import numpy as np import cv2 import functools import time from hivisionai.hycv.matting_tools import read_modnet_image def calTime(mark): if isinstance(mark, str): def decorater(func): @functools.wraps(func) def wrapper(*args, **kw): start_time = time.time() return_param = func(*args, **kw) print("[Mark-{}] {} 函数花费的时间为 {:.2f}.".format(mark, func.__name__, time.time() - start_time)) return return_param return wrapper return decorater else: func = mark @functools.wraps(func) def wrapper(*args, **kw): start_time = time.time() return_param = func(*args, **kw) print("{} 函数花费的时间为 {:.2f}.".format(func.__name__, time.time() - start_time)) return return_param return wrapper def standard_photo_resize(input_image: np.array, size): """ input_image: 输入图像,即高清照 size: 标准照的尺寸 """ resize_ratio = input_image.shape[0] / size[0] resize_item = int(round(input_image.shape[0] / size[0])) if resize_ratio >= 2: for i in range(resize_item - 1): if i == 0: result_image = cv2.resize(input_image, (size[1] * (resize_item - i - 1), size[0] * (resize_item - i - 1)), interpolation=cv2.INTER_AREA) else: result_image = cv2.resize(result_image, (size[1] * (resize_item - i - 1), size[0] * (resize_item - i - 1)), interpolation=cv2.INTER_AREA) else: result_image = cv2.resize(input_image, (size[1], size[0]), interpolation=cv2.INTER_AREA) return result_image def hollowOutFix(src: np.ndarray) -> np.ndarray: b, g, r, a = cv2.split(src) src_bgr = cv2.merge((b, g, r)) # -----------padding---------- # add_area = np.zeros((10, a.shape[1]), np.uint8) a = np.vstack((add_area, a, add_area)) add_area = np.zeros((a.shape[0], 10), np.uint8) a = np.hstack((add_area, a, add_area)) # -------------end------------ # _, a_threshold = cv2.threshold(a, 127, 255, 0) a_erode = cv2.erode(a_threshold, kernel=cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5)), iterations=3) contours, hierarchy = cv2.findContours(a_erode, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) contours = [x for x in contours] # contours = np.squeeze(contours) contours.sort(key=lambda c: cv2.contourArea(c), reverse=True) a_contour = cv2.drawContours(np.zeros(a.shape, np.uint8), contours[0], -1, 255, 2) # a_base = a_contour[1:-1, 1:-1] h, w = a.shape[:2] mask = np.zeros([h + 2, w + 2], np.uint8) # mask必须行和列都加2，且必须为uint8单通道阵列 cv2.floodFill(a_contour, mask=mask, seedPoint=(0, 0), newVal=255) a = cv2.add(a, 255 - a_contour) return cv2.merge((src_bgr, a[10:-10, 10:-10])) def resize_image_by_min(input_image, esp=600): """ 将图像缩放为最短边至少为600的图像。 :param input_image: 输入图像（OpenCV矩阵） :param esp: 缩放后的最短边长 :return: 缩放后的图像，缩放倍率 """ height, width = input_image.shape[0], input_image.shape[1] min_border = min(height, width) if min_border < esp: if height >= width: new_width = esp new_height = height * esp // width else: new_height = esp new_width = width * esp // height return cv2.resize(input_image, (new_width, new_height), interpolation=cv2.INTER_AREA), new_height / height else: return input_image, 1 def rotate_bound(image, angle): """ 一个旋转函数，输入一张图片和一个旋转角，可以实现不损失图像信息的旋转。 """ # grab the dimensions of the image and then determine the # center (h, w) = image.shape[:2] (cX, cY) = (w / 2, h / 2) # grab the rotation matrix (applying the negative of the # angle to rotate clockwise), then grab the sine and cosine # (i.e., the rotation components of the matrix) M = cv2.getRotationMatrix2D((cX, cY), -angle, 1.0) cos = np.abs(M[0, 0]) sin = np.abs(M[0, 1]) # compute the new bounding dimensions of the image nW = int((h * sin) + (w * cos)) nH = int((h * cos) + (w * sin)) # adjust the rotation matrix to take into account translation M[0, 2] += (nW / 2) - cX M[1, 2] += (nH / 2) - cY # perform the actual rotation and return the image return cv2.warpAffine(image, M, (nW, nH)), cos, sin def rotate_bound_4channels(image, a, angle): """ 一个旋转函数，输入一张图片和一个旋转角，可以实现不损失图像信息的旋转。 """ input_image, cos, sin = rotate_bound(image, angle) new_a, _, _ = rotate_bound(a, angle) # 对做matte旋转，以便之后merge b, g, r = cv2.split(input_image) result_image = cv2.merge((b, g, r, new_a)) # 得到抠图结果图的无损旋转结果 # perform the actual rotation and return the image return input_image, result_image, cos, sin def draw_picture_dots(image, dots, pen_size=10, pen_color=(0, 0, 255)): """ 给一张照片上绘制点。 image: Opencv图像矩阵 dots: 一堆点,形如[(100,100),(150,100)] pen_size: 画笔的大小 pen_color: 画笔的颜色 """ if isinstance(dots, dict): dots = [v for u, v in dots.items()] image = image.copy() dots = list(dots) for dot in dots: # print("dot: ", dot) x = dot[0] y = dot[1] cv2.circle(image, (int(x), int(y)), pen_size, pen_color, -1) return image def get_modnet_matting(input_image, sess, ref_size=512): """ 使用modnet模型对图像进行抠图处理。 :param input_image: 输入图像（opencv矩阵） :param sess: onnxruntime推理主体 :param ref_size: 缩放参数 :return: 抠图后的图像 """ input_name = sess.get_inputs()[0].name output_name = sess.get_outputs()[0].name im, width, length = read_modnet_image(input_image=input_image, ref_size=ref_size) matte = sess.run([output_name], {input_name: im}) matte = (matte[0] * 255).astype('uint8') matte = np.squeeze(matte) mask = cv2.resize(matte, (width, length), interpolation=cv2.INTER_AREA) b, g, r = cv2.split(np.uint8(input_image)) output_image = cv2.merge((b, g, r, mask)) return output_image def detect_distance(value, crop_heigh, max=0.06, min=0.04): """ 检测人头顶与照片顶部的距离是否在适当范围内。 输入：与顶部的差值 输出：(status, move_value) status=0 不动 status=1 人脸应向上移动（裁剪框向下移动） status-2 人脸应向下移动（裁剪框向上移动） --------------------------------------- value：头顶与照片顶部的距离 crop_heigh: 裁剪框的高度 max: 距离的最大值 min: 距离的最小值 --------------------------------------- """ value = value / crop_heigh # 头顶往上的像素占图像的比例 if min <= value <= max: return 0, 0 elif value > max: # 头顶往上的像素比例高于max move_value = value - max move_value = int(move_value * crop_heigh) # print("上移{}".format(move_value)) return 1, move_value else: # 头顶往上的像素比例低于min move_value = min - value move_value = int(move_value * crop_heigh) # print("下移{}".format(move_value)) return -1, move_value