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NNHandler_handshake.py
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NNHandler_handshake.py
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import numpy as np
import json
import os, sys
# from PIL import Image
import matplotlib.pyplot as plt
from collections import defaultdict
import argparse
from NNHandler_yolo import NNHandler_yolo
from NNHandler_image import NNHandler_image, cv2
from Graph import Graph
from suren.util import get_iou, Json, eprint, iou_batch
class NNHandler_handshake(NNHandler_yolo):
# yolo_dir = os.path.dirname(os.path.realpath(__file__)) + "/submodules/yolov4-deepsort"
# model_filename = yolo_dir + '/model_data/mars-small128.pb'
weigths_filename = NNHandler_yolo.yolo_dir + '/checkpoints/yolov4-fullshake_best'
class_names = ["Handshake"]
# Definition of the parameters
max_cosine_distance = 0.4
nn_budget = None
nms_max_overlap = 1.0
iou_thresh = .45
score_thresh = .2
input_size = 416
@staticmethod
def plot(img, points, is_tracked):
if is_tracked:
bb_dic = points
else:
bb_dic = points["bboxes"]
for bbox in bb_dic:
x_min, x_max, y_min, y_max = map(int, [bbox["x1"], bbox["x2"], bbox["y1"], bbox["y2"]])
cv2.rectangle(img, (x_min, y_min), (x_max, y_max), (255, 255, 255), 2)
def __init__(self, handshake_file=None, is_tracked=True, vis=False, verbose=True, debug=False):
super().__init__(json_file=handshake_file, is_tracked=is_tracked, vis=vis, verbose=verbose, debug=debug)
print("\t[*] Handshake detector")
def update_handshake(self, start_time=None, end_time = None):
if start_time is None: start_time = 0
if end_time is None: end_time = self.time_series_length
# Use self.graph and find the two people using maximum intersection area
graph = self.graph
handshake_data = self.json_data
# assert self.time_series_length == self.graph.time_series_length, \
# "Both files (yolo and graph) must be of same length :/ (%d, %d)" % (
# self.time_series_length, self.graph.time_series_length)
# This is going to be inefficient:
# Graph contains nodes which have time series info for separate nodes
# YOLO output has timeseries info first and then info of each node for that time series
'''
if self.is_tracked:
shakes = defaultdict(dict)
for t in handshake_data:
# First take all the detected nodes at time t
t_ = int(t)
node_t = []
node_ind = []
for ind, node in enumerate(graph.nodes):
if node.params["detection"][t_]:
node_t.append([node.params["xMin"][t_], node.params["yMin"][t_], node.params["xMax"][t_], node.params["yMax"][t_]])
node_ind.append(ind)
# Next consider all handshake boxes at time t
for bbox in handshake_data[t]:
bb_hs = [bbox["x1"], bbox["y1"], bbox["x2"], bbox["y2"]]
idx = bbox["id"]
# iou between bb_hs and bb_person (node_t)
iou = [] # @suren : TODO : Batch IOU
for i in range(len(node_t)):
try:
iou.append(get_iou(bb_hs, node_t[i], mode=1))
except Exception as e:
print(e)
print(t, i)
input("Enter something")
# iou = list(map(lambda x: get_iou(bb_hs, x, mode=1), node_t))
shakes[idx][int(t)] = iou
if -1 in shakes:
unclassified = shakes.pop(-1) # non-id shakes
# print(shakes)
for idx in shakes:
shake_t = shakes[idx].keys()
shake_iou = list(shakes[idx].values())
shakes_iou_avg = np.mean(np.array(shake_iou), axis=0).astype(float)
# print(shakes_iou_avg)
p1, p2 = np.argpartition(shakes_iou_avg, -2)[-2:]
p1, p2 = int(p1), int(p2)
for t in shake_t:
graph.nodes[p1].params["handshake"][t] = {"person": p2, "confidence": None, "iou": shakes_iou_avg[p1]}
graph.nodes[p2].params["handshake"][t] = {"person": p1, "confidence": None, "iou": shakes_iou_avg[p2]}
graph.state["handshake"] = 3
else:
for t in handshake_data:
# First take all the detected nodes at time t
t_ = int(t)
node_t = []
node_ind = []
for ind, node in enumerate(graph.nodes):
if node.params["detection"][t_]:
node_t.append([node.params["xMin"][t_], node.params["yMin"][t_], node.params["xMax"][t_], node.params["yMax"][t_]])
node_ind.append(ind)
# Next consider all handshake boxes at time t
# nbox = handshake_data[str(t)]["No of boxes"]
# print(t, node_t)
# for bbox in handshake_data[t]["bboxes"]: @ Suren : This is a temp edit
for bbox in handshake_data[t]:
bb_hs = [bbox["x1"], bbox["y1"], bbox["x2"], bbox["y2"]]
conf = bbox["conf"]
