diff --git a/Class.zip b/Class.zip new file mode 100644 index 0000000..8e42720 Binary files /dev/null and b/Class.zip differ diff --git a/Class/collect_data/face_reco_from_camera.py b/Class/collect_data/face_reco_from_camera.py index a602d14..05ad724 100644 --- a/Class/collect_data/face_reco_from_camera.py +++ b/Class/collect_data/face_reco_from_camera.py @@ -8,6 +8,7 @@ import os import time from PIL import Image, ImageDraw, ImageFont +start_time = 0 # 1. Dlib 正向人脸检测器 # detector = dlib.get_frontal_face_detector() @@ -75,11 +76,9 @@ class Face_Recognizer: def return_euclidean_distance(feature_1, feature_2): feature_1 = np.array(feature_1) feature_2 = np.array(feature_2) - dist = np.sqrt(np.sum(np.square(feature_1 - feature_2))) + dist = np.sqrt(np.sum((feature_1 - feature_2) ** 2)) return dist - # TODO th - # 更新 FPS def update_fps(self): now = time.time() @@ -111,7 +110,7 @@ class Face_Recognizer: # TODO 数据库 ID # Default known name: person_1, person_2, person_3 self.name_known_list[0] = '唐麒'.encode('utf-8').decode() - # self.name_known_list[1] ='李四'.encode('utf-8').decode() + self.name_known_list[1] = '段海燕'.encode('utf-8').decode() # self.name_known_list[2] ='xx'.encode('utf-8').decode() # self.name_known_list[3] ='xx'.encode('utf-8').decode() # self.name_known_list[4] ='xx'.encode('utf-8').decode() @@ -122,6 +121,7 @@ class Face_Recognizer: if self.get_face_database(): while stream.isOpened(): flag, img_rd = stream.read() + img_with_name = img_rd kk = cv2.waitKey(1) # 按下 q 键退出 if kk == ord('q'): @@ -164,7 +164,8 @@ class Face_Recognizer: # greater than the minimum confidence if confidence < 0.5: continue - print("##### camera person", k + 1, "#####") + self.faces_cnt+=1 + # print("##### camera person", k + 1, "#####") # 让人名跟随在矩形框的上方 # 确定人名的位置坐标 # 先默认所有人不认识，是 unknown @@ -175,30 +176,31 @@ class Face_Recognizer: box = faces[0, 0, k, 3:7] * np.array([w, h, w, h]) (startX, startY, endX, endY) = box.astype("int") self.pos_camera_list.append(tuple( - [int(startX+5), int(startY - 30)])) + [int(startX + 5), int(startY - 30)])) # 5. 对于某张人脸，遍历所有存储的人脸特征 e_distance_list = [] for i in range(len(self.features_known_list)): # 如果 person_X 数据不为空 if str(self.features_known_list[i][0]) != '0.0': - print("with person", str(i + 1), "the e distance: ", end='') + # print("with person", str(i + 1), "the e distance: ", end='') e_distance_tmp = self.return_euclidean_distance(self.features_camera_list[k], self.features_known_list[i]) - print(e_distance_tmp) + # print(e_distance_tmp) e_distance_list.append(e_distance_tmp) else: # 空数据 person_X e_distance_list.append(999999999) # 6. 寻找出最小的欧式距离匹配 similar_person_num = e_distance_list.index(min(e_distance_list)) - print("Minimum e distance with person", self.name_known_list[similar_person_num]) + # print("Minimum e distance with person", self.name_known_list[similar_person_num]) - if min(e_distance_list) < 0.4: + if min(e_distance_list) < 1: self.name_camera_list[k] = self.name_known_list[similar_person_num] - print("May be person " + str(self.name_known_list[similar_person_num])) + # print("May be person " + str(self.name_known_list[similar_person_num])) else: - print("Unknown person") + pass + # print("Unknown person") # 矩形框 for kk, d in enumerate(faces): @@ -207,23 +209,24 @@ class Face_Recognizer: (0, 255, 0), 2) cv2.rectangle(img_rd, tuple([startX, startY - 35]), tuple([endX, startY]), (0, 255, 0), cv2.FILLED) - print('\n') - - self.faces_cnt = len(faces) - # 7. 