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Detect.py
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Detect.py
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import argparse
import sys
import time
import cv2
from tflite_support.task import core
from tflite_support.task import processor
from tflite_support.task import vision
import tflite_support
import numpy as np
import math
from trackeable import TrackableObject
from datetime import datetime
from upload import GoogleSheet
counter, fps = 0, 0
fps_avg_frame_count = 10
start_time = time.time()
cap = cv2.VideoCapture('TestVideo/TestVideo.mp4')#0)
width = 640#int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = 380#int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
cap.set(cv2.CAP_PROP_FRAME_WIDTH, width)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, height)
base_options = core.BaseOptions(file_name = 'models/Dataset2000/custommodel00.tflite')#'models/custommodel.tflite')
detection_options = processor.DetectionOptions(max_results = 100, score_threshold = 0.35) ### cambiar
options = vision.ObjectDetectorOptions(base_options = base_options, detection_options = detection_options)
detector = vision.ObjectDetector.create_from_options(options)
centerPointsPrevFrame = []
trackingObjects = {}
trackId = 0
roi_position_entry = 0.50 #rigth
roi_position_exit = 0.50 #left
position = [0,0,0,0] #left, right, up, down;
trackableobject = {}
Eje = True # x = True, y = False
sheet = GoogleSheet()
sheet.lengthLeftRigth()
while True:
if trackId > 99:
trackId = 0
trackableobject = {}
_, frame = cap.read()
counter +=1
objects = []
centerPointsCurFrame = []
frame = cv2.flip(frame, 1)
frame = cv2.resize(frame, (width,height))
rgb_image = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
#print(rgb_image)
input_tensor = vision.TensorImage.create_from_array(rgb_image)
detection_result = detector.detect(input_tensor)
for detection in detection_result.detections:
category = detection.categories[0]
category_name = category.category_name
if category_name == 'person':
probability = round(category.score, 2)
bbox = detection.bounding_box
#print(bbox)
star_point = bbox.origin_x, bbox.origin_y
end_point = bbox.origin_x + bbox.width, bbox.origin_y + bbox.height
cv2.rectangle(frame, star_point, end_point, (0,255,0), 2)
result_text = category_name + '('+str(probability)+')'
text_location = (bbox.origin_x + 10, -10 + bbox.origin_y)
cv2.putText(frame, result_text, text_location, cv2.FONT_HERSHEY_PLAIN, 1, (255,255,255), 2)
xmin = star_point[0]
xmax = end_point[0]
ymin = star_point[1]
ymax = end_point[1]
xcenter = xmin + (int(round((xmax - xmin )/ 2)))
ycenter = ymin + (int(round((ymax - ymin )/ 2)))
#cv2.circle(frame, (xcenter, ycenter), 5, (0,255,0), -1)
centerPointsCurFrame.append((xcenter, ycenter))
objects.append((xmin,ymin,xmax,ymax))
if counter <= 2:
for pt in centerPointsCurFrame:
for pt2 in centerPointsPrevFrame:
distance = math.hypot(pt2[0] - pt[0], pt2[1]-pt[1])
if distance < 10:
trackingObjects[trackId] = pt
trackId += 1
else:
trackingObjects_copy = trackingObjects.copy()
centerPointsCurFrame_copy = centerPointsCurFrame.copy()
for objectId, pt2 in trackingObjects_copy.items():
object_exists = False
for pt in centerPointsCurFrame_copy:
distance = math.hypot(pt2[0] - pt[0], pt2[1]-pt[1])
if distance < 20:
trackingObjects[objectId] = pt
object_exists = True
if pt in centerPointsCurFrame:
centerPointsCurFrame.remove(pt)
continue
if not object_exists:
trackingObjects.pop(objectId)
for pt in centerPointsCurFrame:
trackingObjects[trackId] = pt
trackId +=1
counted = False
for objectId, pt in trackingObjects.items():
to = trackableobject.get(objectId, None)
if to is None:
to = TrackableObject(objectId, pt)
else:
if Eje and not to.counted:
Xpos = [c[0] for c in to.centroids]
direction = pt[0] - np.mean(Xpos)
if pt[0] > roi_position_entry*width and direction > 0 and np.mean(Xpos) < roi_position_entry*width:
position[1] += 1
to.counted = True
now = datetime.now().strftime('%H:%M:%S')
sheet.sendData('Entry', now)
sheet.lenRight += 1
elif pt[0] < roi_position_exit*width and direction < 0 and np.mean(Xpos) > roi_position_exit*width:
#elif pt[0] < roi_position_entry*width and direction < 0 and np.mean(Xpos) > roi_position_entry*width:
position[0] += 1
to.counted = True
now = datetime.now().strftime('%H:%M:%S')
sheet.sendData('Exit', now)
sheet.lenLeft += 1
to.centroids.append(pt)
trackableobject[objectId] = to
cv2.circle(frame, pt, 5, (0,255,0), -1)
cv2.putText(frame, str(objectId), (pt[0], pt[1] - 7),0, 1, (0, 0, 255), 2)
if counter % fps_avg_frame_count == 0:
end_time = time.time()
fps = fps_avg_frame_count / (end_time - start_time)
start_time = time.time()
fps_text = 'FPS = {:.1f}'.format(fps)
cv2.putText(frame, fps_text, (24,20), cv2.FONT_HERSHEY_PLAIN, 1, (255,255,255), 1)
cv2.line(frame, (int(roi_position_entry*width), 0),(int(roi_position_entry*width), height), (255, 0, 0), 5)
cv2.line(frame, (int(roi_position_exit*width), 0),(int(roi_position_exit*width), height), (0, 0, 255), 5)
cv2.putText(frame, f'Entrada:{sheet.lenRight}; Salida: {sheet.lenLeft}',(10,35), 2,1, (0, 0, 0), 2, cv2.FONT_HERSHEY_SIMPLEX )
cv2.imshow('frame', frame)
#cv2.imshow('rgb', rgb_image)
centerPointsPrevFrame = centerPointsCurFrame.copy()
if cv2.waitKey(1) == 27:
break
cap.release()
cv2.destroyAllWindows()