# iou between bb_hs and bb_person (node_t)
# TODO : Update to combine this @ suren --> L1
iou = []
for i in range(len(node_t)):
try:
iou.append(get_iou(bb_hs, node_t[i], mode=1))
except Exception as e:
print(e)
print(t, i)
input("Enter something")
# iou = list(map(lambda x: get_iou(bb_hs, x, mode=1), node_t))
# get 2 max values
ind1, ind2 = np.argpartition(iou, -2)[-2:]
p1, p2 = node_ind[ind1], node_ind[ind2]
# print(t, p1, p2, iou)
graph.nodes[p1].params["handshake"][t_] = {"person": p2, "confidence": conf, "iou": iou[ind1]}
graph.nodes[p2].params["handshake"][t_] = {"person": p1, "confidence": conf, "iou": iou[ind2]}
graph.state["handshake"] = 2
'''
if self.is_tracked:
shakes = defaultdict(dict)
for t in handshake_data:
t_ = int(t)
if not (start_time <= t_ < end_time): continue
t_ -= start_time
# First take all the detected nodes at time t
node_t = [[node.params["xMin"][t_],
node.params["yMin"][t_],
node.params["xMax"][t_],
node.params["yMax"][t_]] for node in graph.nodes]
# Next consider all handshake boxes at time t
for i, bbox in enumerate(handshake_data[t]):
bb_hs = [bbox["x1"], bbox["y1"], bbox["x2"], bbox["y2"]]
if self.is_tracked:
idx = bbox["id"]
else:
idx = i # Random index to each HS
# iou between bb_hs and bb_person (node_t)
iou = iou_batch([bb_hs], node_t)
# print(iou) #, np.array(iou).shape)
# iou = list(map(lambda x: get_iou(bb_hs, x, mode=1), node_t))
shakes[idx][t_] = iou[0]
if -1 in shakes:
unclassified = shakes.pop(-1) # non-id shakes
# print(shakes)
if self.is_tracked:
for idx in shakes:
shake_t = shakes[idx].keys()
shake_iou = list(shakes[idx].values())
# print(idx, shakes[idx])
shakes_iou_avg = np.mean(np.array(shake_iou), axis=0).astype(float)
# print(shakes_iou_avg)
p1, p2 = np.argpartition(shakes_iou_avg, -2)[-2:]
p1, p2 = int(p1), int(p2)
# print(graph.nodes)
for t in shake_t:
# print(t, p1, p2)
# print(graph.nodes[p1].params["handshake"])
graph.nodes[p1].params["handshake"][t] = {"person": p2, "confidence": None, "iou": shakes_iou_avg[p1]}
graph.nodes[p2].params["handshake"][t] = {"person": p1, "confidence": None, "iou": shakes_iou_avg[p2]}
graph.state["handshake"] = 3
else:
for idx in shakes:
for t in shakes[idx]:
t_ = int(t)
iou = shakes[idx][t]
p1, p2 = np.argpartition(iou, -2)[-2:]
p1, p2 = int(p1), int(p2)
graph.nodes[p1].params["handshake"][t_] = {"person": p2, "confidence": None, "iou": iou[p1]}
graph.nodes[p2].params["handshake"][t_] = {"person": p1, "confidence": None, "iou": iou[p2]}
graph.state["handshake"] = 2
print("[*] HS_handler : Updated the graph")
def runForBatch(self, start_time=None, end_time = None):
self.update_handshake(start_time, end_time)
if __name__ == "__main__":
json_loc = "./data/labels/DEEE/handshake/cctv1.json"
img_loc = "./data/videos/DEEE/cctv1.mp4"
parser = argparse.ArgumentParser()
parser.add_argument("--input_file", "-i", type=str, dest="input_file", default=img_loc)
parser.add_argument("--output_file", "-o", type=str, dest="output_file", default=json_loc)
parser.add_argument("--overwrite", "--ow", action="store_true", dest="overwrite")
parser.add_argument("--visualize", "--vis", action="store_true", dest="visualize")
parser.add_argument("--verbose", "--verb", action="store_true", dest="verbose")
parser.add_argument("--tracked", "-t", type=bool, dest="tracked", default=True)
args = parser.parse_args()
args.overwrite = True
img_loc = args.input_file
json_loc = args.output_file
# TEST
img_handle = NNHandler_image(format="avi", img_loc=img_loc)
img_handle.runForBatch()
hs_handle = NNHandler_handshake(vis=args.visualize, is_tracked=args.tracked)
if os.path.exists(json_loc) and not args.overwrite:
# To load YOLO + DSORT track from json
hs_handle.init_from_json(json_loc)
else:
# To create YOLO + DSORT track and save to json
hs_handle.create_yolo(img_handle)
hs_handle.save_json(json_loc)
# g = Graph()
# # graph_json = './data/vid-01-graph_handshake_track.json'
#
# try:
# if os.path.exists(graph_json):
#
# # init graph from json
# g.init_from_json(graph_json)
# else:
# raise Exception("Json does not exists : %s"%graph_json)
# except:
# hs_handle.connectToGraph(g)
# hs_handle.runForBatch()
#
# print(g)
# g.saveToFile('./data/vid-01-graph_handshake_track.json')
#
# g.plot()