Eggs and Egg Info

最新推荐文章于 2025-06-03 09:14:29 发布
转载 最新推荐文章于 2025-06-03 09:14:29 发布 · 2.8k 阅读 · 1

本文介绍了Python Eggs的概念及其两种主要格式：`.egg` 和 `.egg-info`。这两种格式分别适用于不同的应用场景，前者更适合于发布和轻松卸载或升级代码，而后者则更侧重于向后兼容性和安装简便性。
----------------------
Eggs and their Formats
----------------------


A "Python egg" is a logical structure embodying the release of a
specific version of a Python project, comprising its code, resources,
and metadata. There are multiple formats that can be used to physically
encode a Python egg, and others can be developed. However, a key
principle of Python eggs is that they should be discoverable and
importable. That is, it should be possible for a Python application to
easily and efficiently find out what eggs are present on a system, and
to ensure that the desired eggs' contents are importable.


There are two basic formats currently implemented for Python eggs:


1. ``.egg`` format: a directory or zipfile *containing* the project's
   code and resources, along with an ``EGG-INFO`` subdirectory that
   contains the project's metadata


2. ``.egg-info`` format: a file or directory placed *adjacent* to the
   project's code and resources, that directly contains the project's
   metadata.


Both formats can include arbitrary Python code and resources, including
static data files, package and non-package directories, Python
modules, C extension modules, and so on.  But each format is optimized
for different purposes.


The ``.egg`` format is well-suited to distribution and the easy
uninstallation or upgrades of code, since the project is essentially
self-contained within a single directory or file, unmingled with any
other projects' code or resources.  It also makes it possible to have
multiple versions of a project simultaneously installed, such that
individual programs can select the versions they wish to use.


The ``.egg-info`` format, on the other hand, was created to support
backward-compatibility, performance, and ease of installation for system
packaging tools that expect to install all projects' code and resources
to a single directory (e.g. ``site-packages``).  Placing the metadata
in that same directory simplifies the installation process, since it
isn't necessary to create ``.pth`` files or otherwise modify
``sys.path`` to include each installed egg.


