def kmeans_img(img):
km = KMeans(MAX_EPOCH, False)
pts = []
for i in range(RAW_IMG_SIZE_X):
for j in range(RAW_IMG_SIZE_Y):
if (img[j][i] == 255):
pts.append((i, j))
km.add_data_pts(pts)
km.add_cluster_pt([random.randint(0, RAW_IMG_SIZE_X),
random.randint(0, RAW_IMG_SIZE_Y)], "red")
km.add_cluster_pt([random.randint(0, RAW_IMG_SIZE_X),
random.randint(0, RAW_IMG_SIZE_Y)], "green")
km.add_cluster_pt([random.randint(0, RAW_IMG_SIZE_X),
random.randint(0, RAW_IMG_SIZE_Y)], "blue")
km.add_cluster_pt([random.randint(0, RAW_IMG_SIZE_X),
random.randint(0, RAW_IMG_SIZE_Y)], "yellow")
km.run_alg()
km.print_cluster_pts()
def center_contours(img):
#trace_cnt = get_num_clusters_from_trace(img)
img_orig = img.copy()
RAW_IMG_SIZE_X = len(img[0])
RAW_IMG_SIZE_Y = len(img)
#img = adjust_gamma(img, 2.5)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
#gray = erode_img(img)
blurred = cv2.GaussianBlur(gray, (3, 3), 0)
# fixed threshold used for contour mapping
thresh = cv2.threshold(blurred, 195, 255, cv2.THRESH_BINARY)[1]
# adaptive threshold used for finding number of cluster points
thresh_a = cv2.adaptiveThreshold(blurred, 255, cv2.ADAPTIVE_THRESH_MEAN_C, \
cv2.THRESH_BINARY, 1001, 35)
# get contours from threshold
cnts = cv2.findContours(thresh.copy(),
cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
# TODO: rework this too
# need at least 2 contours so if fixed-threshold contours are not found,
# decrease threshold filter until found
tv = 255
while(len(cnts) < 2 and tv > 0):
thresh = cv2.threshold(blurred, tv, 255, cv2.THRESH_BINARY)[1]
cnts = cv2.findContours(thresh.copy(),
cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
tv -= 10
# adaptive as fallback
if (tv < 0):
#thresh = cv2.adaptiveThreshold(blurred, 255, cv2.ADAPTIVE_THRESH_MEAN_C, \
# cv2.THRESH_BINARY, 1001, 35)
cnts = cv2.findContours(thresh_a.copy(),
cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
# get non-zero ratio
num_nonzeros = np.count_nonzero(thresh_a)
nonzero_ratio = num_nonzeros / (RAW_IMG_SIZE_X * RAW_IMG_SIZE_Y)
zero_ratio = ((RAW_IMG_SIZE_X * RAW_IMG_SIZE_Y) - num_nonzeros) / \
(RAW_IMG_SIZE_X * RAW_IMG_SIZE_Y)
#print(nonzero_ratio)
#cv2.imshow("thresh", thresh)
#cv2.waitKey(0)
# TODO: need better way to automatically pick cluster amount
# set number of clusters based on amount of contours
num_clusters = int(MAX_NUM_CLUSTERS * nonzero_ratio)
#num_clusters = int((np.log(nonzero_ratio) + 1) * MAX_NUM_CLUSTERS)
if (num_clusters > len(cnts)):
num_clusters = int(len(cnts)/2)
if (num_clusters < 1):
num_clusters = 1
#print("num_clusters = ", num_clusters)
# store list of contour center pts
contour_pts = []
contour_map = {}
for c in cnts:
area = cv2.contourArea(c)
# dropout contours that are too small/noise
if (area < 5):
continue
# compute the center of the contour
M = cv2.moments(c)
if (M["m00"] != 0):
cX = int(M["m10"] / M["m00"])
cY = int(M["m01"] / M["m00"])
else:
cX = 0
cY = 0
contour_pts.append((cX, cY))
contour_map[(cX, cY)] = area
# draw the contour and center of the shape on the image
cv2.drawContours(img, [c], -1, (255, 0, 0), 2)
