polygon self.stop_line_tree = STRtree(stop_polygon) self.pedestrian_crossing_tree = STRtree(pedestrian_crossing_polygon)
时间: 2024-04-29 20:20:19 浏览: 7
这段代码定义了一个名为 "read_stop_line" 的函数,它属于某个类。该函数首先从一个名为 "all_Line_path" 的路径中加载一个 JSON 文件,并获取其中的 "features" 数组。然后,它创建了两个空数组 "stop_polygon" 和 "pedestrian_crossing_polygon"。
接下来,该函数对于 "features" 数组中的每个元素,检查其 "properties" 属性下的 "subtype" 属性是否等于 101、105 或 104。如果是,它将该元素的几何坐标转化为一个多边形,并将该多边形的 ID 和一个包含其适配的线条的 LineString 对象存储在 "stop_polygon" 数组中。此外,该函数还将该多边形存储在字典 "stop_polygon" 中,以多边形 ID 作为键。
如果 "subtype" 属性等于 102,该函数将执行类似的操作,但将多边形存储在 "pedestrian_crossing_polygon" 数组和 "pedestrian_crossing" 字典中。
最后,该函数使用 "stop_polygon" 和 "pedestrian_crossing_polygon" 数组创建两个 STRtree 对象,并将它们存储在类实例对象的 "stop_line_tree" 和 "pedestrian_crossing_tree" 属性中。
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补全以下LeNet网络模型代码class LeNet(paddle.nn.Layer): def __init__(self):
class LeNet(paddle.nn.Layer):
def __init__(self):
super(LeNet, self).__init__()
self.conv1 = paddle.nn.Conv2D(in_channels=1, out_channels=6, kernel_size=5, stride=1, padding=2)
self.pool1 = paddle.nn.MaxPool2D(kernel_size=2, stride=2)
self.conv2 = paddle.nn.Conv2D(in_channels=6, out_channels=16, kernel_size=5, stride=1)
self.pool2 = paddle.nn.MaxPool2D(kernel_size=2, stride=2)
self.fc1 = paddle.nn.Linear(in_features=16*5*5, out_features=120)
self.fc2 = paddle.nn.Linear(in_features=120, out_features=84)
self.fc3 = paddle.nn.Linear(in_features=84, out_features=10)
def forward(self, x):
x = self.conv1(x)
x = F.relu(x)
x = self.pool1(x)
x = self.conv2(x)
x = F.relu(x)
x = self.pool2(x)
x = paddle.flatten(x, start_axis=1, stop_axis=-1)
x = self.fc1(x)
x = F.relu(x)
x = self.fc2(x)
x = F.relu(x)
x = self.fc3(x)
return x
class Queue: def __init__(self, limit=10): self.data = [None] * limit self.head = -1 self.tail = -1
def is_empty(self): return self.head == -1
def is_full(self): return (self.tail + 1) % len(self.data) == self.head
def enqueue(self, value): if self.is_full(): raise ValueError("Queue is full") if self.is_empty(): self.head = 0 self.tail = (self.tail + 1) % len(self.data) self.data[self.tail] = value
def dequeue(self): if self.is_empty(): raise ValueError("Queue is empty") value = self.data[self.head] if self.head == self.tail: self.head = -1 self.tail = -1 else: self.head = (self.head + 1) % len(self.data) return value
def peek(self): if self.is_empty(): raise ValueError("Queue is empty") return self.data[self.head]