基于FaceNet和MTCNN可在win10上可视化界面的人脸识别3.1版app

from tensorflow.keras.layers import Conv2D, Input, MaxPool2D, Flatten, Dense, Permute from tensorflow.keras.layers import PReLU from tensorflow.keras.models import Model import numpy as np import cv2 # -----------------------------# # 计算原始输入图像 # 每一次缩放的比例 # -----------------------------# def calculateScales(img): copy_img = img.copy() pr_scale = 1.0 # 获取原始图像宽高 h, w, _ = copy_img.shape # 计算调整后的图像宽高 if min(w, h) > 500: pr_scale = 500.0 / min(h, w) w = int(w * pr_scale) h = int(h * pr_scale) elif max(w, h) < 500: pr_scale = 500.0 / max(h, w) w = int(w * pr_scale) h = int(h * pr_scale) scales = [] factor = 0.709 factor_count = 0 minl = min(h, w) # 计算缩放比例 while minl >= 12: scales.append(pr_scale * pow(factor, factor_count)) minl *= factor factor_count += 1 return scales # -------------------------------------# # 对pnet处理后的结果进行处理 # -------------------------------------# def detect_face_12net(cls_prob, roi, out_side, scale, width, height, threshold): cls_prob = np.swapaxes(cls_prob, 0, 1) # 获取预测人脸置信度得分 roi = np.swapaxes(roi, 0, 2) stride = 0 # stride略等于2 if out_side != 1: stride = float(2 * out_side - 1) / (out_side - 1) # 获取置信度得分大于阈值的人脸框位置 (x, y) = np.where(cls_prob >= threshold) boundingbox = np.array([x, y]).T # 找到对应原图的位置 bb1 = np.fix((stride * (boundingbox) + 0) * scale) bb2 = np.fix((stride * (boundingbox) + 11) * scale) boundingbox = np.concatenate((bb1, bb2), axis=1) # 获取人脸框位置调整量 dx1 = roi[0][x, y] dx2 = roi[1][x, y] dx3 = roi[2][x, y] dx4 = roi[3][x, y] # 将置信度得分和人脸框位置调整量转成ndarray score = np.array([cls_prob[x, y]]).T offset = np.array([dx1, dx2, dx3, dx4]).T # 获取人脸框在原始图像中位置 boundingbox = boundingbox + offset * 12.0 * scale rectangles = np.concatenate((boundingbox, score), axis=1) # 转化成正方形 rectangles = rect2square(rectangles) pick = [] # 遍历人脸框，防止人脸框越界 for i in range(len(rectangles)): x1 = int(max(0, rectangles[i][0])) y1 = int(max(0, rectangles[i][1])) x2 = int(min(width, rectangles[i][2])) y2 = int(min(height, rectangles[i][3])) sc = rectangles[i][4] if x2 > x1 and y2 > y1: pick.append([x1, y1, x2, y2, sc]) # 对人脸框做非极大值抑制处理 return NMS(pick, 0.3) # -----------------------------# # 将长方形调整为正方形 # -----------------------------# def rect2square(rectangles): # 计算人脸框宽和高 # print(rectangles.shape) w = rectangles[:, 2] - rectangles[:, 0] h = rectangles[:, 3] - rectangles[:, 1] # 计算宽高中的最大值 l = np.maximum(w, h).T # 调整人脸框为正方形 rectangles[:, 0] = rectangles[:, 0] + w * 0.5 - l * 0.5 rectangles[:, 1] = rectangles[:, 1] + h * 0.5 - l * 0.5 rectangles[:, 2:4] = rectangles[:, 0:2] + np.repeat([l], 2, axis=0).T return rectangles # -------------------------------------# # 非极大抑制 # -------------------------------------# def NMS(rectangles, threshold): # 如果无框则返回 if len(rectangles) == 0: return rectangles # 将人脸框转ndarray boxes = np.array(rectangles) # 获取人脸框坐标 x1 = boxes[:, 