├── Images └── README.md ├── METADATA ├── camera_placement.txt ├── flrpln.png └── trackfile.txt ├── VIDEOS └── README.md ├── algorithm-chart.pdf ├── DEPENDENCIES └── README.md ├── CODE ├── clothingRecognition.py ├── config.py ├── time_analyze.py ├── splicer.py ├── initial_detect.py ├── track_logger.py ├── enter_camera_data.py ├── main.py └── deep_reid.py ├── .gitignore ├── README.md └── LICENSE /Images/README.md: -------------------------------------------------------------------------------- 1 | Stores images for repo description. 2 | -------------------------------------------------------------------------------- /METADATA/camera_placement.txt: -------------------------------------------------------------------------------- 1 | (84, 20) 2 | (149, 22) 3 | (149, 62) 4 | (84, 62) 5 | -------------------------------------------------------------------------------- /VIDEOS/README.md: -------------------------------------------------------------------------------- 1 | Add security footage Files here in the form of: 2 | 3 | `cam_X.avi` 4 | -------------------------------------------------------------------------------- /METADATA/flrpln.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/arvganesh/Multi-Camera-Object-Tracking/HEAD/METADATA/flrpln.png -------------------------------------------------------------------------------- /algorithm-chart.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/arvganesh/Multi-Camera-Object-Tracking/HEAD/algorithm-chart.pdf -------------------------------------------------------------------------------- /DEPENDENCIES/README.md: -------------------------------------------------------------------------------- 1 | # Dependencies 2 | 3 | 4 | In this folder, clone into: Within this folder, clone into: 5 | - [deeppersonreid](https://github.com/KaiyangZhou/deep-person-reid) 6 | - [ssd.pytorch](https://github.com/amdegroot/ssd.pytorch) 7 | 8 | -------------------------------------------------------------------------------- /CODE/clothingRecognition.py: -------------------------------------------------------------------------------- 1 | from config import * 2 | 3 | def detect_clothes(img): # Detects clothing in an image 4 | clth_model = load_model(PATH_TO_MODEL) # Load Model 5 | img = transform(img) 6 | pred = clth_model.predict(img) 7 | return pred 8 | 9 | def transform(img): # image path 10 | im = keras_image.load_img(img, target_size=(200, 200)) 11 | im = keras_image.img_to_array(im) 12 | im = np.expand_dims(im, axis=0) 13 | # (1, height, width, channels), add a dimension because the model expects this shape: (batch_size, height, width, channels) 14 | im /= 255. 15 | return im 16 | 17 | print (detect_clothes("jeans.jpeg")) -------------------------------------------------------------------------------- /CODE/config.py: -------------------------------------------------------------------------------- 1 | import sys 2 | import math 3 | import os 4 | import time 5 | import cv2 6 | import numpy as np 7 | from collections import namedtuple 8 | import torch 9 | import torch.nn as nn 10 | import torch.backends.cudnn as cudnn 11 | from torch.autograd import Variable 12 | from PIL import Image 13 | import torchvision.transforms as torch_trans 14 | 15 | 16 | 17 | WORK_DIR = os.path.normpath(os.getcwd() + os.sep + os.pardir) + "/" 18 | SSD_PATH = WORK_DIR + "DEPENDENCIES/SSD" 19 | REID_PATH = WORK_DIR + "DEPENDENCIES/deeppersonreid" 20 | VIDEO_PATH = WORK_DIR + "VIDEOS" 21 | PATH_TO_WEIGHTS = SSD_PATH + "/weights/ssd300_mAP_77.43_v2.pth" 22 | CAM_PLACEMENT_PATH = WORK_DIR + "METADATA/camera_placement.txt" 23 | TRACKFILE_PATH = WORK_DIR + "/METADATA/" + "trackfile.txt" 24 | DIST_THRESHOLD = 260 25 | MAX_REID_TIME = 30 26 | REID_NUM_SKIP_FRAMES = 5 27 | REID_DIM_X = 128 28 | REID_DIM_Y = 256 29 | SEM_DIM_X = 1 30 | SEM_DIM_Y = 1 31 | MAX_TRACK_FRAMES = 150 32 | MAX_TRACK_ERROR_FRAMES = 5 33 | 34 | sys.path.append(SSD_PATH) 35 | sys.path.insert(0, (WORK_DIR + 'DEPENDENCIES/deeppersonreid')) 36 | 37 | from ssd import build_ssd 38 | from torchreid import models 39 | 40 | # print ("WD", WORK_DIR) 41 | # print ("SP", SSD_PATH) 42 | # print ("RP", REID_PATH) 43 | # print ("VP", VIDEO_PATH) 44 | # print ("WP", PATH_TO_WEIGHTS) 45 | # print ("CAM", CAM_PLACEMENT_PATH) -------------------------------------------------------------------------------- /CODE/time_analyze.py: -------------------------------------------------------------------------------- 1 | import time 2 | class Analyzer: 3 | def __init__(self): 4 | self.info = {} 5 | def start(self,timer_name): 6 | if(timer_name in self.info): 7 | self.info[timer_name][0] = time.time() 8 | else: 9 | #[start time,logged times] 10 | self.info[timer_name] = [time.time(),[]] 11 | def stop(self, timer_name): 12 | if(timer_name in self.info): 13 | if(self.info[timer_name][0] != -1): 14 | self.info[timer_name][1].append(time.time()-self.info[timer_name][0]) 15 | self.info[timer_name][0] = -1 16 | else: 17 | print("Time Analyzer timer ("+str(timer_name)+" has not been started.") 18 | else: 19 | print("Time Analyzer timer ("+str(timer_name)+") does not exist.") 20 | def show_timer(self,timer_name,verbose=False): 21 | if(timer_name in self.info): 22 | ttl = sum(self.info[timer_name][1]) 23 | avg = ttl/float(len(self.info[timer_name][1])) 24 | print("Timer ("+str(timer_name)+"):") 25 | print(" Average: "+str(avg)) 26 | print(" Total Time: "+str(ttl)) 27 | if(verbose): 28 | print(" Recorded Values: "+str(self.info[timer_name][1])) 29 | else: 30 | print("Time Analyzer timer ("+str(timer_name)+") does not exist.") 