def analyzing():
# get upload video
in_vlist = session.get("upload_video")
# Perform video tracking and write information to database
# clear the graph
K.clear_session()
# perform video_tracking
det_list, logfile, tracking_list = video_tracking(in_vlist, download_dir,
td_dir, log_dir)
# assign det_list to out_vlist for downloading
out_vlist = [os.path.basename(f) for f in det_list]
# write log data to database
log = configparser.ConfigParser()
log.read(logfile)
log_section = log.sections()
for section in log_section:
log_data = dict(log.items(section))
db.upsert_data(table_name="log", **log_data)
# Save video's heat_map to database
for in_video in in_vlist:
# create heat map for the video
heatmap_path = create_heatmap(in_video, heatmap_dir)
# read heatmap file as binary
heatmap_binary = open(heatmap_path, "rb").read()
# prepare data to write to overlay table
heatmap_data = {
"video_name": os.path.basename(in_video),
"file_extension": "jpg",
"heatmap_binary": psycopg2.Binary(heatmap_binary)
}
# write heat map data to database
db.upsert_data("heatmap", **heatmap_data)
# save pedestrian' activity data to database
for video_path, tracking_path in zip(in_vlist, tracking_list):
num_pedestrians, pedestrian_info = activity_analyzing(tracking_path)
video_name = "_".join(os.path.basename(video_path).split("."))
db.create_table(table_name=video_name)
for person in pedestrian_info:
print(person)
db.upsert_data(table_name=video_name,
condition="person_id",
**person)
return render_template("result.html", list=out_vlist)
def update_heatmap(title, hm_click):
#optional space for creating e.g. RESET button
return create_heatmap(title, hm_click)
machine_learning_table = ColumnDataSource(data=dict())
ML_table = DataTable(source=machine_learning_table,
columns=churn_table_columns,
row_headers=False,
editable=True,
width=280,
height=160)
##############################################################################
# Create heatmap
##########################
from heatmap import create_heatmap
hm = create_heatmap(customer_directory_df)
##############################################################################
# Linear regression plot
################################
import numpy as np
from bokeh.plotting import *
from bokeh.models import ColumnDataSource, DatetimeTickFormatter, NumeralTickFormatter, PrintfTickFormatter
from math import pi
from bokeh.models.glyphs import Line
# TODO: put this in maprdb and query it with Drill
DATA_FILE = "/home/mapr/customer360/bokeh/datasets/credit_card_transactions.csv"
txndf = pd.read_csv(DATA_FILE, thousands=',')
txndf['Date'] = pd.to_datetime(txndf['Date'], format='%m/%d/%Y')
def detect_referee(hg_matrix):
global referee_position
global input_ball
global flag
#İlk açılan pencereler
win_Settings(0)
#Hakem rengi belirlemek için
low_color = np.array([35, 0, 150])
high_color = np.array([85, 170, 255])
#Daha önce oluşturulan saha içi maske
area_mask = cv2.imread(field_mask_path)
cap = cv2.VideoCapture(vid_filepath)
#Hakemin ilk konumunu belirlemek ve konum bulmak için pencere boyutları
resize_x = 40
resize_y = 30
frame = set_object_pos(cap)
frame = init_frames(frame, resize_x, resize_y)
x = 0
y = 0
w = 0
h = 0
#Top_view'e konum atmak için arraylar
input_pts = []
input_referee = [[0, 0], 'r']
input_ball = [[0, 0], 'null']
