如何在Tkinter中基于树莓派热成像相机的整数数组显示彩色图像

Got it, let's fix this step by step. The core issue here is mapping your raw thermal integer values to a colorful thermal gradient (like purple → red → yellow) and converting that into a format Tkinter can display. Here's a complete, working approach tailored to your 32×24 resolution data:

Key Steps Breakdown #

1. Reshape your 1D list: Convert your 768-element integer list into a 24×32 NumPy array (since 24×32=768).
2. Normalize data: Thermal values are likely outside the 0-255 range that colormaps use—we’ll scale them to fit this range for proper color mapping.
3. Apply a thermal colormap: Use a pre-built colormap (like inferno, which has the classic thermal color gradient) to turn grayscale values into RGB colors.
4. Convert to Tkinter PhotoImage: Pack the RGB data into a PPM format (natively supported by Tkinter) and display it.

Full Working Code #

import tkinter as tk
import numpy as np
from matplotlib import cm
from matplotlib.colors import Normalize

def thermal_to_photo(thermal_array, cmap_name='inferno'):
    # Normalize thermal data to 0-255 range (adjust vmin/vmax if you know your temp bounds)
    norm = Normalize(vmin=np.min(thermal_array), vmax=np.max(thermal_array))
    # Apply the colormap to get RGB values
    colored_data = cm.get_cmap(cmap_name)(norm(thermal_array))
    # Convert to 8-bit RGB (discard alpha channel if present)
    rgb_array = (colored_data[:, :, :3] * 255).astype(np.uint8)
    # Get image dimensions
    height, width = rgb_array.shape[:2]
    # Create PPM header (P6 is binary color PPM format)
    ppm_header = f'P6 {width} {height} 255 '.encode()
    # Combine header with raw RGB bytes
    ppm_data = ppm_header + rgb_array.tobytes()
    # Return Tkinter PhotoImage
    return tk.PhotoImage(width=width, height=height, data=ppm_data, format='PPM')

# Initialize Tkinter window
root = tk.Tk()
root.title("Raspberry Pi Thermal Camera")

# Replace this with your actual 768-element integer list
# Example: Simulate thermal values (e.g., 20-40°C scaled by 100)
thermal_1d_list = np.random.randint(2000, 4000, size=768).tolist()

# Convert 1D list to 24x32 array
thermal_2d_array = np.array(thermal_1d_list).reshape(24, 32)

# Generate the colored photo image
thermal_image = thermal_to_photo(thermal_2d_array)

# Display using a Label (simpler than Canvas for static images)
image_label = tk.Label(root, image=thermal_image)
image_label.pack(padx=10, pady=10)

# Critical: Keep a reference to the image to prevent garbage collection
image_label.image = thermal_image

root.mainloop()

Customization Tips #

• Colormap Choice: Swap 'inferno' with other thermal-friendly options like 'plasma' (purple → pink → yellow) or 'magma' (black → purple → red → yellow) to get different color gradients.
• Fixed Temperature Range: If you know the min/max possible temperatures (e.g., 10°C to 50°C), replace vmin=np.min(thermal_array) and vmax=np.max(thermal_array) with your actual scaled values (like vmin=1000, vmax=5000 if you’re scaling by 100). This ensures consistent color mapping across frames, even if the temperature range shifts.
• Image Scaling: To get a larger view, resize the RGB array using cv2.resize (install OpenCV first) or use Tkinter’s PhotoImage.zoom() method—note that resizing will make the low-res 32×24 image pixelated.

Why Your Original Code Didn’t Work #

The StackOverflow color code relied on OpenCV’s BGR-to-RGB conversion, but it required a 3-channel RGB input (not your single-channel thermal array). Our approach directly maps your raw thermal data to RGB using a colormap, which is exactly what you need for thermal imaging.

内容的提问来源于stack exchange，提问作者Will Jordan