如果你只想要（半）連續的區域，Python中已經有一個簡單的實現：SciPy的ndimage.morphology模塊。這是相當常見的image morphology操作。
基本上，你有5個步驟：
def find_paws(data, smooth_radius=5, threshold=0.0001):
data = sp.ndimage.uniform_filter(data, smooth_radius)
thresh = data > threshold
filled = sp.ndimage.morphology.binary_fill_holes(thresh)
coded_paws, num_paws = sp.ndimage.label(filled)
data_slices = sp.ndimage.find_objects(coded_paws)
return object_slices
模糊輸入的數據位，以確保爪子有一個連續的足跡。所以，你有一個（這將是更有效地只使用一個更大的內核（該structure kwarg的各種scipy.ndimage.morphology功能），但是，這是不是很出於某種原因正常工作......）
閾值數組的地方布爾數組，其中壓力超過某一閾值（即thresh = data > value）
填寫任何內部孔，這樣就具有清潔器區域（filled = sp.ndimage.morphology.binary_fill_holes(thresh)）
查找單獨連續的區域（coded_paws, num_paws = sp.ndimage.label(filled)）。這將返回一個數組，其中的區域是由數字編碼的（每個區域都是一個唯一整數的鄰接區域（1到最多爪子的數量），其他地方都是零））。
使用data_slices = sp.ndimage.find_objects(coded_paws)分離連續區域。這會返回slice對象的元組列表，因此您可以使用[data[x] for x in data_slices]來獲取每個爪子的數據區域。相反，我們將根據這些切片繪製一個矩形，這需要稍微多些工作。
下面的兩個動畫顯示您的「重疊爪」和「分組爪」示例數據。這種方法似乎是完美的。 （而不管它的價值，這種運行更加平穩比下面的GIF圖像在我的機器上，因此爪子檢測算法是相當快的...）
這裏是一個完整的例子（現在有更詳細的解釋）。絕大多數是讀取輸入並製作動畫。實際的爪子檢測只有5行代碼。
import numpy as np
import scipy as sp
import scipy.ndimage
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
def animate(input_filename):
"""Detects paws and animates the position and raw data of each frame
in the input file"""
# With matplotlib, it's much, much faster to just update the properties
# of a display object than it is to create a new one, so we'll just update
# the data and position of the same objects throughout this animation...
infile = paw_file(input_filename)
# Since we're making an animation with matplotlib, we need
# ion() instead of show()...
plt.ion()
fig = plt.figure()
ax = fig.add_subplot(111)
fig.suptitle(input_filename)
# Make an image based on the first frame that we'll update later
# (The first frame is never actually displayed)
im = ax.imshow(infile.next()[1])
# Make 4 rectangles that we can later move to the position of each paw
rects = [Rectangle((0,0), 1,1, fc='none', ec='red') for i in range(4)]
[ax.add_patch(rect) for rect in rects]
title = ax.set_title('Time 0.0 ms')
# Process and display each frame
for time, frame in infile:
paw_slices = find_paws(frame)
# Hide any rectangles that might be visible
[rect.set_visible(False) for rect in rects]
# Set the position and size of a rectangle for each paw and display it
for slice, rect in zip(paw_slices, rects):
dy, dx = slice
rect.set_xy((dx.start, dy.start))
rect.set_width(dx.stop - dx.start + 1)
rect.set_height(dy.stop - dy.start + 1)
rect.set_visible(True)
# Update the image data and title of the plot
title.set_text('Time %0.2f ms' % time)
im.set_data(frame)
im.set_clim([frame.min(), frame.max()])
fig.canvas.draw()
def find_paws(data, smooth_radius=5, threshold=0.0001):
"""Detects and isolates contiguous regions in the input array"""
# Blur the input data a bit so the paws have a continous footprint
data = sp.ndimage.uniform_filter(data, smooth_radius)
# Threshold the blurred data (this needs to be a bit > 0 due to the blur)
thresh = data > threshold
# Fill any interior holes in the paws to get cleaner regions...
filled = sp.ndimage.morphology.binary_fill_holes(thresh)
# Label each contiguous paw
coded_paws, num_paws = sp.ndimage.label(filled)
# Isolate the extent of each paw
data_slices = sp.ndimage.find_objects(coded_paws)
return data_slices
def paw_file(filename):
"""Returns a iterator that yields the time and data in each frame
The infile is an ascii file of timesteps formatted similar to this:
Frame 0 (0.00 ms)
0.0 0.0 0.0
0.0 0.0 0.0
Frame 1 (0.53 ms)
0.0 0.0 0.0
0.0 0.0 0.0
...
