import cv2 | |
from PIL import Image | |
import numpy as np | |
def get_concat_h(im1, im2): | |
dst = Image.new('RGB', (im1.width + im2.width, im1.height)) | |
dst.paste(im1, (0, 0)) | |
dst.paste(im2, (im1.width, 0)) | |
return dst | |
def get_concat_v(im1, im2): | |
dst = Image.new('RGB', (im1.width, im1.height + im2.height)) | |
dst.paste(im1, (0, 0)) | |
dst.paste(im2, (0, im1.height)) | |
return dst | |
def hsv_to_rgb(h, s, v): | |
bgr = cv2.cvtColor(np.array([[[h, s, v]]], dtype=np.uint8), cv2.COLOR_HSV2BGR)[0][0] | |
return [bgr[2]/255, bgr[1]/255, bgr[0]/255] | |
# def remove_bg( | |
# path, | |
# BLUR = 21, | |
# CANNY_THRESH_1 = 10, | |
# CANNY_THRESH_2 = 200, | |
# MASK_DILATE_ITER = 10, | |
# MASK_ERODE_ITER = 10, | |
# MASK_COLOR = (0.0,0.0,1.0), | |
# ): | |
# img = cv2.imread(path) | |
# gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) | |
# edges = cv2.Canny(gray, CANNY_THRESH_1, CANNY_THRESH_2) | |
# edges = cv2.dilate(edges, None) | |
# edges = cv2.erode(edges, None) | |
# contour_info = [] | |
# contours, _ = cv2.findContours(edges, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE) | |
# for c in contours: | |
# contour_info.append(( | |
# c, | |
# cv2.isContourConvex(c), | |
# cv2.contourArea(c), | |
# )) | |
# contour_info = sorted(contour_info, key=lambda c: c[2], reverse=True) | |
# max_contour = contour_info[0] | |
# mask = np.zeros(edges.shape) | |
# cv2.fillConvexPoly(mask, max_contour[0], (255)) | |
# mask = cv2.dilate(mask, None, iterations=MASK_DILATE_ITER) | |
# mask = cv2.erode(mask, None, iterations=MASK_ERODE_ITER) | |
# mask = cv2.GaussianBlur(mask, (BLUR, BLUR), 0) | |
# mask_stack = np.dstack([mask]*3) # Create 3-channel alpha mask | |
# mask_stack = mask_stack.astype('float32') / 255.0 # Use float matrices, | |
# img = img.astype('float32') / 255.0 # for easy blending | |
# masked = (mask_stack * img) + ((1-mask_stack) * MASK_COLOR) # Blend | |
# masked = (masked * 255).astype('uint8') # Convert back to 8-bit | |
# c_blue, c_green, c_red = cv2.split(img) | |
# img_a = cv2.merge((c_red, c_green, c_blue, mask.astype('float32') / 255.0)) | |
# index = np.where(img_a[:, :, 3] == 0) | |
# #img_a[index] = [1.0, 1.0, 1.0, 1.0] | |
# return img_a |