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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 |