"""
with open(filename) as infile:
while True:
try:
time, data = read_frame(infile)
yield time, data
except StopIteration:
break
def read_frame(infile):
"""Reads a frame from the infile."""
frame_header = infile.next().strip().split()
time = float(frame_header[-2][1:])
data = []
while True:
line = infile.next().strip().split()
if line == []:
break
data.append(line)
return time, np.array(data, dtype=np.float)
if __name__ == '__main__':
animate('Overlapping paws.bin')
animate('Grouped up paws.bin')
animate('Normal measurement.bin')
更新：至於鑑定爪子是在什麼時間接觸傳感器，最簡單的解決方法就是做同樣的分析，但使用所有數據的一次。 （即將輸入疊加到3D數組中，並使用它來代替單個時間幀）。由於SciPy的ndimage函數旨在與n維數組一起工作，因此我們不必修改原始爪查找函數在所有。
# This uses functions (and imports) in the previous code example!!
def paw_regions(infile):
# Read in and stack all data together into a 3D array
data, time = [], []
for t, frame in paw_file(infile):
time.append(t)
data.append(frame)
data = np.dstack(data)
time = np.asarray(time)
# Find and label the paw impacts
data_slices, coded_paws = find_paws(data, smooth_radius=4)
# Sort by time of initial paw impact... This way we can determine which
# paws are which relative to the first paw with a simple modulo 4.
# (Assuming a 4-legged dog, where all 4 paws contacted the sensor)
data_slices.sort(key=lambda dat_slice: dat_slice[2].start)
# Plot up a simple analysis
fig = plt.figure()
ax1 = fig.add_subplot(2,1,1)
annotate_paw_prints(time, data, data_slices, ax=ax1)
ax2 = fig.add_subplot(2,1,2)
plot_paw_impacts(time, data_slices, ax=ax2)
fig.suptitle(infile)
def plot_paw_impacts(time, data_slices, ax=None):
if ax is None:
ax = plt.gca()
# Group impacts by paw...
for i, dat_slice in enumerate(data_slices):
dx, dy, dt = dat_slice
paw = i%4 + 1
# Draw a bar over the time interval where each paw is in contact
ax.barh(bottom=paw, width=time[dt].ptp(), height=0.2,
left=time[dt].min(), align='center', color='red')
ax.set_yticks(range(1, 5))
ax.set_yticklabels(['Paw 1', 'Paw 2', 'Paw 3', 'Paw 4'])
ax.set_xlabel('Time (ms) Since Beginning of Experiment')
ax.yaxis.grid(True)
ax.set_title('Periods of Paw Contact')
def annotate_paw_prints(time, data, data_slices, ax=None):
if ax is None:
ax = plt.gca()
# Display all paw impacts (sum over time)
ax.imshow(data.sum(axis=2).T)
# Annotate each impact with which paw it is
# (Relative to the first paw to hit the sensor)
x, y = [], []
for i, region in enumerate(data_slices):
dx, dy, dz = region
# Get x,y center of slice...
x0 = 0.5 * (dx.start + dx.stop)
y0 = 0.5 * (dy.start + dy.stop)
x.append(x0); y.append(y0)
# Annotate the paw impacts
ax.annotate('Paw %i' % (i%4 +1), (x0, y0),
color='red', ha='center', va='bottom')
# Plot line connecting paw impacts
ax.plot(x,y, '-wo')
ax.axis('image')
ax.set_title('Order of Steps')
好像你將不得不把從行/列算法遠的你限制有用的信息。 – 2010-11-03 22:21:48
哇！貓控制軟件？ – alxx 2010-11-10 08:34:34
它實際上是狗的數據@alxx ;-)但是，它會被用來診斷它們！ – 2010-11-10 08:37:04