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import cv2
from ultralytics import YOLO
import os
from dotenv import load_dotenv
from pathlib import Path
import math
import json
import numpy as np
env_path = Path('.') / '.env'
load_dotenv(dotenv_path=env_path)
path = {
'DET_MODEL_PATH': str(os.getenv('DET_MODEL_PATH')),
'IMG_DIR_PATH': str(os.getenv('IMG_DIR_PATH')),
'ACTIVITY_DET_MODEL_PATH':str(os.getenv('ACTIVITY_DET_MODEL_PATH')),
}
#constants
PERSON_HEIGHT = 1.5
VEHICAL_HEIGHT = 1.35
ANIMAL_HEIGHT = 0.6
FOCAL_LENGTH = 6400
# CONF = 0.0
#Load models
det_model = YOLO(path['DET_MODEL_PATH'])
activity_det_model = YOLO(path['ACTIVITY_DET_MODEL_PATH'])
activity_classes = ['Standing','Running','Sitting']
def object_detection(image):
"""
Args:
image (numpy array): get numpy array of image which has 3 channels
Returns:
new_boxes: returns json object which has below format
[
{
"actual_boundries": [
{
"top_left": [48, 215],
"bottom_right": [62, 245],
"class": "person"
}
],
"updated_boundries": {
"top_left": [41, 199],
"bottom_right": [73, 269],
"person_count": 1,
"vehical_count": 0,
"animal_count": 0
}
}
]
"""
#detect object using yolo model
results = det_model(image)
boxes = results[0].boxes.xyxy.tolist()
classes = results[0].boxes.cls.tolist()
names = results[0].names
confidences = results[0].boxes.conf.tolist()
ctr = 0
my_boxes = [] # ((x1, y1), (x2,y2), person_count, vehical_count, animal_count)
for box, cls, conf in zip(boxes, classes, confidences):
x1, y1, x2, y2 = box
name = names[int(cls)]
my_obj = {"actual_boundries": [{"top_left": (int(x1), int(y1)),
"bottom_right": (int(x2), int(y2)),
"class": name}]}
# img = cv2.imread(img_path)
x1 = max(0, x1 - (x2-x1)/2)
y1 = max(0, y1 - (y2-y1)/2)
x2 = min(len(image[0])-1, x2 + (x2-x1)/2)
y2 = min(len(image)-1, y2 + (y2-y1)/2)
x1, y1, x2, y2 = math.floor(x1), math.floor(y1), math.ceil(x2), math.ceil(y2)
# image = cv2.rectangle(image, (x1, y1), (x2, y2), (255, 0, 0), 2)
my_obj["updated_boundries"] = {"top_left": (x1, y1),
"bottom_right": (x2, y2),
"person_count": 1 if name == 'person' else 0,
"vehical_count": 1 if name == 'vehical' else 0,
"animal_count": 1 if name == 'animal' else 0}
my_boxes.append(my_obj)
ctr += 1
my_boxes.sort(key=lambda x: (x['updated_boundries']['top_left'], x['updated_boundries']['bottom_right']))
new_boxes = []
if len(my_boxes) > 0:
new_boxes.append(my_boxes[0])
for indx, box in enumerate(my_boxes):
if indx != 0:
top_left_last = new_boxes[-1]['updated_boundries']['top_left']
bottom_right_last = new_boxes[-1]['updated_boundries']['bottom_right']
top_left_curr = box['updated_boundries']['top_left']
bottom_right_curr = box['updated_boundries']['bottom_right']
if bottom_right_last[0] >= top_left_curr[0] and bottom_right_last[1] >= top_left_curr[1]:
new_x1 = min(top_left_last[0], top_left_curr[0])
new_y1 = min(top_left_last[1], top_left_curr[1])
new_x2 = max(bottom_right_last[0], bottom_right_curr[0])
new_y2 = max(bottom_right_last[1], bottom_right_curr[1])
new_boxes[-1]['actual_boundries'] += box['actual_boundries']
new_boxes[-1]['updated_boundries'] = {"top_left": (new_x1, new_y1),
"bottom_right": (new_x2, new_y2),
"person_count": new_boxes[-1]['updated_boundries']['person_count'] + box['updated_boundries']['person_count'],
"vehical_count": new_boxes[-1]['updated_boundries']['vehical_count'] + box['updated_boundries']['vehical_count'],
"animal_count": new_boxes[-1]['updated_boundries']['animal_count'] + box['updated_boundries']['animal_count']}
else:
new_boxes.append(box)
return new_boxes
def croped_images(image,new_boxes):
"""_summary_
Args:
image (numpy array): get numpy array of image which has 3 channels
new_boxes (json array): get json array
Returns:
croped_images_list(list of numpy array): returns list which has croped images
single_object_images(list of numpy array): returns list which has single object images
"""
croped_images_list = []
single_object_images = []
for data in new_boxes:
print(data['updated_boundries'])
crop_image = image[data['updated_boundries']['top_left'][1]:data['updated_boundries']['bottom_right'][1],data['updated_boundries']['top_left'][0]:data['updated_boundries']['bottom_right'][0]]
croped_images_list.append(crop_image)
for object in data['actual_boundries']:
if object['class']=='person':
crop_object= image[object['top_left'][1]:object['bottom_right'][1],object['top_left'][0]:object['bottom_right'][0]]
single_object_images.append(crop_object)
return croped_images_list,single_object_images
def image_enhancements(croped_images_list,single_object_images):
"""_summary_
Args:
croped_images_list (list numpy array): croped images list
single_object_images (list numpy array): single object images list
Returns:
enhanced croped images: returns enhanced images
enhanced single_object_images: returns enhanced images