在这里更改显示的人名 - self.modify_name_camera_list() - # 8. 写名字 - # self.draw_name(img_rd) - img_with_name = self.draw_name(img_rd) + # print('\n') + # self.faces_cnt = faces.shape[2] + # if len(self.name_camera_list) > 0: + # 7. 在这里更改显示的人名 + self.modify_name_camera_list() + # 8. 写名字 + # self.draw_name(img_rd) + img_with_name = self.draw_name(img_rd) else: img_with_name = img_rd - print("Faces in camera now:", self.name_camera_list, "\n") + # print("Faces in camera now:", self.name_camera_list, "\n") - cv2.imshow("camera", img_with_name) + if len(img_with_name): + cv2.imshow("camera", img_with_name) # 9. 更新 FPS / Update stream FPS - self.update_fps() + # self.update_fps() # OpenCV 调用摄像头并进行 process def run(self): diff --git a/Class/collect_data/get_faces_from_camera.py b/Class/collect_data/get_faces_from_camera.py index f6c8ebb..c24195a 100644 --- a/Class/collect_data/get_faces_from_camera.py +++ b/Class/collect_data/get_faces_from_camera.py @@ -7,7 +7,15 @@ import cv2 import os import shutil # 读写文件 import time -import face_recognition + +from PIL import ImageDraw, ImageFont +from PIL import Image + +action_list = ['look_ahead', 'look_left', 'look_right', 'rise_head', 'bow_head', 'blink', 'open_mouth', 'smile', 'over'] +action_map = {'look_ahead': '请看前方', 'blink': '请眨眼', 'open_mouth': '请张嘴', + 'smile': '请笑一笑', 'rise_head': '请抬头', + 'bow_head': '请低头', 'look_left': '请看左边', + 'look_right': '请看右边', 'over': '录入完成'} # Dlib 正向人脸检测器 # detector = dlib.get_frontal_face_detector() @@ -30,6 +38,8 @@ class Face_Register: self.save_flag = 1 # 之后用来检查是否先按 'n' 再按 's'，即先新建文件夹再保存 self.press_n_flag = 0 + # 之后用来提示动作的计数器 + self.index = 0 self.frame_time = 0 self.frame_start_time = 0 @@ -76,6 +86,8 @@ class Face_Register: # 生成的 cv2 window 上面添加说明文字 def draw_note(self, img_rd): # 添加说明 + # cv2.putText(img_rd, action_map[action_list[index]].encode('utf-8').decode(), (20, 250), self.font, 1, + # (0, 255, 0), 1, cv2.LINE_AA) cv2.putText(img_rd, "Face Register", (20, 40), self.font, 1, (255, 255, 255), 1, cv2.LINE_AA) cv2.putText(img_rd, "FPS: " + str(self.fps.__round__(2)), (20, 100), self.font, 0.8, (0, 255, 0), 1, cv2.LINE_AA) @@ -84,6 +96,18 @@ class Face_Register: cv2.putText(img_rd, "S: Save current face", (20, 400), self.font, 0.8, (255, 255, 255), 1, cv2.LINE_AA) cv2.putText(img_rd, "Q: Quit", (20, 450), self.font, 0.8, (255, 255, 255), 1, cv2.LINE_AA) + font = ImageFont.truetype("simsun.ttc", 30, index=1) + img_rd = Image.fromarray(cv2.cvtColor(img_rd, cv2.COLOR_BGR2RGB)) + draw = ImageDraw.Draw(img_rd) + if self.index <= 7: + draw.text((20, 230), text=action_map[action_list[self.index]].encode('utf-8').decode(), font=font, + fill=(0, 255, 0)) + else: + draw.text((20, 230), text=action_map[action_list[8]].encode('utf-8').decode(), font=font, + fill=(0, 255, 0)) + img_rd = cv2.cvtColor(np.array(img_rd), cv2.COLOR_RGB2BGR) + return img_rd + # 获取人脸 def process(self, stream): # 1. 