Its disadvantage, however, is that it provides no support for clean
uninstallation or upgrades, and of course only a single version of a
project can be installed to a given directory. Thus, support from a
package management tool is required. (This is why setuptools' "install"
command refers to this type of egg installation as "single-version,
externally managed".)  Also, they lack sufficient data to allow them to
be copied from their installation source.  easy_install can "ship" an
application by copying ``.egg`` files or directories to a target
location, but it cannot do this for ``.egg-info`` installs, because
there is no way to tell what code and resources belong to a particular
egg -- there may be several eggs "scrambled" together in a single
installation location, and the ``.egg-info`` format does not currently
include a way to list the files that were installed.  (This may change
in a future version.)
GitHub项目egg-info模块
danger2的博客
12-27 807
文件夹通常包含有关 Python 包（package）的元数据信息。这些信息包括包的名称、版本、作者、依赖项等。文件夹中的元数据信息对于包的安装、卸载、版本管理等操作都是非常重要的。文件夹的创建是由 setuptools 库负责的，它是 Python 的一个包管理工具。文件夹中的元数据信息会被用来确定包的名称、版本和依赖关系等。卸载时，也会用到这些信息。文件夹中的元数据会包含在发布的包中，以提供关于包的详细信息。可以查看包的版本信息，确保使用的是正确的版本。中的元数据，以确保所需的依赖项已经满足。
Python第三方包的egg info 是什么东西
kyle1314608的博客
07-19 3236
xxx.egg-info 一般与 xxx文件夹同时存在，一起来表示完整模块。
库名.egg-info是什么？
m0_63372012的博客
08-08 1489
目录是由setuptools生成的，用于存储关于包的元数据和依赖信息。它在包的安装和管理过程中起着重要作用，确保包管理工具能够正确处理该包及其依赖关系。
iPython的安装及问题解决 报错egg_info
weixin_30619101的博客
01-07 280
https://www.jianshu.com/p/8779eac7f313 转载于:https://www.cnblogs.com/Baronboy/p/8228424.html
python egg-info vs dist-info
天涯路的专栏
03-25 2497
而 dist-info 是 Python 3.3 之后新增的包格式，用于描述 wheel 包和源码包的元数据。与 egg-info 不同的是，dist-info 文件夹中还包含了一些其他的元数据信息，如包的 hash 值、入口点、文件列表等。总的来说，dist-info 是 Python 3.3 之后的标准包格式，用于描述 wheel 包和源码包的元数据。在 Python 中，egg-info 和 dist-info 文件夹都是用于描述和管理 Python 包的元数据的文件夹。
eggs和egg是什么意思_egg是什么意思
weixin_39845221的博客
12-21 5916
egg[英][eɡ][美][?ɡ]n.鸡蛋，蛋； 卵； 家伙； vt.煽动，怂恿； 用蛋北天问答网烹调； 向人扔鸡蛋； 第三人称单数：eggs过去分词：egged复数：eggs现在进行时：egging过去式：。egg 英 [eg] 美意思 [?ɡ] n. 蛋；卵子；家伙；鸡蛋vt. 煽动；怂恿egg [简明英汉词典] n.蛋， 鸡蛋， 卵 v.怂恿， 怂恿egg指的是，1. 鸡蛋的英文，egg。...
ERROR: Command errored out with exit status 1: python setup.py egg_info Check the logs for full
热门推荐
Jacob's blog
11-29 8万+
我发现自己的Debug能力还是不行，特别是环境上的一些问题，以后一定要把错误的内容通读！！！！！通读！！！！！ 我现在是在安装mmcv，像这里的错误，核心内容是一个WARNING和两个ERROR，WARNING的内容一般是不重要的，然后看看ERROR，大概就是pytest-runner的版本找不到，尝试安装一波： pip install --user pytest-runner 就好了，这错误我找...
import serial import numpy as np import matplotlib.pyplot as plt import matplotlib.animation as animation import time from matplotlib.patches import Rectangle, Circle from pylab import mpl mpl.rcParams["font.sans-serif"] = ["SimHei"] mpl.rcParams["axes.unicode_minus"] = False class EggCounter: def __init__(self): self.grid_size = (8, 8) self.cover_width = 100 # 宽度100mm self.cell_size = self.cover_width / 8 self.background = None self.bg_alpha = 0.1 self.next_id = 0 self.tracked_eggs = [] # 跟踪的鸡蛋对象 self.max_missed = 5 self.depth_threshold = 15 self.min_points = 3 self.exit_threshold = 30 # 右侧出口触发位置(mm)，传送方向从左到右 self.total_count = 0 self.speed_estimation = [] # 用于速度估计的历史位置 def _grid_to_physical(self, i, j): """将网格坐标转换为物理坐标（传送方向从左到右）""" x = (j - 3.5) * self.cell_size # X: 