Delete utils_mask.py

utils_mask.py

@@ -1,167 +0,0 @@
-import numpy as np
-import cv2
-from PIL import Image, ImageDraw
-
-label_map = {
-    "background": 0,
-    "hat": 1,
-    "hair": 2,
-    "sunglasses": 3,
-    "upper_clothes": 4,
-    "skirt": 5,
-    "pants": 6,
-    "dress": 7,
-    "belt": 8,
-    "left_shoe": 9,
-    "right_shoe": 10,
-    "head": 11,
-    "left_leg": 12,
-    "right_leg": 13,
-    "left_arm": 14,
-    "right_arm": 15,
-    "bag": 16,
-    "scarf": 17,
-}
-
-def extend_arm_mask(wrist, elbow, scale):
-  wrist = elbow + scale * (wrist - elbow)
-  return wrist
-
-def hole_fill(img):
-    img = np.pad(img[1:-1, 1:-1], pad_width = 1, mode = 'constant', constant_values=0)
-    img_copy = img.copy()
-    mask = np.zeros((img.shape[0] + 2, img.shape[1] + 2), dtype=np.uint8)
-
-    cv2.floodFill(img, mask, (0, 0), 255)
-    img_inverse = cv2.bitwise_not(img)
-    dst = cv2.bitwise_or(img_copy, img_inverse)
-    return dst
-
-def refine_mask(mask):
-    contours, hierarchy = cv2.findContours(mask.astype(np.uint8),
-                                           cv2.RETR_CCOMP, cv2.CHAIN_APPROX_TC89_L1)
-    area = []
-    for j in range(len(contours)):
-        a_d = cv2.contourArea(contours[j], True)
-        area.append(abs(a_d))
-    refine_mask = np.zeros_like(mask).astype(np.uint8)
-    if len(area) != 0:
-        i = area.index(max(area))
-        cv2.drawContours(refine_mask, contours, i, color=255, thickness=-1)
-
-    return refine_mask
-
-def get_mask_location(model_type, category, model_parse: Image.Image, keypoint: dict, width=384,height=512):
-    im_parse = model_parse.resize((width, height), Image.NEAREST)
-    parse_array = np.array(im_parse)
-
-    if model_type == 'hd':
-        arm_width = 60
-    elif model_type == 'dc':
-        arm_width = 45
-    else:
-        raise ValueError("model_type must be \'hd\' or \'dc\'!")
-
-    parse_head = (parse_array == 1).astype(np.float32) + \
-                 (parse_array == 3).astype(np.float32) + \
-                 (parse_array == 11).astype(np.float32)
-
-    parser_mask_fixed = (parse_array == label_map["left_shoe"]).astype(np.float32) + \
-                        (parse_array == label_map["right_shoe"]).astype(np.float32) + \
-                        (parse_array == label_map["hat"]).astype(np.float32) + \
-                        (parse_array == label_map["sunglasses"]).astype(np.float32) + \
-                        (parse_array == label_map["bag"]).astype(np.float32)
-
-    parser_mask_changeable = (parse_array == label_map["background"]).astype(np.float32)
-
-    arms_left = (parse_array == 14).astype(np.float32)
-    arms_right = (parse_array == 15).astype(np.float32)
-
-    if category == 'dresses':
-        parse_mask = (parse_array == 7).astype(np.float32) + \
-                     (parse_array == 4).astype(np.float32) + \
-                     (parse_array == 5).astype(np.float32) + \
-                     (parse_array == 6).astype(np.float32)
-
-        parser_mask_changeable += np.logical_and(parse_array, np.logical_not(parser_mask_fixed))
-
-    elif category == 'upper_body':
-        parse_mask = (parse_array == 4).astype(np.float32) + (parse_array == 7).astype(np.float32)
-        parser_mask_fixed_lower_cloth = (parse_array == label_map["skirt"]).astype(np.float32) + \
-                                        (parse_array == label_map["pants"]).astype(np.float32)
-        parser_mask_fixed += parser_mask_fixed_lower_cloth
-        parser_mask_changeable += np.logical_and(parse_array, np.logical_not(parser_mask_fixed))
-    elif category == 'lower_body':
-        parse_mask = (parse_array == 6).astype(np.float32) + \
-                     (parse_array == 12).astype(np.float32) + \
-                     (parse_array == 13).astype(np.float32) + \
-                     (parse_array == 5).astype(np.float32)
-        parser_mask_fixed += (parse_array == label_map["upper_clothes"]).astype(np.float32) + \
