from __future__ import division import time from flirpy.camera.boson import Boson # import torch # import torch.nn as nn # from torch.autograd import Variable import numpy as np import cv2 from matplotlib import pyplot as plt from util import * from darknet import Darknet from preprocess import prep_image, inp_to_image, letterbox_image # import pandas as pd # import random import pickle as pkl import argparse import dlib def get_test_input(input_dim, CUDA): img = cv2.imread("data/dog-cycle-car.png") img = cv2.resize(img, (input_dim, input_dim)) img_ = img[:,:,::-1].transpose((2,0,1)) img_ = img_[np.newaxis,:,:,:]/255.0 img_ = torch.from_numpy(img_).float() img_ = Variable(img_) if CUDA: img_ = img_.cuda() return img_ def prep_image(img, inp_dim): """ Prepare image for inputting to the neural network.Returns a Variable """ orig_im = img dim = orig_im.shape[1], orig_im.shape[0] img = (letterbox_image(orig_im, (inp_dim, inp_dim))) img_ = img[:,:,::-1].transpose((2,0,1)).copy() img_ = torch.from_numpy(img_).float().div(255.0).unsqueeze(0) return img_, orig_im, dim def write(x, img): c1 = tuple(x[1:3].int()) c2 = tuple(x[3:5].int()) cls = int(x[-1]) label = "{0}".format(classes[cls]) color = (0, 0, 255)#random.choice(colors) if (label=='person'): # cv2.line(img, tuple((c1[0]+(c2[0]-c1[0])/2, c1[1] + (c2[1]-c1[1])/2)), c2, (255, 0, 0), 5) # print((c2[1]-c1[1])/2) # cv2.rectangle(img, (c1[0], c1[1] + int((c2[1]-c1[1])/2.5)-5), (c2[0],c2[1]), (225,0,0), 3) cropped_img=img[c1[1]+2:c2[1],c1[0]:c2[0]-2] if len(cropped_img): get_rgb_Hist(cropped_img) # get_nose_position(face_img) cv2.rectangle(img, c1, c2, color, 2) t_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_PLAIN, 1 , 1)[0] c2 = c1[0] + t_size[0] + 3, c1[1] + t_size[1] + 4 # cv2.rectangle(img, c1, c2,color, -1) # cv2.putText(img, label, (c1[0], c1[1] + t_size[1] + 4), cv2.FONT_HERSHEY_PLAIN, 1, [225,255,255], 1); return img log_array = np.zeros(15) def get_rgb_Hist(nose_img): pass # gray = cv2.cvtColor(nose_img, cv2.COLOR_BGR2GRAY) # cv2.imshow("Nose",gray) # hist = cv2.calcHist([gray], [0], None, [256], [0, 256]) # plt.plot(hist) # plt.xlim([0, 256]) # cv2.imshow("Nose", nose_img) # # plt.subplot(231) # histr_b = cv2.calcHist([nose_img], [0], None, [256], [0, 256]) # plt.plot(histr_b, color='b') # plt.xlim([0, 256]) # plt.ylim([0, 80]) # # plt.subplot(232) # histr_g = cv2.calcHist([nose_img], [1], None, [256], [0, 256]) # plt.plot(histr_g, color='g') # plt.xlim([0, 20]) # plt.ylim([0, 80]) # # plt.subplot(233) # histr_r = cv2.calcHist([nose_img], [2], None, [256], [0, 256]) # plt.plot(histr_r, color='r') # plt.xlim([0, 256]) # plt.ylim([0, 80]) # # plt.subplot(234) # color = ['blue', 'green', 'red'] # bgr_mean = [np.average(nose_img[0]), np.average(nose_img[1]), np.average(nose_img[2])] # plt.bar(color, bgr_mean) # plt.ylim([100, 170]) # # plt.subplot(235) # title = ['Green Hist(0 to 20) avg'] # avg_hist=np.average(histr_g[0:20]) # plt.bar(title, avg_hist) # plt.ylim([0, 6]) # # plt.subplot(236) # indicator = np.zeros((50,100)) # if avg_hist>2: # cv2.putText(indicator,'Inhailing', (0,25),cv2.FONT_HERSHEY_COMPLEX,0.6,(255,0,0)) # else: # cv2.putText(indicator,'Exhaling', (0,25),cv2.FONT_HERSHEY_COMPLEX,0.6,(255,0,0)) # plt.imshow(indicator) # # plt.draw() # plt.pause(0.0011) # plt.clf() detector = dlib.get_frontal_face_detector() predictor = dlib.shape_predictor("data/shape_predictor_68_face_landmarks.dat") def get_nose_position(face): ######################## # Facial LandMark Detector face_img = cv2.cvtColor(face, cv2.COLOR_BGR2GRAY) face_img = cv2.equalizeHist(face_img) faces = detector(face_img) for face in faces: x1 = face.left() y1 = face.top() x2 = face.right() y2 = face.bottom() # Then we can also do cv2.rectangle function (frame, (x1, y1), (x2, y2), (0, 255, 0), 3) landmarks = predictor(face_img, face) # We are then accesing the landmark points for n in range(0, 68): x = landmarks.part(n).x y = landmarks.part(n).y cv2.circle(face_img, (x, y), 2, (255, 255, 0), -1) ################# # fgMask = backSub.apply(img[c1[1]:c2[1],c1[0]:c2[0]]) if len(face_img): cv2.imshow("Face", face_img) def arg_parse(): parser = argparse.ArgumentParser(description='YOLO v3 Video Detection Module') parser.add_argument("--video", dest = 'video', help = "Video to run detection upon", default = "video.avi", type = str) parser.add_argument("--dataset", dest = "dataset", help = "Dataset on which the network has been trained", default = "pascal") parser.add_argument("--confidence", dest = "confidence", help = "Object Confidence to filter predictions", default = 0.5) parser.add_argument("--nms_thresh", dest = "nms_thresh", help = "NMS Threshhold", default = 0.4) parser.add_argument("--cfg", dest = 'cfgfile', help = "Config file", default = "cfg/yolov3-face.cfg", type = str)#cfg/yolov3.cfg parser.add_argument("--weights", dest = 'weightsfile', help = "weightsfile", default = "data/yolov3-wider_face_16000.weights", type = str)#yolov3.weights parser.add_argument("--reso", dest = 'reso', help = "Input resolution of the network. Increase to increase accuracy. Decrease to increase speed", default = "416", type = str) return parser.parse_args() if __name__ == '__main__': args = arg_parse() confidence = float(args.confidence) nms_thesh = float(args.nms_thresh) start = 0 CUDA = torch.cuda.is_available() num_classes = 80 CUDA = torch.cuda.is_available() bbox_attrs = 5 + num_classes # print("Loading Face Landmark detector.....") print("Loading network.....") model = Darknet(args.cfgfile) print(args.cfgfile) model.load_weights(args.weightsfile) print("Network successfully loaded") model.net_info["height"] = args.reso inp_dim = int(model.net_info["height"]) assert inp_dim % 32 == 0 assert inp_dim > 32 if CUDA: model.cuda() print("x") model(get_test_input(inp_dim, CUDA), CUDA) model.eval() videofile = args.video # cap = cv2.VideoCapture("vids/breathing_cap.mov")#'http://192.168.21.200:81/') # cap = cv2.VideoCapture(0) assert cap.isOpened(), 'Cannot capture source' frames = 0 start = time.time() while cap.isOpened(): ret, frame = cap.read() if ret: img, orig_im, dim = prep_image(frame, inp_dim) im_dim = torch.FloatTensor(dim).repeat(1,2) if CUDA: im_dim = im_dim.cuda() img = img.cuda() with torch.no_grad(): output = model(Variable(img), CUDA) output = write_results(output, confidence, num_classes, nms = True, nms_conf = nms_thesh) if type(output) == int: frames += 1 print("FPS of the video is {:5.2f}".format( frames / (time.time() - start))) cv2.imshow("frame", orig_im) key = cv2.waitKey(1) if key & 0xFF == ord('q'): break continue im_dim = im_dim.repeat(output.size(0), 1) scaling_factor = torch.min(inp_dim/im_dim,1)[0].view(-1,1) output[:,[1,3]] -= (inp_dim - scaling_factor*im_dim[:,0].view(-1,1))/2 output[:,[2,4]] -= (inp_dim - scaling_factor*im_dim[:,1].view(-1,1))/2 output[:,1:5] /= scaling_factor for i in range(output.shape[0]): output[i, [1,3]] = torch.clamp(output[i, [1,3]], 0.0, im_dim[i,0]) output[i, [2,4]] = torch.clamp(output[i, [2,4]], 0.0, im_dim[i,1]) classes = load_classes('data/coco.names') colors = pkl.load(open("data/pallete", "rb")) list(map(lambda x: write(x, orig_im), output)) cv2.imshow("frame", orig_im) key = cv2.waitKey(1) if key & 0xFF == ord('q'): break frames += 1 print("FPS of the video is {:5.2f}".format( frames / (time.time() - start))) else: break