31 | def show_times(self,verbose=False): 32 | for key, value in self.info.items(): 33 | ttl = sum(value[1]) 34 | avg = ttl/float(len(value[1])) 35 | print("Timer ("+str(key)+"):") 36 | print(" Average: "+str(avg)) 37 | print(" Total Time: "+str(ttl)) 38 | print(" Times Executed: "+str(len(value[1]))) 39 | if(verbose): 40 | print(" Recorded Values: "+str(value[1])) 41 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Don't track these folders: 2 | VIDEOS/ 3 | DEPENDENCIES/ 4 | METADATA/ 5 | 6 | # Byte-compiled / optimized / DLL files 7 | __pycache__/ 8 | *.py[cod] 9 | *$py.class 10 | 11 | # C extensions 12 | *.so 13 | 14 | # Distribution / packaging 15 | .Python 16 | build/ 17 | develop-eggs/ 18 | dist/ 19 | downloads/ 20 | eggs/ 21 | .eggs/ 22 | lib/ 23 | lib64/ 24 | parts/ 25 | sdist/ 26 | var/ 27 | wheels/ 28 | *.egg-info/ 29 | .installed.cfg 30 | *.egg 31 | MANIFEST 32 | 33 | # PyInstaller 34 | # Usually these files are written by a python script from a template 35 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 36 | *.manifest 37 | *.spec 38 | 39 | # Installer logs 40 | pip-log.txt 41 | pip-delete-this-directory.txt 42 | 43 | # Unit test / coverage reports 44 | htmlcov/ 45 | .tox/ 46 | .coverage 47 | .coverage.* 48 | .cache 49 | nosetests.xml 50 | coverage.xml 51 | *.cover 52 | .hypothesis/ 53 | .pytest_cache/ 54 | 55 | # Translations 56 | *.mo 57 | *.pot 58 | 59 | # Django stuff: 60 | *.log 61 | local_settings.py 62 | db.sqlite3 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | target/ 76 | 77 | # Jupyter Notebook 78 | .ipynb_checkpoints 79 | 80 | # pyenv 81 | .python-version 82 | 83 | # celery beat schedule file 84 | celerybeat-schedule 85 | 86 | # SageMath parsed files 87 | *.sage.py 88 | 89 | # Environments 90 | .env 91 | .venv 92 | env/ 93 | venv/ 94 | ENV/ 95 | env.bak/ 96 | venv.bak/ 97 | 98 | # Spyder project settings 99 | .spyderproject 100 | .spyproject 101 | 102 | # Rope project settings 103 | .ropeproject 104 | 105 | # mkdocs documentation 106 | /site 107 | 108 | # mypy 109 | .mypy_cache/ 110 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # CCTVTracker 2 | Track an object across a CCTV Network with non-overlapping camera views. 3 | 4 | ![ani-gif](https://media.giphy.com/media/RgyqvpsJEbegHCVb08/giphy.gif) 5 | 6 | *Chopiness is due to a low framerate camera being used to record CCTV Footage* 7 | 8 | [FULL DEMO](https://youtu.be/8q7Zv_42oH0) 9 | 10 | ### Table of Contents 11 | - Dependencies 12 | - Thought Process 13 | - Flowchart 14 | - Output 15 | - Authors 16 | 17 |   18 |   19 |   20 |   21 | 22 | ## Dependencies 23 | + Directions in /DEPENDENCIES folder 24 | + opencv and opencv_contrib 25 | + numpy 26 | + PyTorch 27 | + matplotlib 28 | 29 | ## How it works 30 | 31 | When a tracking subject is selected by the user, they are "remembered" by the algorithm. While in the view of a single camera, the subject is tracked using a general object tracking algorithm from OpenCV. When the subject has left the view of a given camera, the surrounding cameras are searched for the tracking subject using the user's initial selection as a reference. Once the algorithm identifies the tracking subject in new camera, single camera tracking resumes. 32 | 33 | ## Algorithm Flowchart 34 | ![algorithm-flowchart](https://user-images.githubusercontent.com/21336191/63116742-faadc000-bf5f-11e9-8372-994f0d94395d.jpg) 35 | 36 | ## Output 37 | The algorithm outputs a video of the tracking subject travelling through the network with a bounding box around them. Below, are still frames from the output video produced on our sample footage. 38 | 39 | ![cam2](https://user-images.githubusercontent.com/21336191/63117797-1dd96f00-bf62-11e9-8d67-54776a8296dc.jpg) 40 | 41 | ![cam3](https://user-images.githubusercontent.com/21336191/63117806-22058c80-bf62-11e9-81f1-bc644139a95f.jpg) 42 | 43 | ### Inputting information about the Camera Network 44 | The algorithm relies on a user-inputted "map" of the camera network (showing relative location and camera adjacency) entered here: 45 | 46 | camera-gui 47 | 48 | The user can "place" cameras in placement mode, then indicate the adjacency through the "connections" in connection mode. 49 | 50 | ## Authors 51 | 52 | - [Arvind Ganesh](github.com/arvganesh) 53 | - [Colin Hurley](github.com/colHur) 54 | -------------------------------------------------------------------------------- /CODE/splicer.py: -------------------------------------------------------------------------------- 1 | from config import * 2 | 3 | Camera_info = namedtuple('Camera_info', ['frame_rate', 'start_frame', 'connections', 'file_name']) 4 | #cam_files as array of all video files from cameras 5 | #data_frame_increment is number of frames skipped between each data point 6 | 7 | #splicer.splicer(camera_video_files,TRACKFILE_PATH,data_inc) 8 | def splicer(camera_video_files, TRACKFILE_PATH, data_inc): 9 | f = open(TRACKFILE_PATH, "r") 10 | caps = [] 11 | for x in range(len(camera_video_files)): 12 | # print ("camfiles:", x) 13 | caps.append(cv2.VideoCapture(camera_video_files[x].file_name)) 14 | # print (VIDEO_PATH + "/" + camera_video_files[x].file_name) 15 | caps[len(caps)-1].set(cv2.CAP_PROP_FPS, camera_video_files[x].frame_rate) 16 | #caps[x] = (cv2.VideoCapture(cam_files[x])) 17 | #caps[x].set(cv2.CAP_PROP_FPS, frame_rate) 18 | 19 | bbox_w = 0 20 | bbox_h = 0 21 | for x in f: 22 | x = x.rstrip("\n") 23 | if(x == ""): continue 24 | elif(x[0]=="!"): 25 | vals = x[2:len(x)-1].split(",") 26 | cam_ID_to_use = int(vals[0]) 27 | print("Using Cam_"+str(cam_ID_to_use)) 28 | caps[cam_ID_to_use].set(cv2.CAP_PROP_POS_FRAMES,int(vals[1])) 29 | # print("startfrm: ",int(vals[1])) 30 | bbox_w = int(vals[2]) 31 | bbox_h = int(vals[3]) 32 | elif(x[0] == "["): 33 | bbox = x[1:len(x)-1].split(",") 34 | p1 = (int(float(bbox[0])), int(float(bbox[1]))) 35 | p2 = (int(float(bbox[0]) + bbox_w), int(float(bbox[1]) + bbox_h)) 36 | for z in range(data_inc): 37 | #time.sleep(0.1) 38 | ok, frame = caps[cam_ID_to_use].read() 39 | 40 | if not ok: 41 | print ("not ok. SPLICER") 42 | break 43 | #return False 44 | else: 45 | cv2.rectangle(frame, p1, p2, (255,0,0), 2, 1) 46 | cv2.imshow("Frame",frame) 47 | k = cv2.waitKey(30) & 0xff 48 | if k == 27 : break 49 | # Exit if ESC pressed 50 | 51 | f.close() 52 | 53 | #uncomment to use as