传送方向（从左到右） y = (i - 3.5) * self.cell_size # Y: 宽度方向（垂直于传送） return x, y def _cluster_points(self, mask): """对前景点进行聚类并返回中心坐标""" clusters = [] visited = np.zeros_like(mask, dtype=bool) def dfs(i, j, points): stack = [(i, j)] while stack: x, y = stack.pop() if visited[x, y] or not mask[x, y]: continue visited[x, y] = True points.append((x, y)) for dx in (-1, 0, 1): for dy in (-1, 0, 1): nx, ny = x+dx, y+dy if 0 <= nx < 8 and 0 <= ny < 8: stack.append((nx, ny)) return points for i in range(8): for j in range(8): if mask[i, j] and not visited[i, j]: cluster = dfs(i, j, []) if len(cluster) >= self.min_points: centers = [self._grid_to_physical(*p) for p in cluster] cx = np.mean([c[0] for c in centers]) cy = np.mean([c[1] for c in centers]) clusters.append((cx, cy)) return clusters def _update_background(self, frame): """动态更新背景模型""" if self.background is None: self.background = frame.copy() else: foreground = (self.background - frame) > self.depth_threshold self.background[~foreground] = ( (1 - self.bg_alpha) * self.background[~foreground] + self.bg_alpha * frame[~foreground] ) def _match_and_track(self, current_eggs): """匹配当前帧检测到的鸡蛋与跟踪目标""" # 如果没有跟踪目标，全部初始化为新目标 if not self.tracked_eggs: for pos in current_eggs: self.tracked_eggs.append({ 'id': self.next_id, 'pos': pos, 'history': [pos], # 位置历史用于速度估计 'counted': False, 'missed': 0 }) self.next_id += 1 return # 计算目标间的距离成本矩阵 cost_matrix = np.zeros((len(self.tracked_eggs), len(current_eggs))) for i, egg in enumerate(self.tracked_eggs): for j, pos in enumerate(current_eggs): # 使用欧氏距离作为匹配成本 dist = np.sqrt((egg['pos'][0]-pos[0])**2 + (egg['pos'][1]-pos[1])**2) cost_matrix[i, j] = dist # 使用匈牙利算法进行匹配 row_idx, col_idx = linear_sum_assignment(cost_matrix) # 更新匹配的目标 matched_targets = set() matched_detections = set() for i, j in zip(row_idx, col_idx): if cost_matrix[i, j] < 20: # 距离阈值20mm self.tracked_eggs[i]['pos'] = current_eggs[j] self.tracked_eggs[i]['history'].append(current_eggs[j]) if len(self.tracked_eggs[i]['history']) > 5: self.tracked_eggs[i]['history'].pop(0) self.tracked_eggs[i]['missed'] = 0 matched_targets.add(i) matched_detections.add(j) # 处理未匹配的目标（丢失） for i, egg in enumerate(self.tracked_eggs): if i not in matched_targets: egg['missed'] += 1 # 添加新的检测目标 for j, pos in enumerate(current_eggs): if j not in matched_detections: self.tracked_eggs.append({ 'id': self.next_id, 'pos': pos, 'history': [pos], 'counted': False, 'missed': 0 }) self.next_id += 1 def process_frame(self, frame): """ 处理一帧深度数据并返回检测到的鸡蛋位置 :param frame: 8x8深度矩阵 (单位：mm) :return: (当前检测到的鸡蛋位置, 总计数) """ # 1. 更新背景模型 self._update_background(frame) # 2. 前景检测 (鸡蛋深度小于背景) foreground = (self.background - frame) > self.depth_threshold # 3. 聚类检测到的鸡蛋 current_eggs = self._cluster_points(foreground) # 4. 目标跟踪与匹配 self._match_and_track(current_eggs) # 5. 