用OpenCV裁剪黑色邊緣

Question

我認爲這應該是一個非常簡單的問題，但我無法找到解決方案或有效的關鍵字進行搜索。

我只有這張圖片。所以，我要削減他們，只留下Windows圖標（和藍色背景）

黑邊是無用的。

我不想計算Windows圖標的座標和大小。 GIMP和Photoshop有自動裁剪功能。 OpenCV沒有一個？

Answer 1

我不確定你的所有圖像是否都是這樣的。但是對於這個圖像，下面是一個簡單的python-opencv代碼來裁剪它。

第一導入庫：

import cv2 
import numpy as np 

讀取圖像，將其轉換爲灰度，並在二值圖像爲1

img = cv2.imread('sofwin.png') 
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) 
_,thresh = cv2.threshold(gray,1,255,cv2.THRESH_BINARY) 

閾值現在找到的輪廓在裏面。將只有一個對象，所以找到它的邊界矩形。

contours,hierarchy = cv2.findContours(thresh,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE) 
cnt = contours[0] 
x,y,w,h = cv2.boundingRect(cnt) 

現在裁剪圖像，並將其保存到另一個文件中。

crop = img[y:y+h,x:x+w] 
cv2.imwrite('sofwinres.png',crop) 

下面是結果：

Answer 2

import numpy as np 

def autocrop(image, threshold=0): 
    """Crops any edges below or equal to threshold 

    Crops blank image to 1x1. 

    Returns cropped image. 

    """ 
    if len(image.shape) == 3: 
     flatImage = np.max(image, 2) 
    else: 
     flatImage = image 
    assert len(flatImage.shape) == 2 

    rows = np.where(np.max(flatImage, 0) > threshold)[0] 
    if rows.size: 
     cols = np.where(np.max(flatImage, 1) > threshold)[0] 
     image = image[cols[0]: cols[-1] + 1, rows[0]: rows[-1] + 1] 
    else: 
     image = image[:1, :1] 

    return image 

Answer 3

怎麼樣一個漂亮的小遞歸函數？

import cv2 
import numpy as np 
def trim(frame): 
    #crop top 
    if not np.sum(frame[0]): 
     return trim(frame[1:]) 
    #crop bottom 
    elif not np.sum(frame[-1]): 
     return trim(frame[:-2]) 
    #crop left 
    elif not np.sum(frame[:,0]): 
     return trim(frame[:,1:]) 
    #crop right 
    elif not np.sum(frame[:,-1]): 
     return trim(frame[:,:-2])  
    return frame 

負載和閾值圖像，以確保暗區爲黑色：

img = cv2.imread("path_to_image.png") 
thold = (img>120)*img 

然後調用遞歸函數

trimmedImage = trim(thold) 

Answer 4

OK，所以對於完整性，我實現了各上面的建議，增加了遞歸算法的迭代版本（一旦糾正）並做了一組性能測試。

TLDR：遞歸可能是最好的平均情況下（但使用下面的 - OP有幾個錯誤），autocrop是最好的圖像，你期望幾乎是空的。

一般發現： 1.上面的遞歸算法有一對一的錯誤。更正後的版本如下。 2. cv2.findContours函數存在非矩形圖像的問題，實際上甚至在各種情況下修剪了一些圖像。我添加了一個使用cv2.CHAIN_APPROX_NONE的版本來查看它是否有幫助（它沒有幫助）。 3. autocrop實現對於稀疏圖像很有用，但對於密集圖像很差，這是遞歸/迭代算法的逆。

import numpy as np 
import cv2 

def trim_recursive(frame): 
    if frame.shape[0] == 0: 
    return np.zeros((0,0,3)) 