stand-alone file 54 | 55 | # camera_video_files = [0]*4 56 | 57 | # cons = [0]*4 58 | 59 | # camera_video_files[0] = Camera_info(frame_rate = 8,start_frame = 10*8,file_name = VIDEO_PATH+"/cam_8.avi",connections = cons[0]) 60 | # camera_video_files[1] = Camera_info(frame_rate = 9,start_frame = 2*9,file_name = VIDEO_PATH+"/cam_30.avi",connections = cons[1]) 61 | # camera_video_files[2] = Camera_info(frame_rate = 30,start_frame = 3*10,file_name = VIDEO_PATH+"/cam_23.avi",connections = cons[2]) 62 | # camera_video_files[3] = Camera_info(frame_rate = 21,start_frame = 44*21,file_name = VIDEO_PATH+"/cam_10.avi",connections = cons[3]) 63 | 64 | # splicer(camera_video_files,WORK_DIR + "METADATA/" + "trackfile.txt", 2) -------------------------------------------------------------------------------- /CODE/initial_detect.py: -------------------------------------------------------------------------------- 1 | from config import * 2 | # if torch.cuda.is_available(): 3 | # torch.set_default_tensor_type('torch.cuda.FloatTensor') 4 | 5 | 6 | def init_det(frame, net): 7 | # st = time.time() 8 | # net = build_ssd('test', 300, 21) # initialize SSD 9 | # net.load_weights(weights) 10 | # et = time.time() 11 | # print ("ssd time", et - st) 12 | 13 | # print (type(net)) 14 | # cv2.imread(frame, cv2.IMREAD_COLOR) # uncomment if dataset not downloaded 15 | image = frame 16 | # from data import VOCDetection, VOC_ROOT, VOCAnnotationTransform 17 | # here we specify year (07 or 12) and dataset ('test', 'val', 'train') 18 | # testset = VOCDetection(VOC_ROOT, [('2007', 'val')], None, VOCAnnotationTransform()) 19 | # image = testset.pull_image(img_id) 20 | rgb_image = frame 21 | # View the sampled input image before transform 22 | # plt.figure(figsize=(10,10)) 23 | # plt.imshow(rgb_image) 24 | x = cv2.resize(image, (300, 300)).astype(np.float32) 25 | x -= (104.0, 117.0, 123.0) 26 | x = x.astype(np.float32) 27 | x = x[:, :, ::-1].copy() 28 | #splt.imshow(x) 29 | x = torch.from_numpy(x).permute(2, 0, 1) 30 | 31 | xx = Variable(x.unsqueeze(0)) # wrap tensor in Variable 32 | if torch.cuda.is_available(): 33 | xx = xx.cuda() 34 | y = net(xx) 35 | 36 | from data import VOC_CLASSES as labels 37 | top_k=10 38 | 39 | #plt.figure(figsize=(10,10)) 40 | # colors = plt.cm.hsv(np.linspace(0, 1, 21)).tolist() 41 | #plt.imshow(rgb_image) # plot the image for matplotlib 42 | # currentAxis = plt.gca() 43 | 44 | detections = y.data 45 | # scale each detection back up to the image 46 | scale = torch.Tensor(rgb_image.shape[1::-1]).repeat(2) 47 | big_coords = [] 48 | for i in range(detections.size(1)): 49 | j = 0 50 | while detections[0,i,j,0] >= 0.6: 51 | score = detections[0,i,j,0] 52 | label_name = labels[i-1] 53 | if (label_name == "person"): 54 | display_txt = '%s: %.2f'%(label_name, score) 55 | pt = (detections[0,i,j,1:]*scale).cpu().numpy() 56 | coords = [int(pt[0]), int(pt[1]), int(pt[2]-pt[0]+1), int(pt[3]-pt[1]+1)] 57 | big_coords.append(coords) 58 | # print (coords) 59 | # color = colors[i] 60 | # currentAxis.add_patch(plt.Rectangle(*coords, fill=False, edgecolor=color, linewidth=2)) 61 | # currentAxis.text(pt[0], pt[1], display_txt, bbox={'facecolor':color, 'alpha':0.5}) 62 | j+=1 63 | # plt.show() 64 | return big_coords 65 | 66 | """ 67 | USAGE: 68 | init_det('frame.jpg', 'path-to-weights.pth' [OPTIONAL]) 69 | """ 70 | 71 | # init_det(SSD_PATH + '/lw.jpg') 72 | 73 | 74 | -------------------------------------------------------------------------------- /METADATA/trackfile.txt: -------------------------------------------------------------------------------- 1 | 2 | !(0,25,105,250) 3 | (296,229) 4 | (294,215) 5 | (297,204) 6 | (299,185) 7 | (310,182) 8 | (314,173) 9 | (321,163) 10 | (326,148) 11 | (334,138) 12 | (341,144) 13 | (341,135) 14 | (341,135) 15 | (343,126) 16 | (341,121) 17 | (337,112) 18 | (338,110) 19 | (338,105) 20 | (338,98) 21 | (339,100) 22 | (337,90) 23 | (337,87) 24 | (338,84) 25 | (339,83) 26 | (339,80) 27 | (339,79) 28 | (340,78) 29 | (342,74) 30 | (341,72) 31 | (341,69) 32 | (340,69) 33 | (339,59) 34 | (337,58) 35 | (335,52) 36 | (334,51) 37 | (337,50) 38 | (335,50) 39 | (333,46) 40 | (333,45) 41 | (333,43) 42 | (336,44) 43 | (336,43) 44 | (334,41) 45 | (337,41) 46 | (337,39) 47 | (339,40) 48 | (339,37) 49 | (339,37) 50 | (342,36) 51 | (345,37) 52 | (346,37) 53 | (346,35) 54 | (347,36) 55 | (351,33) 56 | (351,33) 57 | (351,25) 58 | (353,29) 59 | (358,27) 60 | (357,28) 61 | (356,21) 62 | (355,20) 63 | (355,15) 64 | (356,15) 65 | (356,19) 66 | (355,14) 67 | (354,13) 68 | (355,13) 69 | (356,13) 70 | (354,13) 71 | (356,13) 72 | (357,14) 73 | (358,15) 74 | (359,15) 75 | (360,16) 76 | (361,17) 77 | (362,18) 78 | (361,17) 79 | 80 | !(1,715,105,250) 81 | (296,229) 82 | (296,228) 83 | (296,228) 84 | (302,240) 85 | (308,245) 86 | (313,243) 87 | (308,240) 88 | (313,246) 89 | (310,244) 90 | (310,243) 91 | (310,241) 92 | (311,242) 93 | (309,243) 94 | (310,242) 95 | (312,245) 96 | (311,245) 97 | (311,240) 98 | (313,241) 99 | (308,239) 100 | (308,239) 101 | (309,238) 102 | (309,238) 103 | (309,238) 104 | (309,240) 105 | (308,238) 106 | (309,238) 107 | (307,239) 108 | (309,238) 109 | (307,238) 110 | (308,237) 111 | (308,237) 112 | (309,237) 113 | (309,237) 114 | (310,240) 115 | (311,239) 116 | (311,241) 117 | (314,242) 118 | (313,242) 119 | (314,241) 120 | (313,242) 121 | (313,243) 122 | (314,240) 123 | (314,240) 124 | (313,238) 125 | (314,239) 126 | (313,239) 127 | (314,239) 128 | (313,239) 129 | (312,239) 130 | (312,239) 131 | (312,239) 132 | (313,239) 133 | (313,239) 134 | (312,234) 135 | (312,238) 136 | (312,236) 137 | (312,237) 138 | (312,238) 139 | (313,239) 140 | (313,239) 141 | (314,238) 142 | (314,238) 143 | (313,238) 144 | (313,237) 145 | (313,237) 146 | (313,237) 147 | (313,238) 148 | (313,237) 149 | (312,235) 150 | (314,234) 151 | (314,237) 152 | (313,234) 153 | (312,234) 154 | (313,237) 155 | (313,237) 156 | (313,236) 157 | 158 | !