Hello All,

We have a requirement for shape detection and the details is mentioned below. We need to compare a reference shapes ( available in the form of jpg or png) from a other image, containing all other different shapes (provided as a screenshot in an other jpg file) and detect if the image is present or not. If the image is available we need to highlight the draw a rectangle or circle in the screen shot image.

I am pasting the reference image and the screenshot image for your reference.

I would be glad if you could pls let me know the right approach to achieve the above mentioned objective.C:\fakepath\Screenshot.png

(/upfiles/1550234205238557.png)

I have tried template method and contour matching and detection

In case of contour method. I see only one image is recognized and the others are detected falsely. Thanking you in advance

import sys 
import cv2 
import numpy as np 

# Extract all the contours from the image 
def get_all_contours(img): 
    ref_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) 
    ret, thresh = cv2.threshold(ref_gray, 127, 255, 0) 
    contours, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE )
    return contours

# Extract reference contour from the image 
def get_ref_contour(img): 
    contours = get_all_contours(img)

    for contour in contours: 
        area = cv2.contourArea(contour) 
        img_area = img.shape[0] * img.shape[1] 
        if 0.05 < area/float(img_area) < 0.8: 
            return contour 

if __name__=='__main__': 

    img1 = cv2.imread(sys.argv[1]) 

    # Input image containing all the different shapes 
    img2 = cv2.imread(sys.argv[2]) 

    # Extract the reference contour 
    ref_contour = get_ref_contour(img1)

    # Extract all the contours from the input image 
    input_contours = get_all_contours(img2) 

    closest_contour = None
    min_dist = None
    contour_img = img2.copy()
    cv2.drawContours(contour_img, input_contours, -1, color=(0,0,0), thickness=3) 
    cv2.imshow('Contours', contour_img)
    # Finding the closest contour 
    for i, contour in enumerate(input_contours): 
        # Matching the shapes and taking the closest one using 
        # Comparison method CV_CONTOURS_MATCH_I3 (second argument)
        ret = cv2.matchShapes(ref_contour, contour, 3, 0.0)
        print("Contour %d matchs in %f" % (i, ret))
        if min_dist is None or ret < min_dist:
            min_dist = ret 
            closest_contour = contour

    cv2.drawContours(img2, [closest_contour], 0 , color=(0,0,0), thickness=3) 
    cv2.imshow('Best Matching', img2)
    cv2.waitKey()

Hello All,

We have a requirement for shape detection and the details is mentioned below. We need to compare a reference shapes ( available in the form of jpg or png) from a other image, containing all other different shapes (provided as a screenshot in an other jpg file) and detect if the image is present or not. If the image is available we need to highlight the draw a rectangle or circle in the screen shot image.

I am pasting the reference image and the screenshot image for your reference.

I would be glad if you could pls let me know the right approach to achieve the above mentioned objective.

(/upfiles/1550234205238557.png)

I have tried template method and contour matching and detection

In case of contour method. I see only one image is recognized and the others are detected falsely. Thanking you in advance

import sys 
import cv2 
import numpy as np 

# Extract all the contours from the image 
def get_all_contours(img): 
    ref_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) 
    ret, thresh = cv2.threshold(ref_gray, 127, 255, 0) 
    contours, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE )
    return contours

# Extract reference contour from the image 
def get_ref_contour(img): 
    contours = get_all_contours(img)

    for contour in contours: 
        area = cv2.contourArea(contour) 
        img_area = img.shape[0] * img.shape[1] 
        if 0.05 < area/float(img_area) < 0.8: 
            return contour 

if __name__=='__main__': 

    img1 = cv2.imread(sys.argv[1]) 

    # Input image containing all the different shapes 
    img2 = cv2.imread(sys.argv[2]) 

    # Extract the reference contour 
    ref_contour = get_ref_contour(img1)

    # Extract all the contours from the input image 
    input_contours = get_all_contours(img2) 

    closest_contour = None
    min_dist = None
    contour_img = img2.copy()
    cv2.drawContours(contour_img, input_contours, -1, color=(0,0,0), thickness=3) 
    cv2.imshow('Contours', contour_img)
    # Finding the closest contour 
    for i, contour in enumerate(input_contours): 
        # Matching the shapes and taking the closest one using 
        # Comparison method CV_CONTOURS_MATCH_I3 (second argument)
        ret = cv2.matchShapes(ref_contour, contour, 3, 0.0)
        print("Contour %d matchs in %f" % (i, ret))
        if min_dist is None or ret < min_dist:
            min_dist = ret 
            closest_contour = contour

    cv2.drawContours(img2, [closest_contour], 0 , color=(0,0,0), thickness=3) 
    cv2.imshow('Best Matching', img2)
    cv2.waitKey()

Hello All,

We have a requirement for shape detection and the details is mentioned below. We need to compare a reference shapes ( available in the form of jpg or png) from a other image, containing all other different shapes (provided as a screenshot in an other jpg file) and detect if the image is present or not. If the image is available we need to highlight the draw a rectangle or circle in the screen shot image.