新建储存人脸图像文件目录 @@ -96,6 +120,7 @@ class Face_Register: self.check_existing_faces_cnt() while stream.isOpened(): + self.faces_cnt = 0 flag, img_rd = stream.read() # Get camera video stream kk = cv2.waitKey(1) @@ -115,6 +140,7 @@ class Face_Register: print("新建的人脸文件夹 / Create folders: ", current_face_dir) self.ss_cnt = 0 # 将人脸计数器清零 + self.index = 0 self.press_n_flag = 1 # 已经按下 'n' # 5. 检测到人脸 @@ -129,6 +155,8 @@ class Face_Register: if confidence < 0.5: continue + self.faces_cnt += 1 + # compute the (x, y)-coordinates of the bounding box for the # object box = faces[0, 0, i, 3:7] * np.array([w, h, w, h]) @@ -165,18 +193,20 @@ class Face_Register: # 检查有没有先按'n'新建文件夹 if self.press_n_flag: self.ss_cnt += 1 - for ii in range(height * 2): - for jj in range(width * 2): - img_blank[ii][jj] = img_rd[startY - hh + ii][startX - ww + jj] - cv2.imwrite(current_face_dir + "/img_face_" + str(self.ss_cnt) + ".jpg", img_blank) - print("写入本地 / Save into：", - str(current_face_dir) + "/img_face_" + str(self.ss_cnt) + ".jpg") + if self.index<=7: + for ii in range(height * 2): + for jj in range(width * 2): + img_blank[ii][jj] = img_rd[startY - hh + ii][startX - ww + jj] + cv2.imwrite(current_face_dir + "/img_face_" + str(self.ss_cnt) + ".jpg", img_blank) + print("写入本地 / Save into：", + str(current_face_dir) + "/img_face_" + str(self.ss_cnt) + ".jpg") + self.index += 1 else: print("请先按 'N' 来建文件夹, 按 'S' / Please press 'N' and press 'S'") - self.faces_cnt = len(faces) + # self.faces_cnt = len(faces) # 9. 生成的窗口添加说明文字 - self.draw_note(img_rd) + img_rd = self.draw_note(img_rd) # 10. 按下 'q' 键退出 if kk == ord('q'): @@ -200,4 +230,4 @@ def main(): if __name__ == '__main__': - main() \ No newline at end of file + main() diff --git a/Class/detection/algorithm_fall.py b/Class/detection/algorithm_fall.py index 9995da9..b7b3cc7 100644 --- a/Class/detection/algorithm_fall.py +++ b/Class/detection/algorithm_fall.py @@ -6,44 +6,45 @@ import numpy as np import statistics import queue import math +from PIL import ImageDraw, ImageFont +from PIL import Image + - def fall_detect(cnts, defined_min_area, frame, prevX, prevY, xList, yList, centerV, alert): - for c in cnts: - #exclusion + # exclusion if cv2.contourArea(c) < defined_min_area: continue - + # outer bounding box (x_b, y_b, w_b, h_b) = cv2.boundingRect(c) - cv2.rectangle(frame, (x_b, y_b), (x_b + w_b, y_b + h_b), (0, 255, 255), 2) # 黄色矩形 + cv2.rectangle(frame, (x_b, y_b), (x_b + w_b, y_b + h_b), (0, 255, 255), 2) # 黄色矩形 - #rotating bounding box - rect = cv2.minAreaRect(c) # 得到最小外接矩形的（中心(x,y), (宽,高), 旋转角度） - box = cv2.boxPoints(rect) # 获取最小外接矩形的4个顶点坐标 + # rotating bounding box + rect = cv2.minAreaRect(c) # 得到最小外接矩形的（中心(x,y), (宽,高), 旋转角度） + box = cv2.boxPoints(rect) # 获取最小外接矩形的4个顶点坐标 box = np.int0(box) - cv2.drawContours(frame,[box],0,(0,0,255),2) + cv2.drawContours(frame, [box], 0, (0, 0, 255), 2) - #averaging line - rows,cols = frame.shape[:2] - [vx,vy,x,y] = cv2.fitLine(c, cv2.DIST_L2, 