(2,49,105,250) 159 | (296,229) 160 | (294,229) 161 | (295,231) 162 | (294,229) 163 | (294,229) 164 | (293,229) 165 | (295,231) 166 | (296,228) 167 | (294,228) 168 | (294,229) 169 | (294,228) 170 | (293,228) 171 | (294,228) 172 | (293,228) 173 | (293,231) 174 | (294,228) 175 | (294,229) 176 | (294,229) 177 | (293,229) 178 | (293,229) 179 | (293,228) 180 | (293,229) 181 | (295,229) 182 | (298,231) 183 | (299,230) 184 | (301,230) 185 | (299,229) 186 | (351,234) 187 | (350,230) 188 | (348,231) 189 | (350,233) 190 | (355,234) 191 | (359,232) 192 | (399,239) 193 | (398,240) 194 | (395,241) 195 | (395,242) 196 | (401,242) 197 | (404,242) 198 | (403,242) 199 | (403,243) 200 | (406,243) 201 | (402,242) 202 | (404,242) 203 | (404,242) 204 | (405,242) 205 | (406,241) 206 | (403,241) 207 | (406,243) 208 | (405,242) 209 | (406,242) 210 | (403,242) 211 | (405,241) 212 | (408,242) 213 | (409,243) 214 | (411,243) 215 | (412,244) 216 | (413,243) 217 | (413,243) 218 | (413,243) 219 | (413,243) 220 | (417,244) 221 | (418,244) 222 | (418,244) 223 | (418,244) 224 | (418,244) 225 | (418,244) 226 | (417,244) 227 | (418,244) 228 | (417,244) 229 | (417,244) 230 | (416,243) 231 | (415,244) 232 | (415,243) 233 | (416,243) 234 | (416,243) 235 | 236 | !(3,20,105,250) 237 | (296,229) 238 | (296,229) 239 | (296,231) 240 | (298,232) 241 | (302,239) 242 | (305,244) 243 | (308,254) 244 | (294,243) 245 | (305,257) 246 | (313,262) 247 | (311,256) 248 | (306,249) 249 | (307,253) 250 | (309,257) 251 | (310,253) 252 | (309,255) 253 | (310,253) 254 | (309,252) 255 | (308,251) 256 | (309,253) 257 | (308,250) 258 | (310,253) 259 | (308,250) 260 | (310,254) 261 | (312,257) 262 | (311,257) 263 | (309,254) 264 | -------------------------------------------------------------------------------- /CODE/track_logger.py: -------------------------------------------------------------------------------- 1 | import cv2 2 | from config import WORK_DIR, MAX_TRACK_FRAMES, MAX_TRACK_ERROR_FRAMES, VIDEO_PATH 3 | from cv2 import selectROI 4 | import numpy as np 5 | import matplotlib.pyplot as plt 6 | import matplotlib.image as mpimg 7 | import sys 8 | import math 9 | import time 10 | 11 | ''' 12 | import h5py 13 | with h5py.File('random.hdf5', 'w') as f: 14 | dset = f.create_dataset("default", data=arr) 15 | dset = f.create_dataset("yeet", data=arr[:5]) 16 | f.close() 17 | 18 | with h5py.File('random.hdf5', 'r') as f: 19 | deff = f['default'] 20 | yeet = f['yeet'] 21 | print(deff, yeet) 22 | f.close() 23 | with h5py.File('PreprocessedData.h5', 'w') as hf: 24 | hf.create_dataset("X_train", data=X_train_data, maxshape=(None, 512, 512, 9)) 25 | 26 | 27 | ''' 28 | 29 | #frames_elapsed, error = track_logger.track(cap = caps[cur_cam_indx], bbox = bbox, data_inc = data_inc) 30 | def track(cam_id, cap, bbox, data_inc, time_analyzer): 31 | f = open((WORK_DIR + "/METADATA/" + "trackfile.txt"),"a+") 32 | track_info = [] 33 | frm_cnt = 0 34 | print("Progress Cam_" + str(cam_id) + ": ", end="", flush=True) 35 | #cam, start frame, bbox width, bbox height 36 | f.write("\n"+"!("+str(cam_id)+","+str(int(cap.get(1)))+","+str(bbox[2])+","+str(bbox[3])+")\n") 37 | f.write("("+str(int(bbox[0]))+","+str(int(bbox[1]))+"]") 38 | # track_info.append("\n"+"!("+str(cam_id)+","+str(int(cap.get(1)))+","+str(bbox[2])+","+str(bbox[3])+")\n") 39 | # track_info.append("["+str(int(bbox[0]))+","+str(int(bbox[1]))+"]") 40 | 41 | tracker_type = "CSRT" 42 | # if tracker_type == 'BOOSTING': 43 | # tracker = cv2.TrackerBoosting_create() 44 | # if tracker_type == 'MIL': 45 | # tracker = cv2.TrackerMIL_create() 46 | # if tracker_type == 'KCF': 47 | # tracker = cv2.TrackerKCF_create() 48 | # if tracker_type == 'TLD': 49 | # tracker = cv2.TrackerTLD_create() 50 | # if tracker_type == 'MEDIANFLOW': 51 | # tracker = cv2.TrackerMedianFlow_create() 52 | # if tracker_type == 'GOTURN': 53 | # tracker = cv2.TrackerGOTURN_create() 54 | # if tracker_type == 'MOSSE': 55 | # tracker = cv2.TrackerMOSSE_create() 56 | # if tracker_type == "CSRT":' 57 | # st = time.time() 58 | tracker = cv2.TrackerCSRT_create() 59 | # print ("tracker time", time.time() - st) 60 | """ 61 | BOOSTING -> ye 62 | MIL -> H E C K no 63 | KCF -> ehhhhhhhhhhhhhh 64 | TLD -> straight trash lmao 65 | MEDIANFLOW -> see TLD 66 | GOTURN -> nope 67 | MOSSE -> see above and TLD 68 | CSRT -> yeh 69 | """ 70 | ok, frame = cap.read() 71 | # st = time.time() 72 | 73 | ok = tracker.init(frame, tuple(bbox)) 74 | # print ("tracker time", time.time() - st) 75 | fail_detect_itrs = 0 76 | cycle = 0 77 | while True: 78 | # Read a new frame 79 | time_analyzer.start("reading") 80 | ok, frame = cap.read() 81 | time_analyzer.stop("reading") 82 | frm_cnt += 1 83 | if not ok: 84 | f.close() 85 | # write_to_trackfile(track_info) 86 | #finish out progress bar 87 | numofthing = math.ceil((MAX_TRACK_FRAMES - frm_cnt) / data_inc) 88 | print("#" * numofthing, end="", flush=True) 89 | print("") 90 | # with h5py.File((WORK_DIR + "/METADATA/" + "trackfile.hdf5"), 'w') as f: 91 | # f.create_dataset("tracklog", data=track_info) 92 | # f.close() 93 | return frm_cnt # goes to next cams 94 | 95 | # Update tracker 96 | time_analyzer.start("update") 97 | ok, bbox = tracker.update(frame) 98 | time_analyzer.stop("update") 99 | 100 | #only record at data_inc 101 | if(cycle==data_inc-1): 102 | cycle=0 103 | #record bbox pos 104 | if ok: 105 | # track_info.append([int(bbox[0]),int(bbox[1])]) 106 | f.write("("+str(int(bbox[0]))+","+str(int(bbox[1]))+")\n") 107 | else: 108 | # track_info.append([-1,-1]) 109 | f.write("-e-\n") 110 | #increment progress bar 111 | print("#", end="", flush=True) 112 | else: 113 | cycle+=1 114 | if(not ok): 115 | fail_detect_itrs+=1 116 | if(fail_detect_itrs > MAX_TRACK_ERROR_FRAMES): 117 | # write_to_trackfile(track_info) 118 | f.close() 119 | print("") 120 | return frm_cnt 121 | if(frm_cnt > MAX_TRACK_FRAMES): 122 | # write_to_trackfile(track_info) 123 | f.close() 124 | print("") 125 | return frm_cnt 126 | 127 | 128 | 129 | def write_to_trackfile(track_info): 130 | # st = time.time() 131 | with open(WORK_DIR + "/METADATA/" + "trackfile.txt", "a+") as f: 132 | for item in track_info: 133 | f.write(str(item) + "\n") 134 | # print(str(item) + "\n") 135 | f.close() 136 | # et = time.time() 137 | # print ("write time:", et - st) 138 | # sys.exit() 139 | 140 | #uncomment to use as stand-alone file 141 | # start_frame = 8*3 142 | # vid = VIDEO_PATH + "/cam_8.avi" 143 | # cap = cv2.VideoCapture(vid) 144 | # cap.set(cv2.CAP_PROP_FPS, 8) 145 | # cap.set(cv2.CAP_PROP_POS_FRAMES, start_frame) 146 | # ok, frame = cap.read() 147 | # bbox = selectROI(frame) 148 | # track(0,cap,bbox,2) 149 | 150 | 151 | # cam_id, cap, bbox, data_inc 152 | 153 | """ 154 | What we know 155 | - bounding box coords are accurate 156 | - tracker is activated, but for some reason stuck in the same spot. 157 | """ -------------------------------------------------------------------------------- /CODE/enter_camera_data.py: -------------------------------------------------------------------------------- 1 | import cv2 2 | import numpy as np 3 | import math 4 | import sys 5 | from config import WORK_DIR 6 | #from bitarray import bitarray 7 | 8 | cams = [] 9 | max_cams = 255 10 | cons = np.zeros((0,0), dtype = np.bool) 11 | max_dist = 10 12 | selected_cam = None 13 | placing = True 14 | invalidInput = True 15 | floorplan_file = WORK_DIR + "METADATA/" + "flrpln.png" 16 | org_img = cv2.imread(floorplan_file) 17 | cam_img = org_img.copy() 18 | conn_img = cam_img.copy() 19 | 20 | 21 | def dist(p1,p2): return (math.sqrt((p1[0]-p2[0])**2+(p1[1]-p2[1])**2)) 22 | 23 | def closest_cam(x, y): 24 | closestCam = [-1,math.inf] 25 | for c in range(len(cams)): 26 | d = dist((x,y),(cams[c][0],cams[c][1])) 27 | if(d0): 35 | for row in range(len(cons)): 36 | for c in range(row,len(cons)): 37 | if(cons[row][c]): 38 | cv2.line(conn_img,(cams[row][0],cams[row][1]), (cams[c][0],cams[c][1]), (0,0,0), 4) 39 | 40 | def update_cameras_img(): 41 | global cam_img 42 | cam_img = org_img.copy() 43 | for c in cams: 44 | cv2.circle(cam_img, (c[0],c[1]),5,(0,0,255),-1) 45 | 46 | def mouse_evt(event,x,y,flags,param): 47 | global cons,cams, selected_cam 48 | if(event == cv2.EVENT_LBUTTONDOWN): 49 | if(placing): 50 | if(len(cams)0): 78 | indx = closest_cam(x,y) 79 | if(indx != None): 80 | cons = np.delete(cons, indx, axis = 0) 81 | cons = np.delete(cons, indx, axis = 1) 82 | del cams[indx] 83 | update_cameras_img() 84 | cv2.imshow("Floorplan",cam_img) 85 | 86 | while(invalidInput): 87 | inpt = input("Do you want to overwrite any existing information?(y/n) ") 88 | if(inpt == "y"): 89 | #clear files 90 | f = open("camera_placement.txt","w") 91 | f.close() 92 | f = open("connection_info.bin","wb") 93 | f.close() 94 | invalidInput = False 95 | elif(inpt == "n"): 96 | try: 97 | f_cams = open("camera_placement.txt","r") 98 | f_cons = open("connection_info.txt","r") 99 | f_cams.seek(0) 100 | f_cons.seek(0) 101 | for x in f_cams: 102 | x = x.rstrip() 103 | if(x[0] == "("): 104 | vals = (x[1:len(x)-1]).split(",") 105 | for v in range(len(vals)): 106 | vals[v] = int(vals[v]) 107 | cams.append(tuple(vals)) 108 | 109 | update_cameras_img() 110 | 111 | cons = np.zeros((len(cams),len(cams)),dtype=np.bool) 112 | itr = 0 113 | for x in f_cons: 114 | q = x.rstrip() 115 | for j in range(len(q)): 116 | cons[itr][j] = bool(int(q[j])) 117 | itr += 1 118 | #cons[itr] = list(x) 119 | update_connections_img() 120 | # with f_cons as f: 121 | # first_byte = f.read(1) 122 | # num_cams = int.frombytes(byte,byteorder='big') 123 | # cons = np.array((num_cams,num_cams),dtype=np.bool) 124 | # bytes_per_cam = math.ceil(num_cams/8) 125 | # byte = f.read(bytes_per_cam) 126 | # itr = 0 127 | # while byte: 128 | # a = bitarray() 129 | # a.frombytes(byte) 130 | # cons[itr] = (np.frombuffer(a.unpack(zero=b'\x00',one='\xFF'),dtype=np.bool,count=-1))[:num_cams] 131 | # itr += 1 132 | # byte = f.read(bytes_per_cam) 133 | except: pass 134 | invalidInput = False 135 | print("Number of cameras must be "+str(max_cams)+" or less") 136 | print("Use Esc key to save data and close the window") 137 | print("Use Spacebar to enter Camera Placement mode") 138 | print("Use Enter key to enter Camera Connetion mode") 139 | 140 | cv2.namedWindow("Floorplan") 141 | cv2.setMouseCallback("Floorplan",mouse_evt) 142 | cv2.imshow("Floorplan",cam_img) 143 | while(True): 144 | k = cv2.waitKey(1) & 0xff 145 | if k == 27: 146 | if(len(cams)>0): 147 | f_cams = open(WORK_DIR + "METADATA/" + "camera_placement.txt","w") 148 | for x in range(len(cams)): 149 | f_cams.write(str(cams[x])+"\n") 150 | f_cams.close() 151 | f_cons = open(WORK_DIR + "METADATA/" + "connection_info.txt","w") 152 | for x in range(len(cons)): 153 | for j in range(len(cons[x])): 154 | f_cons.write(str(int(cons[x][j]))) 155 | f_cons.write("\n") 156 | f_cons.close() 157 | cv2.destroyAllWindows() 158 | print("Ending...") 159 | sys.exit() 160 | elif k == 32: 161 | if(not placing): 162 | print("Placement Mode") 163 | update_cameras_img() 164 | cv2.imshow("Floorplan",cam_img) 165 | placing = True 166 | elif k == 13: 167 | if(placing): 168 | print("Connection Mode") 169 | update_connections_img() 170 | cv2.imshow("Floorplan",conn_img) 171 | placing = False 172 | 173 | 174 | 175 | 176 | 177 | -------------------------------------------------------------------------------- /CODE/main.py: -------------------------------------------------------------------------------- 1 | import time 2 | import sys 3 | import cv2 4 | from cv2 import selectROI 5 | from config import * 6 | from collections import namedtuple 7 | from deep_reid import deep_reid 8 | import track_logger as track_logger 9 | from initial_detect import init_det 10 | import splicer 11 | from time_analyze import Analyzer 12 | Camera_info = namedtuple('Camera_info', ['frame_rate', 'start_frame', 'connections', 'file_name']) 13 | 14 | # print("Fix DIM_X and DIM_Y in config. I