dist.py

import cv2
import numpy as np
import math

# Load Yolo
print("LOADING YOLO")
net = cv2.dnn.readNet("D:\Downloads\yolov3.weights", "F:\darknet-master\darknet-master\cfg\yolov3.cfg")
#save all the names in file o the list classes
classes = []
with open("F:\darknet-master\darknet-master\data\coco.names", "r") as f:
    classes = [line.strip() for line in f.readlines()]
#get layers of the network
layer_names = net.getLayerNames()
#Determine the output layer names from the YOLO model 
output_layers = [layer_names[i-1] for i in net.getUnconnectedOutLayers()]
print("YOLO LOADED")

# Capture frame-by-frame
img = cv2.imread("F:\huandbear.jpg")
# img = cv2.resize(img, None, fx=0.4, fy=0.4)
height, width, channels = img.shape

    # USing blob function of opencv to preprocess image
blob = cv2.dnn.blobFromImage(img, 1 / 255.0, (416, 416),
     swapRB=True, crop=False)
    #Detecting objects
net.setInput(blob)
outs = net.forward(output_layers)

    # Showing informations on the screen
class_ids = []
confidences = []
boxes = []
for out in outs:
        for detection in out:
            scores = detection[5:]
            class_id = np.argmax(scores)
            
            confidence = scores[class_id]
            if confidence > 0.5:
                # Object detected
                center_x = int(detection[0] * width)
                center_y = int(detection[1] * height)
                w = int(detection[2] * width)
                h = int(detection[3] * height)

                # Rectangle coordinates
                x = int(center_x - w / 2)
                y = int(center_y - h / 2)

                boxes.append([x, y, w, h])
                confidences.append(float(confidence))
                class_ids.append(class_id)
            
print(boxes[0])
print(boxes[1])
print(boxes[2])
print(boxes[3])
k = len(boxes)

i=0
dist = []

for i in range(0, k):
 if (class_id[i]!=class_id[i+1]):
  dist_btw = (boxes[i][0]-boxes[i+1][0])**2 +(boxes[i][1]-boxes[i+1][1])**2
  dist.append(dist_btw)
 else:
      continue
i=i+1




print(math.sqrt(dist))
if math.sqrt(dist) < 110:
        print("Alert")
else:
        print(math.sqrt(dist))
    
    #We use NMS function in opencv to perform Non-maximum Suppression
    #we give it score threshold and nms threshold as arguments.
indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
colors = np.random.uniform(0, 255, size=(len(classes), 3))
for i in range(len(boxes)):
        if i in indexes:
            x, y, w, h = boxes[i]
            label = str(classes[class_ids[i]])
            color = colors[class_ids[i]]
            cv2.rectangle(img, (x, y), (x + w, y + h), color, 2)
            cv2.putText(img, label, (x, y -5),cv2.FONT_HERSHEY_SIMPLEX,
			1/2, color, 2)

cv2.imshow("Image",img)
cv2.waitKey(0)