仍在我學習影像遮罩的旅程中。
我正在嘗試計算影像中紅點的數量。
這是輸入影像 
屏蔽紅色后，我得到這個影像 
問題是，有些 blob 未滿，因此它不會計算所有 blob，例如在此特定影像中，它不會計算數字 6 和 9。（假設左上角為 1）
如何改進屏蔽程序以獲得更準確的 blob？
屏蔽代碼：
import cv2, os
import numpy as np
os.chdir('C:\Program Files\Python\projects\Blob')
#Get image input
image_input = cv2.imread('realbutwithacrylic.png')
image_input = np.copy(image_input)
rgb = cv2.cvtColor(image_input, cv2.COLOR_BGR2RGB)
#Range of color wanted
lower_red = np.array([125, 1, 0])
upper_red = np.array([200, 110, 110])
#Masking the Image
first_mask = cv2.inRange(rgb, lower_red, upper_red)
#Output
cv2.imshow('first_mask', first_mask)
cv2.waitKey()
使用 Blob 計數器屏蔽代碼
import cv2, os
import numpy as np
#Some Visual Studio Code bullshit because it cant find the image????
os.chdir('C:\Program Files\Python\projects\Blob')
#Get image input
image_input = cv2.imread('realbutwithacrylic.png')
image_input = np.copy(image_input)
rgb = cv2.cvtColor(image_input, cv2.COLOR_BGR2RGB)
#Range of color wanted
lower_red = np.array([125, 1, 0])
upper_red = np.array([200, 110, 110])
#Masking the Image
first_mask = cv2.inRange(rgb, lower_red, upper_red)
#Initial masking counter
cv2.imshow('first_mask', first_mask)
cv2.waitKey()
#Blob Counter
thresh = cv2.threshold(first_mask,0,255,cv2.THRESH_OTSU cv2.THRESH_BINARY)[1]
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (7,7))
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=5)
cnts = cv2.findContours(opening, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
#Couting the blobs
blobs = 0
for c in cnts:
area = cv2.contourArea(c)
cv2.drawContours(first_mask, [c], -1, (36,255,12), -1)
if area > 13000:
blobs = 2
else:
blobs = 1
#Blob Number Output
print('blobs:', blobs)
#Masking Output
cv2.imshow('thresh', thresh)
cv2.imshow('opening', opening)
cv2.imshow('image', image_input)
cv2.imshow('mask', first_mask)
cv2.waitKey()
uj5u.com熱心網友回復：
由于您正在尋找足夠亮的紅色，您可能有更好的時間在 HSV 空間中屏蔽事物：
orig_image = cv2.imread("realbutwithacrylic.jpg")
image = orig_image.copy()
# Blur image to get rid of noise
image = cv2.GaussianBlur(image, (3, 3), cv2.BORDER_DEFAULT)
# Convert to hue-saturation-value
h, s, v = cv2.split(cv2.cvtColor(image, cv2.COLOR_BGR2HSV))
# "Roll" the hue value so reds (which would otherwise be at 0 and 255) are in the middle instead.
# This makes it easier to use `inRange` without needing to AND masks together.
image = cv2.merge(((h 128) % 255, s, v))
# Select the correct hues with saturated-enough, bright-enough colors.
image = cv2.inRange(image, np.array([40, 128, 100]), np.array([140, 255, 255]))
對于您的影像，輸出為
要計算點數，您現在可以簡單地：
lab = dip.Label(dots)
print(dip.MaximumAndMinimum(lab)[1])
...其中說 10。
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