Pi直播視頻色彩檢測

我打算在我的電視背後創建一個流光溢彩效果。我想通過使用指向我電視的攝像頭來實現此目的。我認爲最簡單的方法是使用簡單的IP攝像頭。我需要顏色檢測來檢測屏幕上的顏色，並將其轉換爲led燈帶上的rgb值。Pi直播視頻色彩檢測

我有一個樹莓派作爲中心在我家的中心。我正在考慮像這樣使用它

IP攝像機指向我的屏幕處理pi上的視頻並將其轉換爲rgb值並將其發送到mqtt服務器。我的電視後面接收節點MCU上的顏色。

如何檢測我的pi上的實時流（多點）上的顏色？

來源

2017-02-02 Robert

如果您可以創建任何背景顏色，最好的方法可能是計算k-means或median以獲得「最受歡迎」的顏色。如果環境光線可能在不同的地方有所不同，那麼在圖像邊緣使用ROI可以檢查此區域中哪種顏色佔優勢（通過比較不同顏色的樣本數量）。

如果您只有有限的顏色（例如只有R，G和B），那麼您可以簡單地檢查哪個通道在所需區域中具有最高強度。

我編寫了一個假設你可以創建任何RGB環境顏色的代碼。

隨着測試圖像我用這一個：

的代碼是：

import cv2 
import numpy as np 

# Read an input image (in your case this will be an image from the camera) 
img = cv2.imread('saul2.png ', cv2.IMREAD_COLOR) 

# The block_size defines how big the patches around an image are 
# the more LEDs you have and the more segments you want, the lower block_size can be 
block_size = 60 

# Get dimensions of an image 
height, width, chan = img.shape 

# Calculate number of patches along height and width 
h_steps = height/block_size 
w_steps = width/block_size 

# In one loop I calculate both: left and right ambient or top and bottom 
ambient_patch1 = np.zeros((60, 60, 3)) 
ambient_patch2 = np.zeros((60, 60, 3)) 

# Create output image (just for visualization 
# there will be an input image in the middle, 10px black border and ambient color) 
output = cv2.copyMakeBorder(img, 70, 70, 70, 70, cv2.BORDER_CONSTANT, value = 0) 

for i in range(h_steps): 
    # Get left and right region of an image 
    left_roi = img[i * 60 : (i + 1) * 60, 0 : 60] 
    right_roi = img[i * 60 : (i + 1) * 60, -61 : -1] 

    left_med = np.median(left_roi, (0, 1)) # This is an actual RGB color for given block (on the left) 
    right_med = np.median(right_roi, (0, 1)) # and on the right 

    # Create patch having an ambient color - this is just for visualization 
    ambient_patch1[:, :] = left_med 
    ambient_patch2[:, :] = right_med 

    # Put it in the output image (the additional 70 is because input image is in the middle (shifted by 70px) 
    output[70 + i * 60 : 70+ (i + 1) * 60, 0 : 60] = ambient_patch1 
    output[70 + i * 60 : 70+ (i + 1) * 60, -61: -1] = ambient_patch2 


for i in range(w_steps): 
    # Get top and bottom region of an image 
    top_roi = img[0 : 60, i * 60 : (i + 1) * 60] 
    bottom_roi = img[-61 : -1, i * 60: (i + 1) * 60] 

    top_med = np.median(top_roi, (0, 1)) # This is an actual RGB color for given block (on top) 
    bottom_med = np.median(bottom_roi, (0, 1)) # and bottom 

    # Create patch having an ambient color - this is just for visualization 
    ambient_patch1[:, :] = top_med 
    ambient_patch2[:, :] = bottom_med 

    # Put it in the output image (the additional 70 is because input image is in the middle (shifted by 70px) 
    output[0 : 60, 70 + i * 60 : 70 + (i + 1) * 60] = ambient_patch1 
    output[-61: -1, 70 + i * 60 : 70 + (i + 1) * 60] = ambient_patch2 

# Save output image 
cv2.imwrite('saul_output.png', output)

這一點讓結果如下：