In [9]:
import cv2
import numpy as np
# Load our image
image = cv2.imread('images/bunchofshapes.jpg')
cv2.imshow('0 - Original Image', image)
cv2.waitKey(0)
# Create a black image with same dimensions as our loaded image
blank_image = np.zeros((image.shape[0], image.shape[1], 3))
# Create a copy of our original image
orginal_image = image
# Grayscale our image
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
# Find Canny edges
edged = cv2.Canny(gray, 50, 200)
cv2.imshow('1 - Canny Edges', edged)
cv2.waitKey(0)
# Find contours and print how many were found
contours, hierarchy = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
print ("Number of contours found = ", len(contours))
#Draw all contours
cv2.drawContours(blank_image, contours, -1, (0,255,0), 3)
cv2.imshow('2 - All Contours over blank image', blank_image)
cv2.waitKey(0)
# Draw all contours over blank image
cv2.drawContours(image, contours, -1, (0,255,0), 3)
cv2.imshow('3 - All Contours', image)
cv2.waitKey(0)
cv2.destroyAllWindows()
In [11]:
import cv2
import numpy as np
# Function we'll use to display contour area
def get_contour_areas(contours):
# returns the areas of all contours as list
all_areas = []
for cnt in contours:
area = cv2.contourArea(cnt)
all_areas.append(area)
return all_areas
# Load our image
image = cv2.imread('images/bunchofshapes.jpg')
orginal_image = image
# Let's print the areas of the contours before sorting
print "Contor Areas before sorting",
print get_contour_areas(contours)
# Sort contours large to small
sorted_contours = sorted(contours, key=cv2.contourArea, reverse=True)
#sorted_contours = sorted(contours, key=cv2.contourArea, reverse=True)[:3]
print "Contor Areas after sorting",
print get_contour_areas(sorted_contours)
# Iterate over our contours and draw one at a time
for c in sorted_contours:
cv2.drawContours(orginal_image, [c], -1, (255,0,0), 3)
cv2.waitKey(0)
cv2.imshow('Contours by area', orginal_image)
cv2.waitKey(0)
cv2.destroyAllWindows()
In [14]:
import cv2
import numpy as np
# Functions we'll use for sorting by position
def x_cord_contour(contours):
#Returns the X cordinate for the contour centroid
if cv2.contourArea(contours) > 10:
M = cv2.moments(contours)
return (int(M['m10']/M['m00']))
def label_contour_center(image, c):
# Places a red circle on the centers of contours
M = cv2.moments(c)
cx = int(M['m10'] / M['m00'])
cy = int(M['m01'] / M['m00'])
# Draw the countour number on the image
cv2.circle(image,(cx,cy), 10, (0,0,255), -1)
return image
# Load our image
image = cv2.imread('images/bunchofshapes.jpg')
orginal_image = image.copy()
# Computer Center of Mass or centroids and draw them on our image
for (i, c) in enumerate(contours):
orig = label_contour_center(image, c)
cv2.imshow("4 - Contour Centers ", image)
cv2.waitKey(0)
# Sort by left to right using our x_cord_contour function
contours_left_to_right = sorted(contours, key = x_cord_contour, reverse = False)
# Labeling Contours left to right
for (i,c) in enumerate(contours_left_to_right):
cv2.drawContours(orginal_image, [c], -1, (0,0,255), 3)
M = cv2.moments(c)
cx = int(M['m10'] / M['m00'])
cy = int(M['m01'] / M['m00'])
cv2.putText(orginal_image, str(i+1), (cx, cy), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
cv2.imshow('6 - Left to Right Contour', orginal_image)
cv2.waitKey(0)
(x, y, w, h) = cv2.boundingRect(c)
# Let's now crop each contour and save these images
cropped_contour = orginal_image[y:y + h, x:x + w]
image_name = "output_shape_number_" + str(i+1) + ".jpg"
print image_name
cv2.imwrite(image_name, cropped_contour)
cv2.destroyAllWindows()