Computer Vision — Part 2

這篇文章，實現 Computer Vision — Part 2。(本文章內容經筆者實現驗證過)

• Canny 邊緣檢測

import cv2
import numpy as np
img = cv2.imread("statue_small.jpg", 0)
cv2.imwrite("canny.jpg", cv2.Canny(img, 200, 300))
cv2.imshow("canny", cv2.imread("canny.jpg"))

• 邊界框、最小矩形區域、最小閉圓

import cv2
import numpy as np
img = cv2.pyrDown(cv2.imread("hammer.jpg", cv2.IMREAD_UNCHANGED))
ret, thresh = cv2.threshold(cv2.cvtColor(img.copy(), cv2.COLOR_BGR2GRAY) , 127, 255, cv2.THRESH_BINARY)
image, contours, hier = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
for c in contours:
  
  x,y,w,h = cv2.boundingRect(c)
  cv2.rectangle(img, (x,y), (x+w, y+h), (0, 255, 0), 2)
rect = cv2.minAreaRect(c)
  box = cv2.boxPoints(rect)
  box = np.int0(box)
  cv2.drawContours(img, [box], 0, (0,0, 255), 3)
  
  (x,y),radius = cv2.minEnclosingCircle(c)
  center = (int(x),int(y))
  radius = int(radius)
  img = cv2.circle(img,center,radius,(0,255,0),2)
cv2.drawContours(img, contours, -1, (255, 0, 0), 1)
cv2.imshow("contours", img)

• 凸輪廓、Douglas-Peucker

import cv2
import numpy as np
img = cv2.pyrDown(cv2.imread("hammer.jpg", cv2.IMREAD_UNCHANGED))
ret, thresh = cv2.threshold(cv2.cvtColor(img.copy(), cv2.COLOR_BGR2GRAY) , 127, 255, cv2.THRESH_BINARY)
black = cv2.cvtColor(np.zeros((img.shape[1], img.shape[0]), dtype=np.uint8), cv2.COLOR_GRAY2BGR)
image, contours, hier = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
for cnt in contours:
  epsilon = 0.01 * cv2.arcLength(cnt,True)
  approx = cv2.approxPolyDP(cnt,epsilon,True)
  hull = cv2.convexHull(cnt)
  cv2.drawContours(black, [cnt], -1, (0, 255, 0), 2)
  cv2.drawContours(black, [approx], -1, (255, 255, 0), 2)
  cv2.drawContours(black, [hull], -1, (0, 0, 255), 2)
cv2.imshow("hull", black)

• 檢測 corner 特徵

import cv2
import numpy as np
img = cv2.imread('chess_board')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
gray = np.float32(gray)
dst = cv2.cornerHarris(gray, 9, 23, 0.04)
img[dst>0.01 * dst.max()] = [0, 0, 255] 

cv2.imshow('corners', img)

…… 進行中

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