I am pasting the reference image and the screenshot image for your reference.

I would be glad if you could pls let me know the right approach to achieve the above mentioned objective.

I have tried template method and contour matching and detection

In case of contour method. I see only one image is recognized and the others are detected falsely. Thanking you in advance

import sys 
import cv2 
import numpy as np 

# Extract all the contours from the image 
def get_all_contours(img): 
    ref_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) 
    ret, thresh = cv2.threshold(ref_gray, 127, 255, 0) 
    contours, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE )
    return contours

# Extract reference contour from the image 
def get_ref_contour(img): 
    contours = get_all_contours(img)

    for contour in contours: 
        area = cv2.contourArea(contour) 
        img_area = img.shape[0] * img.shape[1] 
        if 0.05 < area/float(img_area) < 0.8: 
            return contour 

if __name__=='__main__': 

    img1 = cv2.imread(sys.argv[1]) 

    # Input image containing all the different shapes 
    img2 = cv2.imread(sys.argv[2]) 

    # Extract the reference contour 
    ref_contour = get_ref_contour(img1)

    # Extract all the contours from the input image 
    input_contours = get_all_contours(img2) 

    closest_contour = None
    min_dist = None
    contour_img = img2.copy()
    cv2.drawContours(contour_img, input_contours, -1, color=(0,0,0), thickness=3) 
    cv2.imshow('Contours', contour_img)
    # Finding the closest contour 
    for i, contour in enumerate(input_contours): 
        # Matching the shapes and taking the closest one using 
        # Comparison method CV_CONTOURS_MATCH_I3 (second argument)
        ret = cv2.matchShapes(ref_contour, contour, 3, 0.0)
        print("Contour %d matchs in %f" % (i, ret))
        if min_dist is None or ret < min_dist:
            min_dist = ret 
            closest_contour = contour

    cv2.drawContours(img2, [closest_contour], 0 , color=(0,0,0), thickness=3) 
    cv2.imshow('Best Matching', img2)
    cv2.waitKey()
cv2.waitKey()[C:\fakepath\Screenshot.png](/upfiles/15502380355683715.png)

Hello All,C:\fakepath\Screenshot.png

import sys 
import cv2 
import numpy as np 

# Extract all the contours from the image 
def get_all_contours(img): 
    ref_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) 
    ret, thresh = cv2.threshold(ref_gray, 127, 255, 0) 
    contours, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE )
    return contours

# Extract reference contour from the image 
def get_ref_contour(img): 
    contours = get_all_contours(img)

    for contour in contours: 
        area = cv2.contourArea(contour) 
        img_area = img.shape[0] * img.shape[1] 
        if 0.05 < area/float(img_area) < 0.8: 
            return contour 

if __name__=='__main__': 

    img1 = cv2.imread(sys.argv[1]) 

    # Input image containing all the different shapes 
    img2 = cv2.imread(sys.argv[2]) 

    # Extract the reference contour 
    ref_contour = get_ref_contour(img1)

    # Extract all the contours from the input image 
    input_contours = get_all_contours(img2) 

    closest_contour = None
    min_dist = None
    contour_img = img2.copy()
    cv2.drawContours(contour_img, input_contours, -1, color=(0,0,0), thickness=3) 
    cv2.imshow('Contours', contour_img)
    # Finding the closest contour 
    for i, contour in enumerate(input_contours): 
        # Matching the shapes and taking the closest one using 
        # Comparison method CV_CONTOURS_MATCH_I3 (second argument)
        ret = cv2.matchShapes(ref_contour, contour, 3, 0.0)
        print("Contour %d matchs in %f" % (i, ret))
        if min_dist is None or ret < min_dist:
            min_dist = ret 
            closest_contour = contour

    cv2.drawContours(img2, [closest_contour], 0 , color=(0,0,0), thickness=3) 
    cv2.imshow('Best Matching', img2)
    cv2.waitKey()[C:\fakepath\Screenshot.png](/upfiles/15502380355683715.png)