通过 OpenCV 裁剪检测到的图像

Question

Can someone help me on how to crop the detected portion of the image. I am struggling to pick the correct x and y axis. I wish to know how to pass the detected boundary X and Y axis to the image to crop.

Note: The Aim is to crop exactly the detected image for comparing the template vs detected image. I am aware of how to crop and compare an image. Problem is to find the exactly boundary that are detected in the original image.

Code to detect:

import cv2
import numpy as np 
img_rgb = cv2.imread('Foo_1.png')
img_gray = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2GRAY) 
template = cv2.imread('template.PNG', 0)
w, h = template.shape[::-1]
res = cv2.matchTemplate(img_gray, template, cv2.TM_CCOEFF_NORMED)
threshold = 0.8
loc = np.where(res >= threshold)
for pt in zip(*loc[::-1]):
    cv2.rectangle(img_rgb, pt, (pt[0] + w, pt[1] + h), (0, 255, 255), 2)
cv2.imshow('Detected', img_rgb)
crop_img = img_rgb[y:y+h, x:x+w] --> Unclear about how to pass the x nd y axis values automatically from detected boundary.
cv2.imshow("cropped", crop_img)
cv2.waitKey(0)

Image_1.png

template.png

Expected Output:- The detected image must to be cropped and match with template image.

Answer 1

The detected image is cropped and matches the image template.

import cv2
img_rgb = cv2.imread('Image_1.jpg')
img_gray = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2GRAY) 
template = cv2.imread('template.png', cv2.IMREAD_GRAYSCALE)
w, h = template.shape
res = cv2.matchTemplate(img_gray, template, cv2.TM_CCOEFF_NORMED)
_, _, _, maxLoc=cv2.minMaxLoc(res)
cv2.rectangle(img_rgb, maxLoc, (maxLoc[0]+h, maxLoc[1]+w), (0, 255, 255), 2)
cv2.imshow('Detected', img_rgb)
crop_img = img_rgb[maxLoc[1]:maxLoc[1]+w, maxLoc[0]:maxLoc[0]+h, :] 
cv2.imshow("cropped", crop_img)
cv2.waitKey(0)
cv2.destroyAllWindows()

Answer 2

This code below will help you use the mouse events to record the co-ordinates n

1. This code will help draw shape on any image
2. reset shape on selection
3. crop the selection You can run the code by: python3 capture_events.py --image name_image.jpg

# import the necessary packages
import argparse
import cv2

# initialize the list of reference points and boolean indicating
# whether cropping is being performed or not
ref_point = []
cropping = False

def shape_selection(event, x, y, flags, param):
  # grab references to the global variables
  global ref_point, cropping

  # if the left mouse button was clicked, record the starting
  # (x, y) coordinates and indicate that cropping is being
  # performed
  if event == cv2.EVENT_LBUTTONDOWN:
    ref_point = [(x, y)]
    cropping = True

  # check to see if the left mouse button was released
  elif event == cv2.EVENT_LBUTTONUP:
    # record the ending (x, y) coordinates and indicate that
    # the cropping operation is finished
    ref_point.append((x, y))
    cropping = False

    # draw a rectangle around the region of interest
    cv2.rectangle(image, ref_point[0], ref_point[1], (0, 255, 0), 2)
    cv2.imshow("image", image)

# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True, help="Path to the image")
args = vars(ap.parse_args())

# load the image, clone it, and setup the mouse callback function
image = cv2.imread(args["image"])
clone = image.copy()
cv2.namedWindow("image")
cv2.setMouseCallback("image", shape_selection)

# keep looping until the 'q' key is pressed
while True:
  # display the image and wait for a keypress
  cv2.imshow("image", image)
  key = cv2.waitKey(1) & 0xFF

  # if the 'r' key is pressed, reset the cropping region
  if key == ord("r"):
    image = clone.copy()

  # if the 'c' key is pressed, break from the loop
  elif key == ord("c"):
    break

# if there are two reference points, then crop the region of interest
# from teh image and display it
if len(ref_point) == 2:
  crop_img = clone[ref_point[0][1]:ref_point[1][1], ref_point[0][0]:ref_point[1][0]]
  cv2.imshow("crop_img", crop_img)
  cv2.waitKey(0)

# close all open windows
cv2.destroyAllWindows()