如何使用局部最大濾波來識別代表狗爪的二維數組中的壓力峰值？

二維數組中的峰值偵測

挑戰：

挑戰：

檢測

偵測的峰值代表狗爪下的壓力測量值，以描繪解剖學分區。

<code class="python">import numpy as np
import matplotlib.pyplot as plt
from scipy.ndimage.filters import maximum_filter
from scipy.ndimage.morphology import generate_binary_structure, binary_erosion

# Define the paw data
paw_data = np.loadtxt("paws.txt").reshape(4, 11, 14)

# Define the 8-connected neighborhood
neighborhood = generate_binary_structure(2, 2)

# Function to detect peaks
def detect_peaks(image):
    # Local maximum filter
    local_max = maximum_filter(image, footprint=neighborhood) == image
    
    # Create a mask of the background
    background = (image == 0)
    
    # Erode the background to remove artifacts
    eroded_background = binary_erosion(background, structure=neighborhood, border_value=1)
    
    # Final mask containing only peaks
    detected_peaks = local_max ^ eroded_background
    
    return detected_peaks

# Detect peaks for each paw
paws = [p.squeeze() for p in np.vsplit(paw_data, 4)]
detected_peaks_list = []
for paw in paws:
    detected_peaks = detect_peaks(paw)
    detected_peaks_list.append(detected_peaks)

# Plot the results
fig, axs = plt.subplots(4, 2, figsize=(10, 10))
for i, paw in enumerate(paws):
    axs[i, 0].imshow(paw)
    axs[i, 0].set_title("Paw Image")
    axs[i, 1].imshow(detected_peaks_list[i])
    axs[i, 1].set_title("Peak Detection")

plt.tight_layout()
plt.show()</code>

解決方案：

實際的解決方案包括使用局部最大濾波器來識別峰值。方法如下：

• 注意事項：
• 此方法假設背景乾淨，可能不適合噪音資料。

鄰近大小可能需要根據峰值大小進行調整。 進一步分析可能涉及使用 scipy.ndimage.measurements.label 來標記不同的物件（峰值）。

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