计数逻辑（鸡蛋从右侧离开时计数） for egg in self.tracked_eggs.copy(): # 如果鸡蛋在出口区域且未计数 if not egg['counted'] and egg['pos'][0] >= self.exit_threshold: self.total_count += 1 egg['counted'] = True # 标记为已计数但继续跟踪直到离开视野 # 移出检测区域（右侧太远或宽度方向超出范围） if abs(egg['pos'][1]) > 50 or egg['pos'][0] > 60: self.tracked_eggs.remove(egg) return [egg['pos'] for egg in self.tracked_eggs if not egg['counted']], self.total_count def init_serial(port='COM12', baudrate=115200): """初始化串口连接""" return serial.Serial(port, baudrate, timeout=10) def create_blackwhite_colormap(): """创建黑白渐变色图""" colors = [(0.95, 0.95, 0.95), (0, 0, 0)] # 浅灰到黑 return plt.cm.colors.LinearSegmentedColormap.from_list('bw', colors, N=256) def init_plot(): """初始化深度图显示（传送方向从左到右）""" fig = plt.figure(figsize=(14, 7)) fig.suptitle('VL53L5CX 鸡蛋计数系统（传送方向：左→右）', fontsize=16) # 深度图区域 ax1 = fig.add_subplot(121) cmap = create_blackwhite_colormap() img = ax1.imshow(np.zeros((8,8)), cmap=cmap, vmin=0, vmax=500) plt.colorbar(img, label='距离(mm)', ax=ax1) ax1.set_title('8x8 深度图') ax1.set_xlabel('传送方向（左→右）') ax1.set_ylabel('宽度方向') # 添加传送带表示（红色表示出口） ax1.add_patch(Rectangle([-0.5, -0.5], 8, 8, fill=False, edgecolor='red', linestyle='--')) ax1.text(7.5, 3.5, '→', color='red', fontsize=20, ha='center') # 传送方向箭头 # 鸡蛋位置可视化区域 ax2 = fig.add_subplot(122) ax2.set_xlim(-60, 60) ax2.set_ylim(-60, 60) ax2.set_title('鸡蛋位置检测') ax2.set_xlabel('传送方向(mm) (左→右)') ax2.set_ylabel('宽度方向(mm)') # 添加检测区域和计数线 ax2.add_patch(Rectangle([-50, -50], 100, 100, fill=False, edgecolor='blue')) ax2.axvline(x=30, color='r', linestyle='--', alpha=0.7) # 出口计数线 ax2.text(32, -45, '计数线', color='r', rotation=90) # 添加传送方向箭头 ax2.arrow(-40, 50, 70, 0, head_width=5, head_length=5, fc='r', ec='r') ax2.text(0, 55, '传送方向', color='r', ha='center') # 鸡蛋计数显示 count_text = ax2.text(0, 45, '鸡蛋计数: 0', ha='center', va='center', fontsize=24, color='green', bbox=dict(facecolor='white', alpha=0.8)) # 帧率显示 fps_text = ax2.text(0, -45, 'FPS: 0.0', ha='center', va='center', fontsize=12, color='blue') # 当前跟踪目标显示 track_text = ax2.text(40, -45, '跟踪目标: 0', ha='left', va='center', fontsize=12, color='purple') # 鸡蛋位置点容器 egg_points = ax2.scatter([], [], s=100, c='red', edgecolors='black', alpha=0.7) return fig, img, ax2, count_text, fps_text, track_text, egg_points def update_frame(frame, ser, img, ax, count_text, fps_text, track_text, egg_points, counter, last_time): """更新深度图帧并处理鸡蛋计数（传送方向从左到右）""" current_time = time.time() fps = 1 / (current_time - last_time[0]) if current_time != last_time[0] else 0 last_time[0] = current_time try: # 读取串口数据 data = ser.readline().decode('utf-8').strip() if data:# and data.startswith('[') and data.endswith(']'): # 处理数据格式: [123.4, 125.6, ...] #data = data[1:-1] # 移除方括号 distances = list(map(float, data.split(','))) if len(distances) == 64: depth_map = np.array(distances).reshape(8,8) # 更新深度图显示 img.set_array(depth_map) img.autoscale() # 鸡蛋检测与计数 eggs, total_count = counter.process_frame(depth_map) # 更新鸡蛋位置散点图 if eggs: x_pos = [egg[0] for egg in eggs] y_pos = [egg[1] for egg in eggs] egg_points.set_offsets(np.column_stack([x_pos, y_pos])) else: egg_points.set_offsets([]) # 更新计数显示 count_text.set_text(f'鸡蛋计数: {total_count}') fps_text.set_text(f'FPS: {fps:.1f}') track_text.set_text(f'跟踪目标: {len(counter.tracked_eggs)}') except Exception as e: print(f"Error: {e}") return img, egg_points, count_text, fps_text, track_text def main(): ser = init_serial() counter = EggCounter() fig, img, ax2, count_text, fps_text, track_text, egg_points = init_plot() last_time = [time.time()] ani = animation.FuncAnimation( fig, update_frame, fargs=(ser, img, ax2, count_text, fps_text, track_text, egg_points, counter, last_time), interval=50, blit=True ) plt.tight_layout() plt.subplots_adjust(top=0.9) plt.show() ser.close() if __name__ == "__main__": from scipy.optimize import linear_sum_assignment # 确保导入 main()