put place holders but they arent the right values") 15 | 16 | t = Analyzer() 17 | 18 | """ INIT MODELS """ 19 | net = build_ssd('test', 300, 21) # init SSD 20 | net.load_weights(PATH_TO_WEIGHTS) # init SSD 21 | 22 | model = models.init_model("resnet50", num_classes=1, loss={'xent'}, use_gpu=False) # init REID 23 | 24 | """ *********** """ 25 | 26 | def check_bboxes(frame): 27 | # frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) 28 | global subject 29 | bboxes_ppl = init_det(frame=frame, net=net) 30 | for bbox in bboxes_ppl: 31 | bbox_img_org = frame[bbox[1]:bbox[1]+bbox[3],bbox[0]:bbox[0]+bbox[2]] 32 | bbox_img = cv2.resize(bbox_img_org, (SEM_DIM_X,SEM_DIM_Y)) 33 | # attrs = semantic_attribute_det(bbox_img) 34 | # if (attrs == subject["attr_id"]): 35 | # #bbox_img = cv2.resize(frame[bbox[1]:bbox[1]+bbox[3],bbox[0]:bbox[0]+bbox[2]], (REID_DIM_X,REID_DIM_Y)) 36 | 37 | # while True: 38 | # cv2.imshow("image", bbox_img_org) 39 | # cv2.imshow("img2", subject["img"]) 40 | # k = cv2.waitKey(1) & 0xff 41 | # if k == 27: 42 | # cv2.destroyAllWindows() 43 | # break 44 | # # sys.exit() 45 | dist = deep_reid(subject["img"], bbox_img_org, model) 46 | # print ("Distmat", dist) 47 | if(dist < DIST_THRESHOLD): 48 | # print ("bbox",bbox) 49 | return bbox 50 | return None 51 | 52 | #clear trackfile **chnage to hdf5 if necisary 53 | f = open(WORK_DIR + "METADATA/" + "trackfile.txt", "w") 54 | f.close() 55 | 56 | #get inputs 57 | st = time.time() 58 | # start_time = int(input("Start Time: ")) 59 | # end_time = int(input("End Time: ")) 60 | # data_inc = int(input("Data increment: ")) 61 | # start_camera_id = int(input("Start Camera ID: ")) 62 | 63 | start_time = 24 64 | end_time = 70 65 | data_inc = 2 66 | start_camera_id = 0 67 | 68 | 69 | #set time diff to check against 70 | #end_time -= start_time 71 | # if(end_time <= 0): 72 | # print("End time is before Start time. MAIN") 73 | # sys.exit() 74 | 75 | 76 | try: 77 | f_cons = open(WORK_DIR + "METADATA/" + "connection_info.txt","r") 78 | f_cons.seek(0) 79 | cons = [] 80 | itr = 0 81 | for x in f_cons: 82 | cons.append([]) 83 | q = x.rstrip() 84 | for j in range(len(q)): 85 | if(bool(int(q[j]))): cons[itr].append(j) 86 | itr += 1 87 | except: 88 | print("Error opening connection config file. MAIN") 89 | sys.exit() 90 | 91 | 92 | #set camera info 93 | #Unhard code this ** 94 | camera_video_files = [0]*len(cons) 95 | 96 | camera_video_files[0] = Camera_info(frame_rate = 8,start_frame = 10*8,file_name = VIDEO_PATH+"/cam_8.avi",connections = cons[0]) 97 | camera_video_files[1] = Camera_info(frame_rate = 9,start_frame = 2*9,file_name = VIDEO_PATH+"/cam_30.avi",connections = cons[1]) 98 | camera_video_files[2] = Camera_info(frame_rate = 30,start_frame = 3*10,file_name = VIDEO_PATH+"/cam_23.avi",connections = cons[2]) 99 | camera_video_files[3] = Camera_info(frame_rate = 21,start_frame = 44*21,file_name = VIDEO_PATH+"/cam_10.avi",connections = cons[3]) 100 | 101 | #initialize caps 102 | caps = [] 103 | for x in range(len(camera_video_files)): 104 | caps.append(cv2.VideoCapture(camera_video_files[x].file_name)) 105 | if(not caps[x].isOpened()): 106 | print("Error opening video. MAIN") 107 | sys.exit() 108 | caps[x].set(5, camera_video_files[x].frame_rate) 109 | caps[x].set(1,max(camera_video_files[x].start_frame-1,0)) 110 | ok,frame = caps[x].read() 111 | if not ok: 112 | print("Cannot read video file "+str(camera_video_files[x].file_name)+". MAIN") 113 | sys.exit() 114 | 115 | cur_time = start_time 116 | cur_cam_id = start_camera_id 117 | 118 | ok,frame = caps[start_camera_id].read() 119 | 120 | #bbox = (348, 80, 53, 124) 121 | bbox = selectROI(frame,False) 122 | cv2.destroyAllWindows() 123 | 124 | subject = {} 125 | 126 | 127 | # bbox_img = cv2.resize(frame[bbox[1]:bbox[1]+bbox[3],bbox[0]:bbox[0]+bbox[2]], (REID_DIM_X,REID_DIM_Y)) 128 | bbox_img = frame[bbox[1]:bbox[1]+bbox[3],bbox[0]:bbox[0]+bbox[2]] 129 | # print("bbox_img type",type(bbox_img)) 130 | subject["img"] = bbox_img 131 | subject["bbox"] = bbox 132 | # subject["attr_id"] = semantic_attribute_det(bbox_img) 133 | 134 | TOTAL_TRACKLOG_TIME = 0 135 | TOTAL_REID_TIME = 0 136 | 137 | while(cur_time < end_time): 138 | print("Now using camera"+str(cur_cam_id)) 139 | stt = time.time() 140 | frames_elapsed = track_logger.track(cam_id = cur_cam_id, cap = caps[cur_cam_id], bbox = subject["bbox"], data_inc = data_inc, time_analyzer = t) 141 | ett = time.time() 142 | TOTAL_TRACKLOG_TIME += ett-stt 143 | 144 | cur_time += int(frames_elapsed/camera_video_files[cur_cam_id].frame_rate) 145 | surrounding_cams = camera_video_files[cur_cam_id].connections 146 | if(len(surrounding_cams) == 0): 147 | print("Cam_"+str(cur_cam_id)+" does not have any surrounding cameras. MAIN") 148 | print ("Amount of video analyzed: "+str(cur_time) +" seconds.") 149 | break 150 | begin_search_time = time.time() 151 | found = False 152 | frame_itr = 0 153 | frame_itr_interval = REID_NUM_SKIP_FRAMES 154 | print("Calculating",end="",flush=True) 155 | stt = time.time() 156 | while(not found): 157 | if(time.time()-begin_search_time > MAX_REID_TIME): 158 | print("Could not RE-ID subject within "+str(MAX_REID_TIME)+" seconds. Ending Program. MAIN") 159 | cur_time = end_time+1 #break outer while 160 | break 161 | for cam in surrounding_cams: 162 | ok, frame = caps[cam].read() 163 | if(not ok): 164 | print("Not ok frame. MAIN") 165 | else: 166 | print(".",end="",flush=True) 167 | match_bbox = check_bboxes(frame) 168 | if(match_bbox != None): 169 | subject["bbox"] = match_bbox 170 | cur_cam_id = cam 171 | found = True 172 | print ("") 173 | break 174 | 175 | for _ in range(frame_itr_interval): 176 | caps[cam].read() 177 | frame_itr += frame_itr_interval 178 | ett = time.time() 179 | TOTAL_REID_TIME += ett-stt 180 | if(cur_cam_id == 0): break #0 is last camera so finish search 181 | 182 | et = time.time() 183 | print("TOTAL_TRACKLOG_TIME",TOTAL_TRACKLOG_TIME) 184 | print("TOTAL_REID_TIME",TOTAL_REID_TIME) 185 | print("\n") 186 | print(" **************** Summary ****************") 187 | print(" Total Time: {} ".format(str(et-st))) 188 | print(" Algorithm Completed ") 189 | print(" *****************************************") 190 | print("\n") 191 | 192 | t.show_times() 193 | 194 | invalid_input = True 195 | while(invalid_input): 196 | inpt = input("Would you like to generate the video? [y/n] ") 197 | if(inpt == "y"): 198 | splicer.splicer(camera_video_files,TRACKFILE_PATH,data_inc) 199 | print("Done generating video. Closing Program. MAIN") 200 | invalid_input = False 201 | elif(inpt == "n"): 202 | print("Ok. Closing Program") 203 | invalid_input = False 204 | -------------------------------------------------------------------------------- /CODE/deep_reid.py: -------------------------------------------------------------------------------- 1 | #from __future__ import #print_function 2 | from __future__ import division 3 | 4 | from config import * 5 | # from PIL import Image 6 | # import torch 7 | # import torch.nn as nn 8 | # import torch.backends.cudnn as cudnn 9 | # from torch.optim import lr_scheduler 10 | 11 | # sys.path.insert(0, (WORK_DIR + 'DEPENDENCIES/deeppersonreid')) 12 | 13 | # from torchreid.data_manager import ImageDataManager 14 | # from torchreid import models 15 | # from torchreid.losses import CrossEntropyLoss, DeepSupervision 16 | # from torchreid.utils.iotools import save_checkpoint, check_isfile 17 | # from torchreid.utils.avgmeter import AverageMeter 18 | # from torchreid.utils.loggers import Logger, RankLogger 19 | # from torchreid.utils.torchtools import count_num_param, open_all_layers, open_specified_layers 20 | # from torchreid.utils.reidtools import visualize_ranked_results 21 | # from torchreid.eval_metrics import evaluate 22 | # from torchreid.optimizers import init_optimizer 23 | 24 | # from torchvision.transforms import * 25 | 26 | 27 | def deep_reid(query_np, gal_np, model): 28 | # print ("reid") 29 | # model params 30 | use_gpus = False 31 | # root = REID_PATH + "/data" 32 | # name = "single_test" 33 | # split_id = 0 34 | # height = 256 35 | # width = 128 36 | # train_batch_size = 32 37 | # test_batch_size = 1 38 | # workers = 4 39 | # train_sampler = '' 40 | # num_instances = 4 41 | # cuhk03_labeled = False 42 | # cuhk03_classic_split = False 43 | 44 | # torch.manual_seed(1) 45 | path_to_weights = REID_PATH + "/weights/resnet50_market_xent/resnet50_market_xent.pth.tar" 46 | save_log_dir = REID_PATH + "/log/eval-resnet50" 47 | 48 | # log_name = 'log_test.txt' 49 | # sys.stdout = Logger(osp.join(save_log_dir, log_name)) 50 | 51 | # Initialize Data 52 | # dm = ImageDataManager(use_gpus, [name], [name], root, split_id, height, width, train_batch_size, test_batch_size, workers, train_sampler, num_instances, cuhk03_labeled, cuhk03_classic_split) 53 | # testloader_dict = dm.return_dataloaders() 54 | 55 | # Initialize Model 56 | # st = time.time() 57 | # model = models.init_model("resnet50", num_classes=1, loss={'xent'}, use_gpu=use_gpus) 58 | # et = time.time() 59 | # print("reid time", et-st) 60 | # Initialize and Load Weights 61 | # from functools import partial 62 | # import pickle 63 | # pickle.load = partial(pickle.load, encoding="latin1") 64 | # pickle.Unpickler = partial(pickle.Unpickler, encoding="latin1") 65 | # checkpoint = torch.load(path_to_weights, pickle_module=pickle) 66 | checkpoint = torch.load(path_to_weights, map_location='cpu') 67 | pretrain_dict = checkpoint['state_dict'] 68 | model_dict = model.state_dict() 69 | pretrain_dict = {k: v for k, v in pretrain_dict.items() if k in model_dict and model_dict[k].size() == v.size()} 70 | model_dict.update(pretrain_dict) 71 | model.load_state_dict(model_dict) 72 | 73 | # Run Model 74 | # queryloader = testloader_dict[name]['query'] 75 | # galleryloader = testloader_dict[name]['gallery'] 76 | # distmat = test(model, query_np, gal_np, use_gpus) 77 | #visualize_ranked_results(distmat, dm.return_testdataset_by_name(name), save_log_dir + "/ranked_results/yeet", 1) 78 | #print ("DIST:", distmat) 79 | #gallery = dm.return_testdataset_by_name(name)[1] 80 | return test(model, query_np, gal_np, use_gpus) 81 | 82 | def t1(img): # opencv 83 | st = time.time() 84 | # img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) 85 | imagenet_mean = [0.485, 0.456, 0.406] 86 | imagenet_std = [0.229, 0.224, 0.225] 87 | normalize = torch_trans.Normalize(mean=imagenet_mean, std=imagenet_std) 88 | transforms = [] 89 | transforms += [torch_trans.Resize((256, 128))] 90 | transforms += [torch_trans.ToTensor()] 91 | transforms += [normalize] 92 | transforms = torch_trans.Compose(transforms) 93 | # img = cv2.resize(img, (128, 256), interpolation=cv2.INTER_NEAREST) 94 | img = Image.fromarray(img) 95 | img = transforms(img) 96 | et = time.time() 97 | # print("cv time",et-st) 98 | return img 99 | 100 | def t2(img_path=None): # PIL 101 | st = time.time() 102 | if img_path is not None: 103 | img = Image.open(img_path) 104 | imagenet_mean = [0.485, 0.456, 0.406] 105 | imagenet_std = [0.229, 0.224, 0.225] 106 | normalize = Normalize(mean=imagenet_mean, std=imagenet_std) 107 | 108 | transforms = [] 109 | transforms += [Resize((256, 128))] 110 | transforms += [ToTensor()] 111 | transforms += [normalize] 112 | 113 | transforms = Compose(transforms) 114 | 115 | img = transforms(img) 116 | et = time.time() 117 | print("PIL time",et-st) 118 | return img 119 | 120 | # def main_test(): 121 | # img = cv2.imread("naturo-monkey-selfie.jpg", cv2.IMREAD_COLOR) 122 | # a = t1(img) 123 | # b = t2("naturo-monkey-selfie.jpg") 124 | # print ("opencv",a) 125 | # print ("PIL", b) 126 | # return torch.eq(a, b).all() 127 | 128 | def test(model, query_np, gal_np, use_gpu, ranks=[1, 5, 15, 20], return_distmat=True): # !!!!!! 