cv2.circle(img, (cX, cY), 7, (255, 255, 255), -1)
cv2.putText(img, "center", (cX - 20, cY - 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2)
# DEFAULT: if no area, add a center point to be the middle of the image
if (len(contour_pts) == 0):
pt = (RAW_IMG_SIZE_X/2, RAW_IMG_SIZE_Y/2)
contour_pts.append(pt)
contour_map[pt] = [RAW_IMG_SIZE_X * RAW_IMG_SIZE_Y]
# show image
#cv2.imshow("contours", img)
#cv2.waitKey(0)
# perform kmeans on contour points
km = KMeans(MAX_EPOCH, False)
km.add_data_pts(contour_pts)
# get the num_clusters largest contour points and store the points
# as a list
largest_contour_pts = \
list(dict(Counter(contour_map).most_common(num_clusters)).keys())
#print(len(contour_pts))
if (num_clusters > len(contour_pts)):
num_clusters = len(contour_pts)
# create n clusters (based on non-zero pixel ratio)
for n in range(num_clusters):
# using random points
#km.add_cluster_pt([random.randint(0, RAW_IMG_SIZE_X-1),
# random.randint(0, RAW_IMG_SIZE_Y-1)], "CENTER"+str(n))
# initialize initial cluster points to be the locations of the LARGEST
# contours.
try: # TODO: revise this whole concept
km.add_cluster_pt([largest_contour_pts[n][0],
largest_contour_pts[n][1]], "CENTER"+str(n))
except: # random point if out of range (?)
km.add_cluster_pt([random.randint(0, RAW_IMG_SIZE_X),
random.randint(0, RAW_IMG_SIZE_Y)], "CENTER"+str(n))
km.run_alg()
#km.print_cluster_pts()
i = 0
segment_imgs = []
segment_pts = []
#print(len(km.cluster_pts))
for cp in km.cluster_pts:
farthest_pt = km.get_farthest_x_and_y(i)
fX = farthest_pt[0] if farthest_pt[0] != 0 else RAW_IMG_SIZE_X
fY = farthest_pt[1] if farthest_pt[1] != 0 else RAW_IMG_SIZE_Y
cN = cp[1]
# skip cluster in case nan
try:
cX = int(cp[0][0])
cY = int(cp[0][1])
except IndexError:
i += 1
continue
# draw the contour and center of the shape on the image
cv2.circle(img, (cX, cY), 10, (255, 0, 0), -1)
cv2.putText(img, cN, (cX - 20, cY - 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)
# coordinates for top-left and bottom-right points of the rectangle
# centered at cluster point
pt1 = [int(cX-fX), int(cY-fY)]
pt2 = [int(cX+fX), int(cY+fY)]
# restrict coordinates to be within original images resolution
if (pt1[0] < 0):
pt1[0] = 0
if (pt2[0] < 0):
pt2[0] = 0
if (pt1[1] < 0):
pt1[1] = 0
if (pt2[1] < 0):
pt2[1] = 0
if (pt1[0] >= RAW_IMG_SIZE_X):
pt1[0] = RAW_IMG_SIZE_X-1
if (pt1[1] >= RAW_IMG_SIZE_Y):
pt1[1] = RAW_IMG_SIZE_Y-1
if (pt2[0] >= RAW_IMG_SIZE_X):
pt2[0] = RAW_IMG_SIZE_X-1
if (pt2[1] >= RAW_IMG_SIZE_Y):
pt2[1] = RAW_IMG_SIZE_Y-1
pt1 = tuple(pt1)
pt2 = tuple(pt2)
#print(pt1, pt2)
# draw on image
cv2.rectangle(img, pt1, pt2, (0, 0, 255), 3)
# crop original image and store in array
crop_img = img_orig[pt1[1]:pt2[1], pt1[0]:pt2[0]]
# exclude empty segments
if (crop_img.shape[1] * crop_img.shape[2] != 0):
segment_imgs.append(crop_img)
segment_pts.append((pt1, pt2))
#cv2.imshow("cropped", crop_img)
#cv2.waitKey(0)
i += 1
# show image
#cv2.imshow("Image", img)
#cv2.waitKey(0)
return segment_imgs, segment_pts