feet_coord = [0, 0]
#Top_View boyutlarında ısı haritası oluşturma
heatmap.create_heatmap()
heat_key = True
mask_key = False
#-Hakemin her iki saniyedeki hızı
print('Starting..\n\n')
print(
'Click anywhere on Main_Frame to determine the distance.\n L-Mouse: Choose point\n R-Mouse: Remove point'
)
second = 2
rt = speed.RepeatedTimer(second, speed.flag)
#----- Hakemin tespit edilip işaretlendiği kısım ---------
while True:
ret, frame = cap.read()
if ret:
area_masked = cv2.bitwise_and(frame, area_mask)
cropped_frame_referee = area_masked[resized_r[0]:resized_r[1],
resized_r[2]:resized_r[3]]
#Maskeleme işlemleri
masked = image_masks(cropped_frame_referee, low_color, high_color)
image, contours, hierarchy = cv2.findContours(
masked, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
if (len(contours) <= 0):
cv2.rectangle(cropped_frame_referee, (x, y), (x + w, y + h),
(0, 0, 255), 2)
cv2.rectangle(frame, (x + resized_r[2], y + resized_r[0]),
(x + w + resized_r[2], y + h + resized_r[0]),
(0, 255, 0), 2)
else:
for contour in contours:
(x, y, w, h) = cv2.boundingRect(contour)
if ((w < 22 or h < 20) or (w > 40 or h > 45)): continue
feet_coord = [
float((x + resized_r[2]) + int(25 / 2.0)),
float((y + resized_r[0]) + 25)
]
# feet_coord = [x + w/2, y + h)]
input_referee = [feet_coord, 'r']
cv2.rectangle(cropped_frame_referee, (x, y),
(x + w, y + h), (0, 0, 255), 2)
cv2.rectangle(frame, (x + resized_r[2], y + resized_r[0]),
(x + w + resized_r[2], y + h + resized_r[0]),
(0, 255, 0), 2)
if (x != 0 and y <= 5): set_resized_r(0, frame_lenx)
if (y >= resize_x * 2 - h - 5):
set_resized_r(1, frame_lenx)
if (x <= 5 and y != 0): set_resized_r(2, frame_lenx)
if (x >= resize_y * 2 - w - 5):
set_resized_r(3, frame_lenx)
cv2.setMouseCallback('Main_Frame', on_mouse_click,
[x + resized_r[2], y + resized_r[0]])
input_pts = [input_referee, input_ball]
top_img, player_top_points = top_view.create_topview(
hg_matrix, input_pts)
heatmap_mask = heatmap.add(player_top_points[0][0])
ref_speed = speed.cal_speed(time.localtime().tm_sec,
player_top_points[0][0])
top_img = drawoffside.drawspeed(top_img, player_top_points,
ref_speed)
if (player_top_points[1][1][0] == 'b'):
rb_distance = distance.cal_distance(player_top_points)
top_img = drawoffside.drawLine(top_img, player_top_points,
rb_distance)
cv2.imshow('Main_Frame', frame)
cv2.imshow('Top_View', top_img)
#Gereksiz kalabalık m tuşuna basınca gözükmesi ve kapanması için
if mask_key:
win_Settings(1)
cv2.imshow('Mask_Frame', masked)
cv2.imshow('Cropped_Frame', cropped_frame_referee)
else:
cv2.destroyWindow('Mask_Frame')
cv2.destroyWindow('Cropped_Frame')
else:
print("ret, false, video bitti")
win_Settings(2)
colored_heatmap = heatmap.colorize(heatmap_mask)
cv2.imshow('Heat_Map_Mask', heatmap_mask)
cv2.imshow('Heat_Map_Color', colored_heatmap)
#Tuş takımı
key = cv2.waitKey(15) & 0xFF
if key == ord('h'):
if heat_key:
win_Settings(2)
colored_heatmap = heatmap.colorize(heatmap_mask)
cv2.imshow('Heat_Map_Mask', heatmap_mask)
cv2.imshow('Heat_Map_Color', colored_heatmap)
heat_key = False
else:
heat_key = True
cv2.destroyWindow('Heat_Map_Mask')
cv2.destroyWindow('Heat_Map_Color')
if key == ord('m'):
if mask_key:
mask_key = False
else:
mask_key = True
if key == ord('q'):
break
rt.stop()
cap.release()
cv2.destroyAllWindows()