0, 0.01, 0.01) - lefty = (-x * vy/vx) + y - righty =((cols-x) * vy/vx)+y - cv2.line(frame,(cols-1,righty),(0,lefty),(255,0,0),2) + # averaging line + rows, cols = frame.shape[:2] + [vx, vy, x, y] = cv2.fitLine(c, cv2.DIST_L2, 0, 0.01, 0.01) + lefty = (-x * vy / vx) + y + righty = ((cols - x) * vy / vx) + y + cv2.line(frame, (cols - 1, righty), (0, lefty), (255, 0, 0), 2) - #ellipse + # ellipse elps = cv2.fitEllipse(c) (x, y), (MA, ma), angle = cv2.fitEllipse(c) - cv2.ellipse(frame, elps,(255,0,0),3) #red + cv2.ellipse(frame, elps, (255, 0, 0), 3) # red - #Aspect Ratio - AR = MA/ma + # Aspect Ratio + AR = MA / ma - #Center Speed - acceleration + # Center Speed - acceleration prevX = 0.0 prevY = 0.0 - centerSpeed =0 + centerSpeed = 0 if xList.full(): prevX = statistics.median(list(xList.queue)) prevY = statistics.median(list(yList.queue)) @@ -54,49 +55,57 @@ def fall_detect(cnts, defined_min_area, frame, prevX, prevY, xList, yList, cente yList.put(elps[0][1]) X = statistics.median(list(xList.queue)) Y = statistics.median(list(yList.queue)) - - if xList.full(): - dx = abs(prevX-X) - dy = abs(prevY-Y) - centerV = math.sqrt(dx**2+dy**2) - - # calculate probabilities for the 4 features - pAngle = (abs(angle-90)-50)/10 - pAngle = 1 / (math.exp(pAngle)+1) - - pAR = 10*AR - 5 - pAR = 1 / (math.exp(pAR) + 1) - - ACS = centerV - 9 - ACS = 1 / (math.exp(ACS) + 1) + if xList.full(): + dx = abs(prevX - X) + dy = abs(prevY - Y) + centerV = math.sqrt(dx ** 2 + dy ** 2) + + # calculate probabilities for the 4 features + pAngle = (abs(angle - 90) - 50) / 10 + pAngle = 1 / (math.exp(pAngle) + 1) + + pAR = 10 * AR - 5 + pAR = 1 / (math.exp(pAR) + 1) + + ACS = centerV - 9 + try: + ACS = 1 / (math.exp(ACS) + 1) + except: + ACS = 1 / (float('inf') + 1) # print("pAngle : ", pAngle) # print("pAR : ", pAR) # print("ACS : ", ACS) - - #confidence + + # confidence P_FALL = pAngle * pAR * ACS + 0.5 # print("P_FALL1 : ", P_FALL) - - P_FALL = 1/ (math.exp(-(P_FALL-0.65)*10)+1) + + P_FALL = 1 / (math.exp(-(P_FALL - 0.65) * 10) + 1) # print("P_FALL2: ", P_FALL) - #status display - cv2.putText(frame, "Status : ", (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2) - cv2.putText(frame, "Fall Confidence: {0:.2f} ".format(P_FALL), (10,50),cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 128, 255), 2) + # status display + # cv2.putText(frame, "Status : ", (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2) + # cv2.putText(frame, "Fall Confidence: {0:.2f} ".format(P_FALL), (10, 50), cv2.FONT_HERSHEY_SIMPLEX, 0.5, + # (0, 128, 255), 2) # cv2.putText(frame, "Angle: {0:.2f}".format(angle), (10, 220),cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 2) # cv2.putText(frame, "AR: {0:.2f}".format(AR), (10, 237),cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 2) # cv2.putText(frame, "Center Speed: {0:.2f}".format(centerV), (10, 256),cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 2) - #fall + # fall if P_FALL > 0.88: - if alert >= 8: - print("fall") - cv2.putText(frame, " Fall Detected", (82, 20),cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2) + if