    # crop top 
    if not np.sum(frame[0]): 
    return trim_recursive(frame[1:]) 
    # crop bottom 
    elif not np.sum(frame[-1]): 
    return trim_recursive(frame[:-1]) 
    # crop left 
    elif not np.sum(frame[:, 0]): 
    return trim_recursive(frame[:, 1:]) 
    # crop right 
    elif not np.sum(frame[:, -1]): 
    return trim_recursive(frame[:, :-1]) 
    return frame 

def trim_contours(frame): 
    gray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY) 
    _,thresh = cv2.threshold(gray,1,255,cv2.THRESH_BINARY) 
    _, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) 
    if len(contours) == 0: 
    return np.zeros((0,0,3)) 
    cnt = contours[0] 
    x, y, w, h = cv2.boundingRect(cnt) 
    crop = frame[y:y + h, x:x + w] 
    return crop 

def trim_contours_exact(frame): 
    gray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY) 
    _,thresh = cv2.threshold(gray,1,255,cv2.THRESH_BINARY) 
    _, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) 
    if len(contours) == 0: 
    return np.zeros((0,0,3)) 
    cnt = contours[0] 
    x, y, w, h = cv2.boundingRect(cnt) 
    crop = frame[y:y + h, x:x + w] 
    return crop 

def trim_iterative(frame): 
    for start_y in range(1, frame.shape[0]): 
    if np.sum(frame[:start_y]) > 0: 
     start_y -= 1 
     break 
    if start_y == frame.shape[0]: 
    if len(frame.shape) == 2: 
     return np.zeros((0,0)) 
    else: 
     return np.zeros((0,0,0)) 
    for trim_bottom in range(1, frame.shape[0]): 
    if np.sum(frame[-trim_bottom:]) > 0: 
     break 

    for start_x in range(1, frame.shape[1]): 
    if np.sum(frame[:, :start_x]) > 0: 
     start_x -= 1 
     break 
    for trim_right in range(1, frame.shape[1]): 
    if np.sum(frame[:, -trim_right:]) > 0: 
     break 

    end_y = frame.shape[0] - trim_bottom + 1 
    end_x = frame.shape[1] - trim_right + 1 

    # print('iterative cropping x:{}, w:{}, y:{}, h:{}'.format(start_x, end_x - start_x, start_y, end_y - start_y)) 
    return frame[start_y:end_y, start_x:end_x] 

def autocrop(image, threshold=0): 
    """Crops any edges below or equal to threshold 

    Crops blank image to 1x1. 

    Returns cropped image. 

    """ 
    if len(image.shape) == 3: 
    flatImage = np.max(image, 2) 
    else: 
    flatImage = image 
    assert len(flatImage.shape) == 2 

    rows = np.where(np.max(flatImage, 0) > threshold)[0] 
    if rows.size: 
    cols = np.where(np.max(flatImage, 1) > threshold)[0] 
    image = image[cols[0]: cols[-1] + 1, rows[0]: rows[-1] + 1] 
    else: 
    image = image[:1, :1] 

    return image 

然後對其進行測試，我做了這個簡單的功能：

import datetime 
import numpy as np 
import random 

ITERATIONS = 10000 

def test_image(img): 
    orig_shape = img.shape 
    print ('original shape: {}'.format(orig_shape)) 
    start_time = datetime.datetime.now() 
    for i in range(ITERATIONS): 
    recursive_img = trim_recursive(img) 
    print ('recursive shape: {}, took {} seconds'.format(recursive_img.shape, (datetime.datetime.now()-start_time).total_seconds())) 
    start_time = datetime.datetime.now() 
    for i in range(ITERATIONS): 
    contour_img = trim_contours(img) 
    print ('contour shape: {}, took {} seconds'.format(contour_img.shape, (datetime.datetime.now()-start_time).total_seconds())) 
    start_time = datetime.datetime.now() 
    for i in range(ITERATIONS): 
    exact_contour_img = trim_contours(img) 
    print ('exact contour shape: {}, took {} seconds'.format(exact_contour_img.shape, (datetime.datetime.now()-start_time).total_seconds())) 
    start_time = datetime.datetime.now() 
    for i in range(ITERATIONS): 
    iterative_img = trim_iterative(img) 
    print ('iterative shape: {}, took {} seconds'.format(iterative_img.shape, (datetime.datetime.now()-start_time).total_seconds())) 
    start_time = datetime.datetime.now() 
    for i in range(ITERATIONS): 
    auto_img = autocrop(img) 
    print ('autocrop shape: {}, took {} seconds'.format(auto_img.shape, (datetime.datetime.now()-start_time).total_seconds())) 


