[英]OpenCV Python remove object/pattern from images
I have been facing this problem from some days: i need to remove this image/pattern from images like this or this using OpenCV. I know that the problem is a Template Matching problem and I have to use filters (like canny) and and "slide" the template over the image, once this has been transformed by the filters.几天来我一直面临这个问题:我需要使用 OpenCV 从这样的图像中删除这个图像/图案。我知道问题是模板匹配问题,我必须使用过滤器(如 canny)和“在图像上滑动“模板,一旦它被过滤器转换。
I tried some solutions like this or this , but i had poor results, for example applying the second method I obtain this images 1 2我尝试了一些类似这样的解决方案,但结果很差,例如应用第二种方法我获得了这个图像1 2
this is my code这是我的代码
import cv2
import numpy as np
# Resizes a image and maintains aspect ratio
def maintain_aspect_ratio_resize(image, width=None, height=None, inter=cv2.INTER_AREA):
# Grab the image size and initialize dimensions
dim = None
(h, w) = image.shape[:2]
# Return original image if no need to resize
if width is None and height is None:
return image
# We are resizing height if width is none
if width is None:
# Calculate the ratio of the height and construct the dimensions
r = height / float(h)
dim = (int(w * r), height)
# We are resizing width if height is none
else:
# Calculate the ratio of the 0idth and construct the dimensions
r = width / float(w)
dim = (width, int(h * r))
# Return the resized image
return cv2.resize(image, dim, interpolation=inter)
# Load template, convert to grayscale, perform canny edge detection
template = cv2.imread('C:\\Users\Quirino\Desktop\Reti\Bounding_box\Checkboard.jpg')
template = cv2.resize(template, (640,480))
template = cv2.cvtColor(template, cv2.COLOR_BGR2GRAY)
template = cv2.Canny(template, 50, 200)
(tH, tW) = template.shape[:2]
# cv2.imshow("template", template)
# Load original image, convert to grayscale
original_image = cv2.imread('F:\\ARCHAIDE\Appearance\Data_Archaide_Complete\MTL_G6\MTL_G6_MMO090.jpg')
# original_image = cv2.resize(original_image, (640,480))
final = original_image.copy()
gray = cv2.cvtColor(original_image, cv2.COLOR_BGR2GRAY)
found = None
# Dynamically rescale image for better template matching
for scale in np.linspace(0.2, 1.0, 20)[::-1]:
# Resize image to scale and keep track of ratio
resized = maintain_aspect_ratio_resize(gray, width=int(gray.shape[1] * scale))
r = gray.shape[1] / float(resized.shape[1])
# Stop if template image size is larger than resized image
if resized.shape[0] < tH or resized.shape[1] < tW:
break
# Detect edges in resized image and apply template matching
canny = cv2.Canny(resized, 50, 200)
detected = cv2.matchTemplate(canny, template, cv2.TM_CCOEFF)
(_, max_val, _, max_loc) = cv2.minMaxLoc(detected)
# Uncomment this section for visualization
'''
clone = np.dstack([canny, canny, canny])
cv2.rectangle(clone, (max_loc[0], max_loc[1]), (max_loc[0] + tW, max_loc[1] + tH), (0,255,0), 2)
cv2.imshow('visualize', clone)
cv2.waitKey(0)
'''
# Keep track of correlation value
# Higher correlation means better match
if found is None or max_val > found[0]:
found = (max_val, max_loc, r)
# Compute coordinates of bounding box
(_, max_loc, r) = found
(start_x, start_y) = (int(max_loc[0] * r), int(max_loc[1] * r))
(end_x, end_y) = (int((max_loc[0] + tW) * r), int((max_loc[1] + tH) * r))
original_image = cv2.resize(original_image, (640,480))
# Draw bounding box on ROI to remove
cv2.rectangle(original_image, (start_x, start_y), (end_x, end_y), (0,255,0), 2)
cv2.imshow('detected', original_image)
# Erase unwanted ROI (Fill ROI with white)
cv2.rectangle(final, (start_x, start_y), (end_x, end_y), (255,255,255), -1)
final = cv2.resize(final, (640,480))
cv2.imshow('final', final)
cv2.waitKey(0)
what could i try?我可以尝试什么?
Here is one way to approach that in Python/OpenCV这是在 Python/OpenCV 中处理该问题的一种方法
Input:输入:
import cv2
import numpy as np
# read the input
img = cv2.imread('checks_object.jpg')
# threshold on outer white area of checkerboard pattern
lower = (210,210,210)
upper = (255,255,255)
thresh = cv2.inRange(img, lower, upper)
# get external contours and keep largest
contours = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = contours[0] if len(contours) == 2 else contours[1]
big_contour = max(contours, key=cv2.contourArea)
# get bounding box of big contour
x,y,w,h = cv2.boundingRect(big_contour)
# get the average color of the 4 pixels just outside of the bounding box corners
[[color1]] = img[y-1:y, x-1:x]
[[color2]] = img[y-1:y, x+w:x+w+1]
[[color3]] = img[y+h:y+h+1, x+w:x+w+1]
[[color4]] = img[y+h:y+h+1, x-1:x]
ave_color = (color1.astype(np.float32) + color2.astype(np.float32) + color3.astype(np.float32) + color4.astype(np.float32)) / 4
ave_color = ave_color.astype(np.uint8)
print(ave_color)
# fill color inside contour bounding box
result = img.copy()
result[y:y+h, x:x+w] = ave_color
# save results
cv2.imwrite('checks_object_color_filled.jpg', result)
# show results
cv2.imshow('thresh', thresh)
cv2.imshow('checks_color_filled', result)
cv2.waitKey(0)
Results:结果:
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