129 | # batch_time = AverageMeter() 130 | 131 | model.eval() 132 | # imgs -> queryloader / galleryloader -> testloader_dict[q/g] -> dm.return_dataloaders() -> imagedatamanager (line 57) -> transform function -> img dataset thingy -> dataset loaders -> somehow transfroms img from img path -> transformed image. 133 | # we want to stop image writing from (numpy array -> ) 134 | with torch.no_grad(): 135 | qf = [] 136 | query_np = t1(query_np) 137 | query_np = query_np.unsqueeze(0) 138 | # print ("T1",query_np.size()) 139 | # query_np = t2("hi.jpg") 140 | # print ("T2", query_np) 141 | features = model(query_np) # !!! 142 | features = features.data.cpu() 143 | qf.append(features) 144 | # q_pids.extend(pids) 145 | # q_camids.extend(camids) 146 | qf = torch.cat(qf, 0) 147 | # q_pids = np.asarray(q_pids) 148 | # q_camids = snp.asarray(q_camids) 149 | 150 | # #print ("\n") 151 | # #print ("pid", q_pids) 152 | # #print ("camid", q_camids) 153 | # #print ("\n") 154 | gf = [] 155 | # #print ("\n") 156 | # #print ("g_pid", pids) 157 | # #print ("g_camid", camids) # 158 | gal_np = t1(gal_np) 159 | gal_np = gal_np.unsqueeze(0) 160 | features = model(gal_np) 161 | # #print ("FEaT1", features) 162 | # #print ("FL1", len(features[0])) 163 | 164 | features = features.data.cpu() 165 | # #print ("FEaT", features) 166 | # #print ("FL", len(features[0])) 167 | gf.append(features) 168 | # g_pids.extend(pids) 169 | # g_camids.extend(camids) 170 | gf = torch.cat(gf, 0) 171 | # g_pids = np.asarray(g_pids) 172 | # g_camids = np.asarray(g_camids) 173 | 174 | #print("Extracted features for gallery set, obtained {}-by-{} matrix".format(gf.size(0), gf.size(1))) 175 | 176 | m, n = qf.size(0), gf.size(0) 177 | distmat = torch.pow(qf, 2).sum(dim=1, keepdim=True).expand(m, n) + \ 178 | torch.pow(gf, 2).sum(dim=1, keepdim=True).expand(n, m).t() 179 | # #print ("dist_b", distmat) 180 | 181 | distmat = distmat.addmm(beta=1, alpha=-2, mat1=qf, mat2=gf.t()) 182 | 183 | distmat = distmat.numpy() 184 | # #print("dids: "+str(distmat)) 185 | # #print ("W",distmat) 186 | indices = np.argsort(distmat, axis=1) 187 | # #print ("RESULT:", distmat[0][0], (distmat[0][0] < 0)) 188 | # #print ("IND_E:", indices) 189 | # #print("Computing CMC and mAP") 190 | #match = evaluate(distmat, q_pids, g_pids, q_camids, g_camids, use_cython=False) 191 | # #print("Results ----------") 192 | # #print("mAP: {:.1%}".format(mAP)) 193 | # #print("CMC curve") 194 | # # for r in ranks: 195 | # # from p#print import p#print 196 | # # #print("Rank-{:<3}: {:.1%}".format(r, cmc[r-1])) 197 | # # #print("------------------") 198 | 199 | #print ("match", match) 200 | if return_distmat: 201 | return distmat 202 | 203 | #print(cmc) 204 | #return ((distmat)) 205 | # img = cv2.imread("hi.jpg") 206 | # deep_reid(img, img) -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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Use with the GNU Affero General Public License. 553 | 554 | Notwithstanding any other provision of this License, you have 555 | permission to link or combine any covered work with a work licensed 556 | under version 3 of the GNU Affero General Public License into a single 557 | combined work, and to convey the resulting work. The terms of this 558 | License will continue to apply to the part which is the covered work, 559 | but the special requirements of the GNU Affero General Public License, 560 | section 13, concerning interaction through a network will apply to the 561 | combination as such. 562 | 563 | 14. Revised Versions of this License. 564 | 565 | The Free Software Foundation may publish revised and/or new versions of 566 | the GNU General Public License from time to time. Such new versions will 567 | be similar in spirit to the present version, but may differ in detail to 568 | address new problems or concerns. 569 | 570 | Each version is given a distinguishing version number. If the 571 | Program specifies that a certain numbered version of the GNU General 572 | Public License "or any later version" applies to it, you have the 573 | option of following the terms and conditions either of that numbered 574 | version or of any later version published by the Free Software 575 | Foundation. If the Program does not specify a version number of the 576 | GNU General Public License, you may choose any version ever published 577 | by the Free Software Foundation. 578 | 579 | If the Program specifies that a proxy can decide which future 580 | versions of the GNU General Public License can be used, that proxy's 581 | public statement of acceptance of a version permanently authorizes you 582 | to choose that version for the Program. 583 | 584 | Later license versions may give you additional or different 585 | permissions. However, no additional obligations are imposed on any 586 | author or copyright holder as a result of your choosing to follow a 587 | later version. 588 | 589 | 15. Disclaimer of Warranty. 590 | 591 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY 592 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT 593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY 594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, 595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 596 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM 597 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF 598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 599 | 600 | 16. Limitation of Liability. 601 | 602 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING 603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS 604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY 605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE 606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF 607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD 608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), 609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF 610 | SUCH DAMAGES. 611 | 612 | 17. Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | --------------------------------------------------------------------------------