I have an image directories like this:
and I have a code like this following:
base_dir = 'dataset/'
data = []
target = []
for folder in os.listdir(base_dir):
files = os.listdir(os.path.join(base_dir, folder))
for batch in files:
img = cv2.imread(os.path.join(base_dir, folder, batch))
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(gray, 170, 255, cv2.THRESH_BINARY)
canny = cv2.Canny(thresh,50,100)
circles = cv2.HoughCircles(canny,cv2.HOUGH_GRADIENT,1,10000,param1=50,param2=30,minRadius=0,maxRadius=1000)
height,width = gray.shape
r = 0
mask = np.zeros((height,width), np.uint8)
for i in circles[0,:]:
cv2.circle(img,(i[0],i[1]),i[2],(0,255,0),3)
cv2.circle(mask,(i[0],i[1]),i[2],(255,255,255),thickness=-1)
blank_image = img[:int(i[1]),:int(i[1])]
masked_data = cv2.bitwise_and(gray, gray, mask=mask)
_,thresh = cv2.threshold(mask,1,255,cv2.THRESH_BINARY)
contours = cv2.findContours(thresh,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
x,y,w,h = cv2.boundingRect(contours[0][0])
crop = masked_data[y:y+h,x:x+w]
r = i[2]
crop_dim =cv2.cvtColor(crop, cv2.COLOR_RGB2BGR)
# liang's code for reference
def daugman_normalization(image, height, width, r_in, r_out):
thetas = np.arange(0, 2 * np.pi, 2 * np.pi / width) # Theta values
r_out = r_in + r_out
# Create empty flatten image
flat = np.zeros((height,width, 3), np.uint8)
circle_x = int(image.shape[0] / 2)
circle_y = int(image.shape[1] / 2)
for i in range(width):
for j in range(height):
theta = thetas[i] # value of theta coordinate
r_pro = j / height # value of r coordinate(normalized)
# get coordinate of boundaries
Xi = circle_x + r_in * np.cos(theta)
Yi = circle_y + r_in * np.sin(theta)
Xo = circle_x + r_out * np.cos(theta)
Yo = circle_y + r_out * np.sin(theta)
# the matched cartesian coordinates for the polar coordinates
Xc = (1 - r_pro) * Xi + r_pro * Xo
Yc = (1 - r_pro) * Yi + r_pro * Yo
color = image[int(Xc)][int(Yc)] # color of the pixel
flat[j][i] = color
return flat
try:
image_nor = daugman_normalization(crop_dim,30,180,50,50)
except:
image_nor = daugman_normalization(crop_dim,30,180,45,45)
image_nor = image_nor[:,:,0]
image_nor = np.asarray(image_nor)
image_nor = image_nor.flatten()
data.append(image_nor)
nama_target = os.path.splitext(folder)
target.append(nama_target)
all_imgs = []
for img in data:
all_imgs.append(img)
all_imgs.append(target)# all_imgs.append(label)
columns = []
x = range(1,5400)
for n in x:
columns.append(n)
columns.append("target")
df = pd.DataFrame(all_imgs,columns = columns)
df
and the dataframe output like this:
but, I wanna make a "target" column with the folder name (directories of image), and the image have 5400 pixel with total all column 5401 (5400 for the value of image's pixel and 1 another column for target name)
and my expect DataFrame output with the following image:
How can I change the code with the output like this?
Thank you for advance!