09-11
for i, egg in enumerate(self.tracked_eggs): for j, pos in enumerate(current_eggs): # 使用欧氏距离作为匹配成本 dist = np.sqrt((egg['pos'][0]-pos[0])**2 + (egg['pos'][1]-pos[1])**2) cost_matrix[i, ...
import serial import numpy as np import matplotlib.pyplot as plt import matplotlib.animation as animation import time from matplotlib.patches import Rectangle, Circle from pylab import mpl from scipy.optimize import linear_sum_assignment mpl.rcParams["font.sans-serif"] = ["SimHei"] mpl.rcParams["axes.unicode_minus"] = False import numpy as np from scipy.optimize import linear_sum_assignment class EggCounter: def __init__(self): self.grid_size = (8, 8) self.cover_width = 100 # 宽度100mm self.cell_size = self.cover_width / 8 self.background = None self.bg_alpha = 0.1 self.next_id = 0 self.tracked_eggs = [] # 跟踪的鸡蛋对象 self.max_missed = 5 self.depth_threshold = 15 self.min_points = 3 self.exit_threshold = 30 # 右侧出口触发位置(mm)，传送方向从左到右 self.total_count = 0 self.speed_estimation = [] # 用于速度估计的历史位置 def _grid_to_physical(self, i, j): """将网格坐标转换为物理坐标（传送方向从左到右）""" x = (j - 3.5) * self.cell_size # X: 传送方向（从左到右） y = (i - 3.5) * self.cell_size # Y: 宽度方向（垂直于传送） return x, y def _cluster_points(self, mask): """对前景点进行聚类并返回中心坐标""" # 确保mask是二维数组 if mask.ndim == 1 or mask.shape != self.grid_size: try: mask = mask.reshape(self.grid_size) except ValueError: print(f"无法重塑数组形状: {mask.shape} -> {self.grid_size}") return [] clusters = [] visited = np.zeros(self.grid_size, dtype=bool) def dfs(i, j, points): """深度优先搜索聚类点""" stack = [(i, j)] while stack: x, y = stack.pop() # 确保索引在有效范围内 if x < 0 or x >= self.grid_size[0] or y < 0 or y >= self.grid_size[1]: continue if visited[x, y] or not mask[x, y]: continue visited[x, y] = True points.append((x, y)) # 8邻域搜索 for dx in (-1, 0, 1): for dy in (-1, 0, 1): if dx == 0 and dy == 0: continue nx, ny = x + dx, y + dy # 确保新坐标在网格内 if 0 <= nx < self.grid_size[0] and 0 <= ny < self.grid_size[1]: stack.append((nx, ny)) return points for i in range(self.grid_size[0]): for j in range(self.grid_size[1]): if mask[i, j] and not visited[i, j]: cluster = dfs(i, j, []) if len(cluster) >= self.min_points: centers = [self._grid_to_physical(*p) for p in cluster] cx = np.mean([c[0] for c in centers]) cy = np.mean([c[1] for c in centers]) clusters.append((cx, cy)) return clusters def _update_background(self, frame): """动态更新背景模型""" # 确保frame是二维数组 if frame.ndim == 1 or frame.shape != self.grid_size: try: frame = frame.reshape(self.grid_size) except ValueError: print(f"无法重塑背景数组形状: {frame.shape} -> {self.grid_size}") return if self.background is None: self.background = frame.copy().astype(float) else: # 确保背景和当前帧形状一致 if self.background.shape != frame.shape: try: self.background = self.background.reshape(frame.shape) except ValueError: print("形状不匹配，无法更新背景") return foreground = (self.background - frame) > self.depth_threshold self.background[~foreground] = ( (1 - self.bg_alpha) * self.background[~foreground] + self.bg_alpha * frame[~foreground] ) def _match_and_track(self, current_eggs): """匹配当前帧检测到的鸡蛋与跟踪目标""" # 如果没有跟踪目标，全部初始化为新目标 if not