在影像中查找框并保存為影像cv2

我是計算機視覺的新手，我想創建一個程式來幫助我檢測影像中的框并保存為影像。

等等...我嘗試了一些代碼，但沒有得到我想要的結果。這是我的代碼及其輸出。

import cv2
# Load iamge, grayscale, adaptive threshold
image = cv2.imread('image.jpeg')
result = image.copy()
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
thresh = cv2.adaptiveThreshold(gray,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV,51,9)

# Fill rectangular contours
cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    cv2.drawContours(thresh, [c], -1, (255,255,255), -1)

# Morph open
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (9,9))
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=4)

# Draw rectangles
cnts = cv2.findContours(opening, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    x,y,w,h = cv2.boundingRect(c)
    cv2.rectangle(image, (x, y), (x   w, y   h), (36,255,12), 3)

cv2.imshow('thresh', thresh)
cv2.imshow('opening', opening)
cv2.imshow('image', image)
cv2.waitKey()

輸出：

uj5u.com熱心網友回復：

這是您的問題解決方案：

嘗試這個：

import cv2
import numpy as np

#Read input image
img = cv2.imread('hw_data.png')

#convert from BGR to HSV color space
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

#apply threshold
thresh = cv2.threshold(gray, 30, 255, cv2.THRESH_BINARY)[1]

# find contours and get one with area about 180*35
# draw all contours in green and accepted ones in red
contours = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contours = contours[0] if len(contours) == 2 else contours[1]
#area_thresh = 0
min_area = 0.95*180*44
max_area = 1.05*180*44
print(min_area)
print(max_area)
result = img.copy()
i = 1
for c in contours:
#     print(c)
    area = cv2.contourArea(c)
    cv2.drawContours(result, [c], -1, (0, 255, 0), 1)
    
    x,y,w,h = cv2.boundingRect(c)
    # crop region of img using bounding box
    region = result[y:y h, x:x w]
    # save region to new image
    print(region.shape,' i ',i)
#     cv2.imwrite("black_region_{0}.png".format(i), region)
    i = i   1
    if region.shape[0]>70 and region.shape[1]<100:
        cv2.imwrite("black_region_{0}.png".format(i), region)
#         break
#     if area > min_area and area < max_area:
#             cv2.drawContours(result, [c], -1, (0, 0, 255), 1)
#             break

# save result
# cv2.imwrite("box_found.png", result)

# show images
# cv2.imshow("GRAY", gray)
# cv2.imshow("THRESH", thresh)
# cv2.imshow("RESULT", result)
# cv2.waitKey(0)

uj5u.com熱心網友回復：

您需要做的只是首先洗掉最外面的白色區域，也就是說，將其設為黑色，以便我們可以使用cv2.RETR_EXTERNAL標志檢測沒有任何問題的框，因為它們沒有接觸。然后我們將一個一個地提取這些框。

為了去除最外面的區域，我使用了輪廓的點多邊形測驗。如果點 (1, 1) 位于輪廓內部或輪廓上，則不會繪制它，并且每隔一個輪廓將繪制在新影像上。從這張新影像中，我讀取了盒子輪廓并提取了它們。

import cv2
import numpy as np

img = cv2.imread("2lscp.png", cv2.IMREAD_GRAYSCALE)
ret, img = cv2.threshold(img, 50, 255, cv2.THRESH_BINARY cv2.THRESH_OTSU)

Contours = cv2.findContours(img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)[-2]

newImg = np.zeros(img.shape, dtype=np.uint8)
for Contour in Contours:
    if cv2.pointPolygonTest(Contour, (1, 1), False) == -1:
        cv2.drawContours(newImg, [Contour], -1, 255, 1)

Contours = cv2.findContours(newImg, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[-2]
for Contour in Contours:
    [x, y, w, h] = cv2.boundingRect(Contour)

    cv2.imshow("box extracted", img[y:y h, x:x w])
    cv2.waitKey(0)

cv2.destroyAllWindows()

uj5u.com熱心網友回復：

這種情況看起來特別簡單，因為影像是準二進制的。檢測白色區域的輪廓，并選擇面積占整個影像 10% 到 15% 的區域。這些是所需的盒子。然后適合矩形或旋轉矩形。

無需額外處理。

標籤：Python opencv 图像处理 计算机视觉 物体检测

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