"""
enhanced_images = []
enhanced_single_object_images = []
for image in croped_images_list:
# resize the image
res = cv2.resize(image,(500*image.shape[1]//image.shape[0],500), interpolation = cv2.INTER_CUBIC)
# brightness and contrast
brightness = 16
contrast = 0.95
res2 = cv2.addWeighted(res, contrast, np.zeros(res.shape, res.dtype), 0, brightness)
# Sharpen the image
kernel = np.array([[0, -1, 0], [-1, 5, -1], [0, -1, 0]])
sharpened_image = cv2.filter2D(res2, -1, kernel)
#append in the list
enhanced_images.append(sharpened_image)
for image in single_object_images:
# resize the image
res = cv2.resize(image,(500*image.shape[1]//image.shape[0],500), interpolation = cv2.INTER_CUBIC)
# brightness and contrast
brightness = 16
contrast = 0.95
res2 = cv2.addWeighted(res, contrast, np.zeros(res.shape, res.dtype), 0, brightness)
# Sharpen the image
kernel = np.array([[0, -1, 0], [-1, 5, -1], [0, -1, 0]])
sharpened_image = cv2.filter2D(res2, -1, kernel)
#append enhnaced single object image
enhanced_single_object_images.append(sharpened_image)
return enhanced_images,enhanced_single_object_images
def detect_activity(single_object_images):
"""_summary_
Args:
single_object_images (list of numpy array): list of single object images
Returns:
activities(list of strings): returns list of activities perform by person
"""
activities = []
for img in single_object_images:
predictions =activity_det_model.predict(img)
for result in predictions:
probs = result.probs
class_index = probs.top1
activities.append(activity_classes[class_index])
return activities
def get_distances(new_boxes):
"""_summary_
Args:
new_boxes (json array): takes json array of detected image's data
Returns:
distance_list: list of distances of each object
"""
distance_list = []
for box in new_boxes:
for actual_box in box['actual_boundries']:
height = actual_box['bottom_right'][1] - actual_box['top_left'][1]
if actual_box['class'] == "person":
distance = FOCAL_LENGTH*PERSON_HEIGHT/height
elif actual_box['class'] == "vehical":
distance = FOCAL_LENGTH*PERSON_HEIGHT/height
else:
distance = FOCAL_LENGTH*PERSON_HEIGHT/height
distance_list.append(str(round(distance)) + "m")
return distance_list
def get_json_data(json_data,enhanced_images,detected_activity,distances_list):
"""_summary_
Args:
json_data (json Array): get json data of image
enhanced_images (list of numpy array): list of enhanced images
detected_activity (list of strings): list of activities of person
distances_list (lsit of integers): list of distances of each object
Returns:
results(json Array): contains all informations needed for frontend
{'zoomed_img':np.array([]) ,
'actual_boxes':[],
'updated_boxes':{},
}
"""
results = []
object_count = 0
activity_count = 0
for idx,box in enumerate(json_data):
final_json_output = {'zoomed_img':np.array([]) ,
'actual_boxes':[],
'updated_boxes':{},
}
final_json_output['zoomed_img'] = enhanced_images[idx]
final_json_output['updated_boxes'] = { "top_left": box['updated_boundries']['top_left'],
"bottom_right": box['updated_boundries']['bottom_right']}
for actual_box in box['actual_boundries']:
temp = {"top_left": [],
"bottom_right": [],
"class": "",
"distance":0,
"activity":'none'}
temp['top_left'] = actual_box['top_left']
temp['bottom_right'] = actual_box['bottom_right']
temp['class'] = actual_box['class']
temp['distance'] = distances_list[object_count]
object_count+=1
if temp['class'] == 'person':
temp['activity'] = detected_activity[activity_count]
activity_count+=1
final_json_output['actual_boxes'].append(temp)
final_json_output = fix_distance(final_json_output)
results.append(final_json_output)
return results
def fix_distance(final_json_output):
"""_summary_
Args:
final_json_output (json Array): array of json object
Returns:
final_json_output (json Array): array of json object
"""
distances = []
DIFF = 90
for idx,box in enumerate(final_json_output['actual_boxes']):
distances.append({'idx':idx,'distance':int(box['distance'][:-1])})
sorted_dist = sorted(distances, key=lambda d: d['distance'])
sum_dist = []
idx= 0
sum_dist.append({'sum':sorted_dist[0]['distance'],'idxes':[sorted_dist[0]['idx']]})
for i in range(1,len(sorted_dist)):
print(sorted_dist[i]['distance'],sorted_dist[i-1]['distance'])
if abs(sorted_dist[i]['distance']-sorted_dist[i-1]['distance']) <=DIFF:
sum_dist[idx]['sum']+= sorted_dist[i]['distance']
sum_dist[idx]['idxes'].append(sorted_dist[i]['idx'])
else:
sum_dist.append({'sum':sorted_dist[i]['distance'],'idxes':[sorted_dist[i]['idx']]})
idx+=1
#change values in distance array
for data in sum_dist:
count = len(data['idxes'])
mean = data['sum']//count
for i in data['idxes']:
final_json_output['actual_boxes'][i]['distance'] = str(mean)+'m'
return final_json_output
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