self.tracked_eggs: for pos in current_eggs: self.tracked_eggs.append({ 'id': self.next_id, 'pos': pos, 'history': [pos], # 位置历史用于速度估计 'counted': False, 'missed': 0 }) self.next_id += 1 return # 计算目标间的距离成本矩阵 cost_matrix = np.zeros((len(self.tracked_eggs), len(current_eggs))) for i, egg in enumerate(self.tracked_eggs): for j, pos in enumerate(current_eggs): # 使用欧氏距离作为匹配成本 dist = np.sqrt((egg['pos'][0]-pos[0])**2 + (egg['pos'][1]-pos[1])**2) cost_matrix[i, j] = dist # 使用匈牙利算法进行匹配 row_idx, col_idx = linear_sum_assignment(cost_matrix) # 更新匹配的目标 matched_targets = set() matched_detections = set() for i, j in zip(row_idx, col_idx): if cost_matrix[i, j] < 20: # 距离阈值20mm self.tracked_eggs[i]['pos'] = current_eggs[j] self.tracked_eggs[i]['history'].append(current_eggs[j]) if len(self.tracked_eggs[i]['history']) > 5: self.tracked_eggs[i]['history'].pop(0) self.tracked_eggs[i]['missed'] = 0 matched_targets.add(i) matched_detections.add(j) # 处理未匹配的目标（丢失） for i, egg in enumerate(self.tracked_eggs): if i not in matched_targets: egg['missed'] += 1 # 添加新的检测目标 for j, pos in enumerate(current_eggs): if j not in matched_detections: self.tracked_eggs.append({ 'id': self.next_id, 'pos': pos, 'history': [pos], 'counted': False, 'missed': 0 }) self.next_id += 1 def process_frame(self, frame): """ 处理一帧深度数据并返回检测到的鸡蛋位置 :param frame: 8x8深度矩阵 (单位：mm) :return: (当前检测到的鸡蛋位置, 总计数) """ # 确保frame是二维数组 if frame.ndim == 1: try: frame = frame.reshape(self.grid_size) except ValueError: print(f"无法处理帧数据: {frame.shape}") return [], self.total_count # 1. 更新背景模型 self._update_background(frame) # 2. 前景检测 (鸡蛋深度小于背景) foreground = (self.background - frame) > self.depth_threshold # 3. 聚类检测到的鸡蛋 current_eggs = self._cluster_points(foreground) # 4. 目标跟踪与匹配 self._match_and_track(current_eggs) # 5. 计数逻辑（鸡蛋从右侧离开时计数） for egg in self.tracked_eggs.copy(): # 如果鸡蛋在出口区域且未计数 if not egg['counted'] and egg['pos'][0] >= self.exit_threshold: self.total_count += 1 egg['counted'] = True # 标记为已计数但继续跟踪直到离开视野 # 移出检测区域（右侧太远或宽度方向超出范围） if abs(egg['pos'][1]) > 50 or egg['pos'][0] > 60: self.tracked_eggs.remove(egg) return [egg['pos'] for egg in self.tracked_eggs if not egg['counted']], self.total_count def init_serial(port='COM12', baudrate=115200): """初始化串口连接""" return serial.Serial(port, baudrate, timeout=10) def create_blackwhite_colormap(): """创建黑白渐变色图""" colors = [(0.95, 0.95, 0.95), (0, 0, 0)] # 浅灰到黑 return plt.cm.colors.LinearSegmentedColormap.from_list('bw', colors, N=256) def init_plot(): """初始化深度图显示（传送方向从左到右）""" fig = plt.figure(figsize=(14, 7)) fig.suptitle('VL53L5CX 鸡蛋计数系统（传送方向：左→右）', fontsize=16) # 深度图区域 ax1 = fig.add_subplot(121) cmap = create_blackwhite_colormap() img = ax1.imshow(np.zeros((8,8)), cmap=cmap, vmin=0, vmax=500) plt.colorbar(img, label='距离(mm)', ax=ax1) ax1.set_title('8x8 深度图') ax1.set_xlabel('传送方向（左→右）') ax1.set_ylabel('宽度方向') # 添加传送带表示（红色表示出口） ax1.add_patch(Rectangle([-0.5, -0.5], 8, 8, fill=False, edgecolor='red', linestyle='--')) ax1.text(7.5, 3.5, '→', color='red', fontsize=20, ha='center') # 传送方向箭头 # 鸡蛋位置可视化区域 ax2 = fig.add_subplot(122) ax2.set_xlim(-60, 60) ax2.set_ylim(-60, 60) ax2.set_title('鸡蛋位置检测') ax2.set_xlabel('传送方向(mm) (左→右)') ax2.set_ylabel('宽度方向(mm)') # 添加检测区域和计数线 ax2.add_patch(Rectangle([-50, -50], 100, 