image = cv2.imread(args['image'])
accumEdged = np.zeros(image.shape[:2], dtype='uint8')
for chan in cv2.split(image):
?? ?chan = cv2.medianBlur(chan, 11)
? ? edged = cv2.Canny(chan, 50, 200)
? ? accumEdged = cv2.bitwise_or(accumEdged, edged)
cv2.imshow('edge map', accumEdged)

運行結果如下:

左側為原圖,右側為邊緣檢測圖.

2.2 獲取輪廓

opencv-python中查找圖像輪廓的API為：findContours 函數(shù),該函數(shù)接收二值圖像作為輸入，可輸出物體外輪廓、內外輪廓等等.

代碼如下:

cnts = cv2.findContours(accumEdged.copy(), cv2.RETR_EXTERNAL, ?cv2.CHAIN_APPROX_SIMPLE)
cnts = grab_contours(cnts)
cnts = sorted(cnts, key=cv2.contourArea, reverse=True)[:5]
orig = image.copy()
# unsorted
for (i, c) in enumerate(cnts):
? ? orig = draw_contour(orig, c, i)
cv2.imshow('Unsorted', orig)
cv2.imwrite("./Unsorted.jpg", orig)

運行結果如下:

需要注意的是,在OpenCV2.X版本,函數(shù)findContours返回兩個值,

函數(shù)聲明如下:

contours, hierarchy = cv2.findContours(binary,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)

但是在OpenCV3以上版本,該函數(shù)的聲明形式如下:

image, contours, hierarchy = cv2.findContours(binary, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

所以為了適配兩種模式,我們實現(xiàn)函數(shù)grab_contours 來根據(jù)不同的版本,選擇對應的返回輪廓的下標位置,

代碼如下:

def grab_contours(cnts):
? ? # if the length the contours tuple returned by cv2.findContours
? ? # is '2' then we are using either OpenCV v2.4, v4-beta, or
? ? # v4-official
? ? if len(cnts) == 2:
? ? ? ? cnts = cnts[0]

? ? # if the length of the contours tuple is '3' then we are using
? ? # either OpenCV v3, v4-pre, or v4-alpha
? ? elif len(cnts) == 3:
? ? ? ? cnts = cnts[1]

? ? return cnts

2.3 輪廓排序

通過上述步驟,我們得到了圖像中的所有物體的輪廓,接下來我們定義函數(shù)sort_contours函數(shù)來實現(xiàn)對輪廓進行排序操作,該函數(shù)接受method參數(shù)來實現(xiàn)按照不同的次序對輪廓進行排序,比如從左往右,或者從右往左.

代碼如下:

def sort_contours(cnts, method='left-to-right'):
? ? # initialize the reverse flag and sort index
? ? reverse = False
? ? i = 0
? ? # handle if sort in reverse
? ? if method == 'right-to-left' or method == 'bottom-to-top':
? ? ? ? reverse = True
? ? # handle if sort against y rather than x of the bounding box
? ? if method == 'bottom-to-top' or method == 'top-to-bottom':
? ? ? ? i = 1

? ? boundingBoxes = [cv2.boundingRect(c) for c in cnts]
? ? (cnts, boundingBoxes) = zip(*sorted(zip(cnts, boundingBoxes), key=lambda b: b[1][i], reverse=reverse))
? ? return (cnts, boundingBoxes)

上述代碼的核心思想為先求出每個輪廓的外接矩形框,然后通過對外接框按照x或y坐標排序進而來實現(xiàn)對輪廓的排序.

調用代碼如下:

# sorted
(cnts, boundingboxes) = sort_contours(cnts, method=args['method'])
for (i, c) in enumerate(cnts):
? ? image = draw_contour(image, c, i)
cv2.imshow('Sorted', image)
cv2.waitKey(0)

運行結果如下: