Question

我尝试在这张图片中的每个对象上绘制一个边界框，我从documentation

编写了这段代码

import cv2 as cv2
import os
import numpy as np


img = cv2.imread('1 (2).png')
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY);
ret,thresh = cv2.threshold(img,127,255,0)
im2,contours,hierarchy = cv2.findContours(thresh, 1, 2)
for item in range(len(contours)):
    cnt = contours[item]
    if len(cnt)>20:
        print(len(cnt))
        M = cv2.moments(cnt)
        cx = int(M['m10']/M['m00'])
        cy = int(M['m01']/M['m00'])
        x,y,w,h = cv2.boundingRect(cnt)
        cv2.rectangle(img,(x,y),(x+w,y+h),(0,255,0),2)
        cv2.imshow('image',img)
        cv2.waitKey(0)
        cv2.destroyAllWindows()

结果只有一个对象，

当我将此行中的值127更改为此行ret,thresh = cv2.threshold(img,127,255,0)中的200时，我得到了不同的对象。

这里是原始图像

问题是如何一次检测所有物体？

Answer 1

这种方法相当简单。我们首先转换为HSV并仅抓取色调通道。

image_hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
h,_,_ = cv2.split(image_hsv)

接下来，我们找到主导色调 - 首先使用numpy.bincount（我们flatten色调通道图像计算每个色调的出现次数）：

bins = np.bincount(h.flatten())

然后使用numpy.where找出哪些是常见的：

MIN_PIXEL_CNT_PCT = (1.0/20.0)
peaks = np.where(bins > (h.size * MIN_PIXEL_CNT_PCT))[0]

现在我们已经确定了所有主要色调，我们可以重复处理图像，找到与每个图像对应的区域：

for i, peak in enumerate(peaks):

我们首先创建一个遮罩，选择此色调的所有像素（cv2.inRange，然后从输入BGR图像中提取相应的部分（cv2.bitwise_and。

mask = cv2.inRange(h, peak, peak)
blob = cv2.bitwise_and(image, image, mask=mask)

接下来，我们找到此色调的所有连续区域的轮廓（cv2.findContours，以便我们可以单独处理它们

_, contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

现在，对于每个已识别的连续区域

for j, contour in enumerate(contours):

我们确定边界框（{{3}}，并通过用白色填充轮廓多边形（cv2.boundingRect和numpy.zeros_like）来创建与此轮廓相对应的蒙版

bbox = cv2.boundingRect(contour)
contour_mask = np.zeros_like(mask)
cv2.drawContours(contour_mask, contours, j, 255, -1)

然后我们可以额外加上与边界框相对应的ROI

region = blob.copy()[bbox[1]:bbox[1]+bbox[3],bbox[0]:bbox[0]+bbox[2]]
region_mask = contour_mask[bbox[1]:bbox[1]+bbox[3],bbox[0]:bbox[0]+bbox[2]]
region_masked = cv2.bitwise_and(region, region, mask=region_mask)

或可视化（cv2.drawContours边界框：

result = cv2.bitwise_and(blob, blob, mask=contour_mask)
top_left, bottom_right = (bbox[0], bbox[1]), (bbox[0]+bbox[2], bbox[1]+bbox[3])
cv2.rectangle(result, top_left, bottom_right, (255, 255, 255), 2)

或者进行任何其他处理。

完整脚本

import cv2
import numpy as np

# Minimum percentage of pixels of same hue to consider dominant colour
MIN_PIXEL_CNT_PCT = (1.0/20.0)

image = cv2.imread('colourblobs.png')
if image is None:
    print("Failed to load iamge.")
    exit(-1)

image_hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
# We're only interested in the hue
h,_,_ = cv2.split(image_hsv)
# Let's count the number of occurrences of each hue
bins = np.bincount(h.flatten())
# And then find the dominant hues
peaks = np.where(bins > (h.size * MIN_PIXEL_CNT_PCT))[0]

# Now let's find the shape matching each dominant hue
for i, peak in enumerate(peaks):
    # First we create a mask selecting all the pixels of this hue
    mask = cv2.inRange(h, peak, peak)
    # And use it to extract the corresponding part of the original colour image
    blob = cv2.bitwise_and(image, image, mask=mask)

    _, contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

    for j, contour in enumerate(contours):
        bbox = cv2.boundingRect(contour)
        # Create a mask for this contour
        contour_mask = np.zeros_like(mask)
        cv2.drawContours(contour_mask, contours, j, 255, -1)

        print "Found hue %d in region %s." % (peak, bbox)
        # Extract and save the area of the contour
        region = blob.copy()[bbox[1]:bbox[1]+bbox[3],bbox[0]:bbox[0]+bbox[2]]
        region_mask = contour_mask[bbox[1]:bbox[1]+bbox[3],bbox[0]:bbox[0]+bbox[2]]
        region_masked = cv2.bitwise_and(region, region, mask=region_mask)
        file_name_section = "colourblobs-%d-hue_%03d-region_%d-section.png" % (i, peak, j)
        cv2.imwrite(file_name_section, region_masked)
        print " * wrote '%s'" % file_name_section

        # Extract the pixels belonging to this contour
        result = cv2.bitwise_and(blob, blob, mask=contour_mask)
        # And draw a bounding box
        top_left, bottom_right = (bbox[0], bbox[1]), (bbox[0]+bbox[2], bbox[1]+bbox[3])
        cv2.rectangle(result, top_left, bottom_right, (255, 255, 255), 2)
        file_name_bbox = "colourblobs-%d-hue_%03d-region_%d-bbox.png" % (i, peak, j)
        cv2.imwrite(file_name_bbox, result)
        print " * wrote '%s'" % file_name_bbox

控制台输出

Found hue 32 in region (186, 184, 189, 122).
 * wrote 'colourblobs-0-hue_032-region_0-section.png'
 * wrote 'colourblobs-0-hue_032-region_0-bbox.png'
Found hue 71 in region (300, 197, 1, 1).
 * wrote 'colourblobs-1-hue_071-region_0-section.png'
 * wrote 'colourblobs-1-hue_071-region_0-bbox.png'
Found hue 71 in region (301, 195, 1, 1).
 * wrote 'colourblobs-1-hue_071-region_1-section.png'
 * wrote 'colourblobs-1-hue_071-region_1-bbox.png'
Found hue 71 in region (319, 190, 1, 1).
 * wrote 'colourblobs-1-hue_071-region_2-section.png'
 * wrote 'colourblobs-1-hue_071-region_2-bbox.png'
Found hue 71 in region (323, 176, 52, 14).
 * wrote 'colourblobs-1-hue_071-region_3-section.png'
 * wrote 'colourblobs-1-hue_071-region_3-bbox.png'
Found hue 71 in region (45, 10, 330, 381).
 * wrote 'colourblobs-1-hue_071-region_4-section.png'
 * wrote 'colourblobs-1-hue_071-region_4-bbox.png'
Found hue 109 in region (0, 0, 375, 500).
 * wrote 'colourblobs-2-hue_109-region_0-section.png'
 * wrote 'colourblobs-2-hue_109-region_0-bbox.png'
Found hue 166 in region (1, 397, 252, 103).
 * wrote 'colourblobs-3-hue_166-region_0-section.png'
 * wrote 'colourblobs-3-hue_166-region_0-bbox.png'