327 lines
11 KiB
Text
327 lines
11 KiB
Text
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 pickle as pkl
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import argparse
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from scipy.signal import savgol_filter
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from pythonosc import udp_client
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from pythonosc import osc_server
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from pythonosc import dispatcher
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import threading
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# import sounddevice as sd
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import soundfile as sf
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from playsound import playsound
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client = udp_client.SimpleUDPClient('192.168.21.200', 10000 )
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# client1 = udp_client.SimpleUDPClient('192.168.21.200', 30000)
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client1 = udp_client.SimpleUDPClient('127.0.0.1', 15000)
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client_is_person_present = udp_client.SimpleUDPClient('192.168.21.200', 20000)
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# data, fs = sf.read('bell_short.wav', dtype='float32')
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# sd.play(data.T, fs, device=2)
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print("""
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Choose Object detection:
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Nose = 'n'
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Face = 'f'
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""")
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detection_model = input()
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if detection_model == 'f':
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weight_file = "data/yolov3-wider_face_16000.weights"
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cfg_file = "cfg/yolov3-face.cfg"
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print("Activating FACE Detection")
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else:
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weight_file = "data/yolov3_training_final.weights"
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cfg_file = "cfg/yolov3_pysource_testing.cfg"
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print("Activating NOSE Detection")
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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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prev_vals = []
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inhale_val = 0
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exhale_val = 0
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is_val_up = False
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is_exaling = False
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Is_person_present = 0
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def write(x, img, abs_image):
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global prev_vals, inhale_val, exhale_val, Is_person_present
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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 = "person"#"{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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if c1[0]>10 and c1[0]<600:
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client_is_person_present.send_message("/python/person", 1)
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else:
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client1.send_message("/sound", (0,0,0))
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if detection_model == 'f':
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cv2.rectangle(img, c1, c2, (0,0,0), 2)
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c1 = tuple([c1[0]+20, c1[1]+25])
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c2 = tuple([c2[0]-15, c2[1]-15])
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cropped_abs = abs_image[c1[1]:c2[1], c1[0]:c2[0]]
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cropped_abs = np.ndarray.flatten(cropped_abs)
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number_of_min_pixl = int(len(cropped_abs) * 0.1)
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idx = np.argpartition(cropped_abs, number_of_min_pixl)
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cv2.rectangle(img, c1, c2, color, 2)
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else:
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cropped_abs = abs_image[c1[1]:c2[1], c1[0]:c2[0]]
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cropped_abs = np.ndarray.flatten(cropped_abs)
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number_of_min_pixl = int(len(cropped_abs)*0.2)
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idx = np.argpartition(cropped_abs, number_of_min_pixl)
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cv2.rectangle(img, c1, c2,color, 2)
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if cropped_abs.any():
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# obs = np.min(cropped_abs)
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obs = np.mean(cropped_abs[idx[:number_of_min_pixl]])
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if len(prev_vals) >= 100:
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prev_vals = prev_vals[1:]
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prev_vals.append(obs)
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sav_filtered = savgol_filter(prev_vals, 11, 3)
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prev_vals_mean = np.ones(100) * np.mean(sav_filtered[40:])#((np.max(sav_filtered[40:]) - np.min(sav_filtered[40:]))/2 + np.min(sav_filtered[40:]))#
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# print(prev_vals_mean)
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if sav_filtered[-1] > prev_vals_mean[-1]:
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exhale_val = abs(sav_filtered[-1] - prev_vals_mean[-1])
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if exhale_val>0.1:
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inhale_val = 0
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# print('Exhaling')
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exhale_val = exhale_val/(160)
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client.send_message("/chan", (inhale_val, exhale_val))
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# if is_exaling == True:
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# sd.play(data, fs, device=1)
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# print(exhale_val)
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# ////////////// Sound
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exhale_val = exhale_val + 0.20
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if c1[0] > 10 and c1[0] < 600:
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client1.send_message("/sound", (1, inhale_val, exhale_val))
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else:
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client1.send_message("/sound", (0,inhale_val, exhale_val))
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# //////////////
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else:
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inhale_val = abs(prev_vals_mean[-1] - sav_filtered[-1])
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if inhale_val>0.1:
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exhale_val = 0
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# print('Inhaling')
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# ////////////Sound
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inhale_val = inhale_val/(160)
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if c1[0] > 10 and c1[0] < 600:
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client1.send_message("/sound", (1, inhale_val, exhale_val))
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else:
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client1.send_message("/sound", (0, inhale_val, exhale_val))
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# //////////////
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client.send_message("/chan", (inhale_val, exhale_val))
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plt.subplot(211)
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plt.plot(sav_filtered)
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plt.plot(prev_vals_mean)
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plt.subplot(212)
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plt.bar(['Inhale', 'exhale'], [inhale_val, exhale_val])
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plt.ylim([0,1])
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plt.draw()
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plt.pause(0.0011)
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plt.clf()
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else:
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prev_vals.append(obs)
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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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return 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_pysource_testing.cfg", type = str)#cfg/yolov3.cfg yolov3_pysource_testing.cfg
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# parser.add_argument("--weights", dest = 'weightsfile', help =
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# "weightsfile",
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# default = "data/yolov3_training_final_17_10_2020.weights", type = str)#yolov3.weights yolov3-wider_face_16000 yolov3_training_final_17_10_2020.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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val_reset = False
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def osc_command_receiver(unused_addr):
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global val_reset
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val_reset = True
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print('Reset')
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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 = 1
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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(cfg_file)#args.cfgfile)
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model.load_weights(weight_file)#args.weightsfile)
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print("Network successfully loaded")
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print(CUDA)
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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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camera = Boson()
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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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dispatcher = dispatcher.Dispatcher()
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dispatcher.map("/python/reset", osc_command_receiver)
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server = osc_server.ThreadingOSCUDPServer(('192.168.21.87', 12000), dispatcher)
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thread = threading.Thread(target=server.serve_forever)
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thread.start()
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while True:
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image = camera.grab()
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img = (image - np.min(image)) / (np.max(image) - np.min(image)) * 255
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img = img.astype(np.uintc)
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frame = np.uint8(img)
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frame= cv2.cvtColor(frame, cv2.COLOR_GRAY2BGR)
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if len(image):
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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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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,image), 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('r') or val_reset == True:
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print("______Resetting Breathing Values_______")
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prev_vals.clear()
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val_reset = False
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plt.draw()
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plt.pause(0.0011)
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plt.clf()
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if key & 0xFF == ord('q'):
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print("Closing Down the program")
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camera.close()
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plt.close()
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cv2.destroyAllWindows()
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break
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frames += 1
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if frames % 100 == 0:
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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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