def main(): 
    orig_shape = (10,10,3) 

    print('Empty image--should be 0x0x3') 
    zero_img = np.zeros(orig_shape, dtype='uint8') 
    test_image(zero_img) 

    print('Small image--should be 1x1x3') 
    small_img = np.zeros(orig_shape, dtype='uint8') 
    small_img[3,3] = 1 
    test_image(small_img) 

    print('Medium image--should be 3x7x3') 
    med_img = np.zeros(orig_shape, dtype='uint8') 
    med_img[5:8, 2:9] = 1 
    test_image(med_img) 

    print('Random image--should be full image: 100x100') 
    lg_img = np.zeros((100,100,3), dtype='uint8') 
    for y in range (100): 
    for x in range(100): 
     lg_img[y,x, 0] = random.randint(0,255) 
     lg_img[y, x, 1] = random.randint(0, 255) 
     lg_img[y, x, 2] = random.randint(0, 255) 
    test_image(lg_img) 

main() 

...，結果......

Empty image--should be 0x0x3 
original shape: (10, 10, 3) 
recursive shape: (0, 0, 3), took 0.295851 seconds 
contour shape: (0, 0, 3), took 0.048656 seconds 
exact contour shape: (0, 0, 3), took 0.046273 seconds 
iterative shape: (0, 0, 3), took 1.742498 seconds 
autocrop shape: (1, 1, 3), took 0.093347 seconds 
Small image--should be 1x1x3 
original shape: (10, 10, 3) 
recursive shape: (1, 1, 3), took 1.342977 seconds 
contour shape: (0, 0, 3), took 0.048919 seconds 
exact contour shape: (0, 0, 3), took 0.04683 seconds 
iterative shape: (1, 1, 3), took 1.084258 seconds 
autocrop shape: (1, 1, 3), took 0.140886 seconds 
Medium image--should be 3x7x3 
original shape: (10, 10, 3) 
recursive shape: (3, 7, 3), took 0.610821 seconds 
contour shape: (0, 0, 3), took 0.047263 seconds 
exact contour shape: (0, 0, 3), took 0.046342 seconds 
iterative shape: (3, 7, 3), took 0.696778 seconds 
autocrop shape: (3, 7, 3), took 0.14493 seconds 
Random image--should be full image: 100x100 
original shape: (100, 100, 3) 
recursive shape: (100, 100, 3), took 0.131619 seconds 
contour shape: (98, 98, 3), took 0.285515 seconds 
exact contour shape: (98, 98, 3), took 0.288365 seconds 
iterative shape: (100, 100, 3), took 0.251708 seconds 
autocrop shape: (100, 100, 3), took 1.280476 seconds 

Answer 5

在情況下，它可以幫助任何人，我去這個調整@ wordsforthewise的replacement爲基於PIL的解決方案：

bw = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) 
rows, cols = bw.shape 

non_empty_columns = np.where(bw.max(axis=0) > 0)[0] 
non_empty_rows = np.where(bw.max(axis=1) > 0)[0] 
cropBox = (min(non_empty_rows) * (1 - padding), 
      min(max(non_empty_rows) * (1 + padding), rows), 
      min(non_empty_columns) * (1 - padding), 
      min(max(non_empty_columns) * (1 + padding), cols)) 

return img[cropBox[0]:cropBox[1]+1, cropBox[2]:cropBox[3]+1 , :] 

（這是一個調整，原代碼預計將裁剪掉白色背景而不是黑色背景。）