100, fill=False, edgecolor='blue')) ax2.axvline(x=30, color='r', linestyle='--', alpha=0.7) # 出口计数线 ax2.text(32, -45, '计数线', color='r', rotation=90) # 添加传送方向箭头 ax2.arrow(-40, 50, 70, 0, head_width=5, head_length=5, fc='r', ec='r') ax2.text(0, 55, '传送方向', color='r', ha='center') # 鸡蛋计数显示 count_text = ax2.text(0, 45, '鸡蛋计数: 0', ha='center', va='center', fontsize=24, color='green', bbox=dict(facecolor='white', alpha=0.8)) # 帧率显示 fps_text = ax2.text(0, -45, 'FPS: 0.0', ha='center', va='center', fontsize=12, color='blue') # 当前跟踪目标显示 track_text = ax2.text(40, -45, '跟踪目标: 0', ha='left', va='center', fontsize=12, color='purple') # 鸡蛋位置点容器 egg_points = ax2.scatter([], [], s=100, c='red', edgecolors='black', alpha=0.7) return fig, img, ax2, count_text, fps_text, track_text, egg_points def update_frame(frame, ser, img, ax, count_text, fps_text, track_text, egg_points, counter, last_time): """更新深度图帧并处理鸡蛋计数（传送方向从左到右）""" current_time = time.time() fps = 1 / (current_time - last_time[0]) if current_time != last_time[0] else 0 last_time[0] = current_time try: # 读取串口数据 data = ser.readline().decode('utf-8').strip() if data: distances = list(map(float, data.split(','))) if len(distances) == 64: depth_map = np.array(distances).reshape(8,8) # 更新深度图显示 img.set_array(depth_map) img.autoscale() # 鸡蛋检测与计数 eggs, total_count = counter.process_frame(depth_map) # 更新鸡蛋位置散点图 if eggs: x_pos = [egg[0] for egg in eggs] y_pos = [egg[1] for egg in eggs] egg_points.set_offsets(np.column_stack([x_pos, y_pos])) else: egg_points.set_offsets([]) # 更新计数显示 count_text.set_text(f'鸡蛋计数: {total_count}') fps_text.set_text(f'FPS: {fps:.1f}') track_text.set_text(f'跟踪目标: {len(counter.tracked_eggs)}') except Exception as e: print(f"Error: {e}") return img, egg_points, count_text, fps_text, track_text def main(): ser = init_serial() counter = EggCounter() fig, img, ax2, count_text, fps_text, track_text, egg_points = init_plot() last_time = [time.time()] ani = animation.FuncAnimation( fig, update_frame, fargs=(ser, img, ax2, count_text, fps_text, track_text, egg_points, counter, last_time), interval=50, blit=True, save_count=1000 ) plt.tight_layout() plt.subplots_adjust(top=0.9) plt.show() ser.close() if __name__ == "__main__": from scipy.optimize import linear_sum_assignment # 确保导入 main()
最新发布
09-12
return [egg['pos'] for egg in self.tracked_eggs if not egg['counted']], self.total_count ``` 这样，我们同时处理了两种移除情况。 除此之外，我们还需要注意： - 背景初始化：第一帧作为背景，然后动态更新...
pcdet.egg-info/SOURCES.txt
W1995S的博客
03-25 453
README.md setup.py pcdet/__init__.py pcdet/config.py pcdet/version.py pcdet.egg-info/PKG-INFO pcdet.egg-info/SOURCES.txt pcdet.egg-info/dependency_links.txt pcdet.egg-info/requires.txt pcdet.egg-info/top_level.txt pcdet/datasets/__init__.py pcdet/datasets/
成功解决：python setup.py egg_info did not run successfully.
naicha2的博客
11-22 3211
如果还不行，那就指定setuptools版本，使用比较低的版本。这是因为setuptools版本与python版本不兼容。一,更新pip更新setuptools。
pip install, python setup.py, egg-info的说明--以Nvidia Apex安装为例
xiaojiajia007的博客
02-12 8214
Install Nvidia Apex 若第一次安装需要把项目从github克隆到本地 Clean the old install before rebuilding: pip uninstall apex cd apex_repo_dir rm -rf build (if present) rm -rf apex.egg-info (if present) Install package： ...
解决 Command "python setup.py egg_info" failed ...问题的参考 :
