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import cv2
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import numpy as np
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import dlib
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from matplotlib import pyplot as plt
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def idCardPicGenerate(img, type):
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detector = dlib.get_frontal_face_detector() # 返回人脸的矩形框
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# 返回人脸重要的68个点的位置
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landmark_predictor = dlib.shape_predictor('E:/Digital_Image_Process-master/function/IdCardPicGenerate/shape_predictor_68_face_landmarks.dat')
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print("11111")
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if img.shape[2] == 4:
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img = cv2.cvtColor(img,cv2.COLOR_RGBA2BGR)
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imgg = img.copy()
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t = img.shape
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faces = detector(img, 1)
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print(faces)
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mask = np.zeros(img.shape[:2], np.uint8)
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bgdModel = np.zeros((1, 65), np.float64)
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fgdModel = np.zeros((1, 65), np.float64)
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if (len(faces) > 0):
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for k, d in enumerate(faces):
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left = max(int((3 * d.left() - d.right()) / 2), 1)
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top = max(int((3 * d.top() - d.bottom()) / 2) - 50, 1)
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right = min(int((3 * d.right() - d.left()) / 2), t[1])
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bottom = min(int((3 * d.bottom() - d.top()) / 2), t[0])
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rect = (left, top, right, bottom)
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rect_reg = (d.left(), d.top(), d.right(), d.bottom())
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# shape = landmark_predictor(img, d)
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shape = landmark_predictor(img, d)
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print(shape)
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else:
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exit(0)
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# mask会保存明显的背景像素为0，明显的前景像素为1，可能的背景像素为2，可能的前景像素为3
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cv2.grabCut(img, mask, rect, bgdModel, fgdModel, 100,
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cv2.GC_INIT_WITH_RECT) # 函数返回值为mask,bgdModel,fgdModel
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# 利用mask做模板
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mask2 = np.where((mask == 2) | (mask == 0), 0, 1).astype('uint8') # 0和2做背景
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# 背景颜色
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bg_color = [(225, 166, 23), (0, 0, 255), (255, 255, 255)]
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img = img * mask2[:, :, np.newaxis] # 使用蒙板来获取前景区域
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erode = cv2.erode(img, None, iterations=1) # 腐蚀操作
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dilate = cv2.dilate(erode, None, iterations=1) # 膨胀操作
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for i in range(t[0]): # 高、
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for j in range(t[1]):
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if max(dilate[i, j]) <= 0:
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dilate[i, j] = bg_color[type]
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img = img[rect[1]:rect[3], rect[0]:rect[2]]
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dilate = dilate[rect[1]:rect[3], rect[0]:rect[2]]
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output_im = cv2.resize(dilate, (361, 381))
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return output_im |