-                             (parse_array == 14).astype(np.float32) + \
-                             (parse_array == 15).astype(np.float32)
-        parser_mask_changeable += np.logical_and(parse_array, np.logical_not(parser_mask_fixed))
-    else:
-        raise NotImplementedError
-
-    # Load pose points
-    pose_data = keypoint["pose_keypoints_2d"]
-    pose_data = np.array(pose_data)
-    pose_data = pose_data.reshape((-1, 2))
-
-    im_arms_left = Image.new('L', (width, height))
-    im_arms_right = Image.new('L', (width, height))
-    arms_draw_left = ImageDraw.Draw(im_arms_left)
-    arms_draw_right = ImageDraw.Draw(im_arms_right)
-    if category == 'dresses' or category == 'upper_body':
-        shoulder_right = np.multiply(tuple(pose_data[2][:2]), height / 512.0)
-        shoulder_left = np.multiply(tuple(pose_data[5][:2]), height / 512.0)
-        elbow_right = np.multiply(tuple(pose_data[3][:2]), height / 512.0)
-        elbow_left = np.multiply(tuple(pose_data[6][:2]), height / 512.0)
-        wrist_right = np.multiply(tuple(pose_data[4][:2]), height / 512.0)
-        wrist_left = np.multiply(tuple(pose_data[7][:2]), height / 512.0)
-        ARM_LINE_WIDTH = int(arm_width / 512 * height)
-        size_left = [shoulder_left[0] - ARM_LINE_WIDTH // 2, shoulder_left[1] - ARM_LINE_WIDTH // 2, shoulder_left[0] + ARM_LINE_WIDTH // 2, shoulder_left[1] + ARM_LINE_WIDTH // 2]
-        size_right = [shoulder_right[0] - ARM_LINE_WIDTH // 2, shoulder_right[1] - ARM_LINE_WIDTH // 2, shoulder_right[0] + ARM_LINE_WIDTH // 2,
-                      shoulder_right[1] + ARM_LINE_WIDTH // 2]
-        
-
-        if wrist_right[0] <= 1. and wrist_right[1] <= 1.:
-            im_arms_right = arms_right
-        else:
-            wrist_right = extend_arm_mask(wrist_right, elbow_right, 1.2)
-            arms_draw_right.line(np.concatenate((shoulder_right, elbow_right, wrist_right)).astype(np.uint16).tolist(), 'white', ARM_LINE_WIDTH, 'curve')
-            arms_draw_right.arc(size_right, 0, 360, 'white', ARM_LINE_WIDTH // 2)
-
-        if wrist_left[0] <= 1. and wrist_left[1] <= 1.:
-            im_arms_left = arms_left
-        else:
-            wrist_left = extend_arm_mask(wrist_left, elbow_left, 1.2)
-            arms_draw_left.line(np.concatenate((wrist_left, elbow_left, shoulder_left)).astype(np.uint16).tolist(), 'white', ARM_LINE_WIDTH, 'curve')
-            arms_draw_left.arc(size_left, 0, 360, 'white', ARM_LINE_WIDTH // 2)
-
-        hands_left = np.logical_and(np.logical_not(im_arms_left), arms_left)
-        hands_right = np.logical_and(np.logical_not(im_arms_right), arms_right)
-        parser_mask_fixed += hands_left + hands_right
-
-    parser_mask_fixed = np.logical_or(parser_mask_fixed, parse_head)
-    parse_mask = cv2.dilate(parse_mask, np.ones((5, 5), np.uint16), iterations=5)
-    if category == 'dresses' or category == 'upper_body':
-        neck_mask = (parse_array == 18).astype(np.float32)
-        neck_mask = cv2.dilate(neck_mask, np.ones((5, 5), np.uint16), iterations=1)
-        neck_mask = np.logical_and(neck_mask, np.logical_not(parse_head))
-        parse_mask = np.logical_or(parse_mask, neck_mask)
-        arm_mask = cv2.dilate(np.logical_or(im_arms_left, im_arms_right).astype('float32'), np.ones((5, 5), np.uint16), iterations=4)
-        parse_mask += np.logical_or(parse_mask, arm_mask)
-
-    parse_mask = np.logical_and(parser_mask_changeable, np.logical_not(parse_mask))
-
-    parse_mask_total = np.logical_or(parse_mask, parser_mask_fixed)
-    inpaint_mask = 1 - parse_mask_total
-    img = np.where(inpaint_mask, 255, 0)
-    dst = hole_fill(img.astype(np.uint8))
-    dst = refine_mask(dst)
-    inpaint_mask = dst / 255 * 1
-    mask = Image.fromarray(inpaint_mask.astype(np.uint8) * 255)
-    mask_gray = Image.fromarray(inpaint_mask.astype(np.uint8) * 127)
-
-    return mask, mask_gray