qq_24044813的博客
03-10 3万+
“pip install unroll”: “python setup.py egg_info” failed with error code 1 解决 Command “python setup.py egg_info” failed with error code 1 问题 在执行 pip install pyautgui 时遇到错误： Command “python setup.py egg...
Python egg-info目录(setuptools)(.egg文件)
hxxjxw的博客
02-15 3305
根据官方文件："Eggs are to Pythons as Jars are to Java..." ， python 中的egg文件；类似于Java 中的jar 包，把一系列的python源码文件、元数据文件、其他资源文件 zip 压缩，重新命名为.egg 文件，目的是作为一个整体进行发布。 ......
解决安装Optuna时的‘egg-info’命令错误
weixin_30789053的博客
06-03 1070
在尝试安装optuna这一用于机器学习超参数优化的Python库时，许多开发者可能会遇到一个常见的错误信息：。这一错误通常指示用户在使用pip或setuptools安装时遇到了问题。虽然初看起来，这个问题似乎非常基础，但它却可能涉及复杂的环境配置，或是版本不兼容等深层次的原因。这个问题的根源多种多样，比如可能是因为用户的setuptools版本过旧，或者是Python环境配置不当，甚至是操作系统的权限设置问题。在进行优化之前，我们必须深入分析问题发生的上下文。
成功解决ERROR: No .egg-info directory found in C:\Users\admin\AppData\Local\Temp\pip-pip-egg-info
Csy79的博客
03-06 8546
成功解决ERROR: No .egg-info directory found in C:\Users\admin\AppData\Local\Temp\pip-pip-egg-info
pip install报python setup.py egg_info Check the logs for full command output.
修炼之路
07-20 8279
错误信息 pip install nvidia-pyindex Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple Processing ./nvidia-pyindex-1.0.3.tar.gz ERROR: Command errored out with exit status 1: command: /root/anaconda3/envs/cv_1/bin/python -c 'import io, os, s
No .egg-info directory found in xxx\pip-pip-egg-info-mq
猿小白的博客
07-18 8080
No .egg-info directory found in xxx\pip-pip-egg-info-mq
egg信号图像识别算法
12-26
### Egg Signal Image Recognition Algorithm In the context of egg signal image recognition, algorithms typically involve advanced machine learning techniques to accurately identify and classify eggs within images. While specific studies may not be directly mentioned in provided references, principles from similar domains can offer valuable insights. For instance, when dealing with large datasets containing numerous images, convolutional neural networks (CNNs) are often employed due to their effectiveness in handling complex visual patterns[^1]. CNN architectures excel at feature extraction and classification tasks by leveraging multiple layers designed specifically for spatial hierarchies present in natural scenes or objects like eggs. Moreover, integrating softmax functions into these models ensures proper probability distribution across different classes during prediction phases[^3]: ```python import tensorflow as tf from tensorflow.keras import Sequential from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Softmax model = Sequential([ Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(img_height, img_width, channels)), MaxPooling2D(pool_size=(2, 2)), Flatten(), Dense(units=number_of_classes), Softmax() ]) ``` The theoretical underpinnings behind such intelligent systems also draw upon concepts related to information theory and consciousness modeling efforts aimed at understanding how data processing leads to emergent properties observed in biological organisms[^2].
评论
添加红包

请填写红包祝福语或标题

个

红包个数最小为10个

元

红包金额最低5元

当前余额3.43元 前往充值 >
需支付：10.00元
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付元
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明：

1.余额是钱包充值的虚拟货币，按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载，可以购买VIP、付费专栏及课程。

余额充值