Project 11

import cv2
import numpy as np

pip install tensorflow

import tensorflow

#Load YOLO Algorithm
net=cv2.dnn.readNet("yolov3.weights","yolov3.cfg")

#To load all objects that have to be detected
classes=[]
with open("coco.names","r") as f:
read=f.readlines()
for i in range(len(read)):
classes.append(read[i].strip("\n"))

#Defining layer names
layer_names=net.getLayerNames()
output_layers=[]
for i in net.getUnconnectedOutLayers():
output_layers.append(layer_names[i-1])

#Loading the Image
img=cv2.imread("dog.jpg")
height,width,channels=img.shape

#Extracting features to detect objects
blob=cv2.dnn.blobFromImage(img,0.00392,(416,416),(0,0,0),True,crop=False)
#Inverting blue with red
#bgr->rgb

#We need to pass the img_blob to the algorithm
net.setInput(blob)
outs=net.forward(output_layers)
#print(outs)

#Displaying informations on the screen
class_ids=[]
confidences=[]
boxes=[]
for output in outs:
for detection in output:
#Detecting confidence in 3 steps
scores=detection[5:] #1
class_id=np.argmax(scores) #2
confidence =scores[class_id] #3

if confidence >0.5: #Means if the object is detected
center_x=int(detection[0]*width)
center_y=int(detection[1]*height)
w=int(detection[2]*width)
h=int(detection[3]*height)

#Drawing a rectangle
x=int(center_x-w/2) # top left value
y=int(center_y-h/2) # top left value

boxes.append([x,y,w,h])
confidences.append(float(confidence))
class_ids.append(class_id)
#cv2.rectangle(img,(x,y),(x+w,y+h),(0,255,0),2)

#Removing Double Boxes
indexes=cv2.dnn.NMSBoxes(boxes,confidences,0.3,0.4)

for i in range(len(boxes)):
if i in indexes:
x, y, w, h = boxes[i]
label = classes[class_ids[i]] # name of the objects
cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)
cv2.putText(img, label, (x, y), cv2.FONT_HERSHEY_PLAIN, 1, (0, 0, 255), 2)

cv2.imshow("Object_detected",img)
cv2.waitKey(0)
cv2.destroyAllWindows()