alert >3: + # print("fall") + font = ImageFont.truetype("simsun.ttc", 30, index=1) + img_rd = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)) + draw = ImageDraw.Draw(img_rd) + draw.text((10, 10), text="Fall Detected", font=font, + fill=(255, 0, 0)) + frame = cv2.cvtColor(np.array(img_rd), cv2.COLOR_RGB2BGR) # cv2.imwrite("report.jpg", frame) # send_alert.SendMail("report.jpg") alert = alert + 1 else: alert = alert + 1 - return alert + return frame,alert diff --git a/Class/detection/check_smile_from_camera.py b/Class/detection/check_smile_from_camera.py new file mode 100644 index 0000000..325a1d0 --- /dev/null +++ b/Class/detection/check_smile_from_camera.py @@ -0,0 +1,110 @@ +from sklearn.externals import joblib + +import ML_ways_sklearn + +import dlib +import numpy as np +import cv2 + + +detector = dlib.get_frontal_face_detector() +# OpenCV DNN face detector +# detector = cv2.dnn.readNetFromCaffe("data/data_opencv/deploy.prototxt.txt", +# "data/data_opencv/res10_300x300_ssd_iter_140000.caffemodel") +predictor = dlib.shape_predictor('data/data_dlib_model/shape_predictor_68_face_landmarks.dat') + +# OpenCV 调用摄像头 +cap = cv2.VideoCapture(0) + +# 设置视频参数 +cap.set(3, 480) + + +def get_features(img_rd): + + # 输入: img_rd: 图像文件 + # 输出: positions_lip_arr: feature point 49 to feature point 68, 20 feature points / 40D in all + + # 取灰度 + img_gray = cv2.cvtColor(img_rd, cv2.COLOR_RGB2GRAY) + + # 计算68点坐标 + positions_68_arr = [] + faces = detector(img_gray, 0) + landmarks = np.matrix([[p.x, p.y] for p in predictor(img_rd, faces[0]).parts()]) + + for idx, point in enumerate(landmarks): + # 68点的坐标 + pos = (point[0, 0], point[0, 1]) + positions_68_arr.append(pos) + + positions_lip_arr = [] + # 将点 49-68 写入 CSV + # 即 positions_68_arr[48]-positions_68_arr[67] + for i in range(48, 68): + positions_lip_arr.append(positions_68_arr[i][0]) + positions_lip_arr.append(positions_68_arr[i][1]) + + return positions_lip_arr + + +while cap.isOpened(): + # 480 height * 640 width + flag, img_rd = cap.read() + kk = cv2.waitKey(1) + + img_gray = cv2.cvtColor(img_rd, cv2.COLOR_RGB2GRAY) + + # 人脸数 faces + faces = detector(img_gray, 0) + # 检测到人脸 + if len(faces) != 0: + # 提取单张40维度特征 + positions_lip_test = get_features(img_rd) + + # path of models + path_models = "data/data_models/" + + # ######### LR ########### + LR = joblib.load(path_models+"model_LR.m") + ss_LR = ML_ways_sklearn.model_LR() + X_test_LR = ss_LR.transform([positions_lip_test]) + y_predict_LR = str(LR.predict(X_test_LR)[0]).replace('0', "no smile").replace('1', "with smile") + print("LR:", y_predict_LR) + + # ######### LSVC ########### + LSVC = joblib.load(path_models+"model_LSVC.m") + ss_LSVC = ML_ways_sklearn.model_LSVC() + X_test_LSVC = ss_LSVC.transform([positions_lip_test]) + y_predict_LSVC = str(LSVC.predict(X_test_LSVC)[0]).replace('0', "no smile").replace('1', "with smile") + print("LSVC:", y_predict_LSVC) + + # ######### MLPC ########### + MLPC = joblib.load(path_models+"model_MLPC.m") + ss_MLPC = ML_ways_sklearn.model_MLPC() + X_test_MLPC = ss_MLPC.transform([positions_lip_test]) + y_predict_MLPC = str(MLPC.predict(X_test_MLPC)[0]).replace('0', "no smile").replace('1', "with smile") + print("MLPC:", y_predict_MLPC) + + # ######### SGDC ########### + SGDC = joblib.load(path_models+"model_SGDC.m") + ss_SGDC = ML_ways_sklearn.model_SGDC() + X_test_SGDC = ss_SGDC.transform([positions_lip_test]) + y_predict_SGDC = str(SGDC.predict(X_test_SGDC)[0]).replace('0', "no smile").replace('1', "with smile") + print("SGDC:", y_predict_SGDC) + + print('\n') + + # 按下 'q' 键退出 + if kk == ord('q'): + break + + # 窗口显示 + # cv2.namedWindow("camera", 0) # 如果需要摄像头窗口大小可调 + cv2.imshow("camera", img_rd) + +# 释放摄像头 +cap.release() + +# 删除建立的窗口 +cv2.destroyAllWindows() \ No newline at end of file diff --git a/Class/detection/fall_detection.py b/Class/detection/fall_detection.py index 1cb2a50..dedd033 100644 --- a/Class/detection/fall_detection.py +++ b/Class/detection/fall_detection.py @@ -9,6 +9,9 @@ from sys import platform import argparse import numpy as np +from PIL import ImageDraw, ImageFont +from PIL import Image + from imutils.video import VideoStream import datetime import imutils @@ -39,7 +42,7 @@ try: raise e # cap = cv2.VideoCapture(0) - cap = cv2.VideoCapture("demo1.mp4") + cap = cv2.VideoCapture("test4.mp4") _, frame = cap.read() cv2.imwrite('fall_detection.jpg', frame) @@ -113,6 +116,7 @@ try: imageToProcess = cv2.imread(args[0].image_path) datum.cvInputData = imageToProcess opWrapper.emplaceAndPop([datum]) + img_rd = datum.cvOutputData # Display Image # print("Body keypoints: \n" + str(datum.poseKeypoints)) @@ -147,7 +151,7 @@ try: np.array(height0))): couter += 1 # print("alarm by v and a") - elif (width > height and (x[8] != 0 or x[9] != 0 or x[12] != 0)): + elif (width > height and (x[8] != 0 or x[9] != 0 or x[12] != 0) and v < 0.41): couter += 1 # print("alarm by w and h") else: @@ -157,8 +161,15 @@ try: couter = 0 error = 0 - if couter > 2: - print("alarm") + if couter > 3: + font = ImageFont.truetype("simsun.ttc", 30, index=1) + img_rd = Image.fromarray(cv2.cvtColor(datum.cvOutputData, cv2.COLOR_BGR2RGB)) + draw = ImageDraw.Draw(img_rd) + draw.text((10, 10), text="Fall Detected", font=font, + fill=(255, 0, 0)) + img_rd = cv2.cvtColor(np.array(img_rd), cv2.COLOR_RGB2BGR) + + # print("fall") # update variables frame_start_time = now @@ -183,8 +194,8 @@ try: cnts = frame_process.get_contours(firstFrame, gray) defined_min_area = 3000 - alert = algorithm_fall.fall_detect(cnts, defined_min_area, frame, prevX, prevY, xList, yList, - centerV, alert) + frame, alert = algorithm_fall.fall_detect(cnts, defined_min_area, frame, prevX, prevY, xList, yList, + centerV, alert) # cv2.putText(frame, datetime.datetime.now().strftime("%A %d %B %Y %I:%M:%S%p"), # (10, frame.shape[0] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.35, (255, 255, 255), 1) @@ -192,9 +203,8 @@ try: cv2.imshow("OpenPose 1.6.0 - Tutorial Python API", frame) continue - cv2.imshow("OpenPose 1.6.0 - Tutorial Python API", datum.cvOutputData) + cv2.imshow("OpenPose 1.6.0 - Tutorial Python API", img_rd) cap.release() cv2.destroyAllWindows() except Exception as e: print(e) - sys.exit(-1)