0] y1 = boxes[:, 1] x2 = boxes[:, 2] y2 = boxes[:, 3] # 获取人脸框置信度 s = boxes[:, 4] # 计算人脸框面积 area = np.multiply(x2 - x1 + 1, y2 - y1 + 1) # 对人脸框置信度得分排序 I = np.array(s.argsort()) pick = [] while len(I) > 0: # 计算置信度得分最高的人脸框x1坐标与其他框的x1坐标最大值 xx1 = np.maximum(x1[I[-1]], x1[I[0:-1]]) # 计算置信度得分最高的人脸框y1坐标与其他框的y1坐标最大值 yy1 = np.maximum(y1[I[-1]], y1[I[0:-1]]) # 计算置信度得分最高的人脸框x2坐标与其他框的x2坐标最小值 xx2 = np.minimum(x2[I[-1]], x2[I[0:-1]]) # 计算置信度得分最高的人脸框y2坐标与其他框的y2坐标最小值 yy2 = np.minimum(y2[I[-1]], y2[I[0:-1]]) # 计算重合区域宽高 w = np.maximum(0.0, xx2 - xx1 + 1) h = np.maximum(0.0, yy2 - yy1 + 1) # 计算重合区域面积 inter = w * h # 计算交并比IOU o = inter / (area[I[-1]] + area[I[0:-1]] - inter) pick.append(I[-1]) I = I[np.where(o <= threshold)[0]] result_rectangle = boxes[pick].tolist() return result_rectangle # -------------------------------------# # 对pnet处理后的结果进行处理 # -------------------------------------# def filter_face_24net(cls_prob, roi, rectangles, width, height, threshold): prob = cls_prob[:, 1] # 获取预测人脸置信度得分 pick = np.where(prob >= threshold) # 获取置信度得分大于阈值的索引 # 人脸框转ndarray rectangles = np.array(rectangles) # 获取置信度大于阈值的人脸框 x1 = rectangles[pick, 0] y1 = rectangles[pick, 1] x2 = rectangles[pick, 2] y2 = rectangles[pick, 3] # 获取置信度得分大于阈值的置信度得分 sc = np.array([prob[pick]]).T # 获取人脸框偏移量 dx1 = roi[pick, 0] dx2 = roi[pick, 1] dx3 = roi[pick, 2] dx4 = roi[pick, 3] # 计算人脸框宽高 w = x2 - x1 h = y2 - y1 # 计算人脸框在原图上的位置 x1 = np.array([(x1 + dx1 * w)[0]]).T y1 = np.array([(y1 + dx2 * h)[0]]).T x2 = np.array([(x2 + dx3 * w)[0]]).T y2 = np.array([(y2 + dx4 * h)[0]]).T rectangles = np.concatenate((x1, y1, x2, y2, sc), axis=1) # 人脸框转成正方形 rectangles = rect2square(rectangles) pick = [] # 遍历人脸框，防止人脸框越界 for i in range(len(rectangles)): x1 = int(max(0, rectangles[i][0])) y1 = int(max(0, rectangles[i][1])) x2 = int(min(width, rectangles[i][2])) y2 = int(min(height, rectangles[i][3])) sc = rectangles[i][4] if x2 > x1 and y2 > y1: pick.append([x1, y1, x2, y2, sc]) # 对人脸框做非极大值抑制处理 return NMS(pick, 0.3) # -------------------------------------# # 对onet处理后的结果进行处理 # -------------------------------------# def filter_face_48net(cls_prob, roi, pts, rectangles, width, height, threshold): prob = cls_prob[:, 1] # 获取预测人脸置信度得分 pick = np.where(prob >= threshold) # 获取置信度得分大于阈值的索引 rectangles = np.array(rectangles) # 人脸框转ndarray # 获取置信度大于阈值的人脸框 x1 = rectangles[pick, 0] y1 = rectangles[pick, 1] x2 = rectangles[pick, 2] y2 = rectangles[pick, 3] # 获取置信度得分大于阈值的置信度得分 sc = np.array([prob[pick]]).T # 获取人脸框偏移量 dx1 = roi[pick, 0] dx2 = roi[pick, 1] dx3 = roi[pick, 2] dx4 = roi[pick, 3] # 计算人脸框宽高 w = x2 - x1 h = y2 - y1 # 计算人脸关键点在原图中的位置 pts0 = np.array([(w * pts[pick, 0] + x1)[0]]).T pts1 = np.array([(h * pts[pick, 5] + y1)[0]]).T pts2 = np.array([(w * pts[pick, 1] + x1)[0]]).T pts3 = np.array([(h * pts[pick, 6] + y1)[0]]).T pts4 = np.array([(w * pts[pick, 2] + x1)[0]]).T pt