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