Yolo detection of Objects

# Yolo Object Detection.

### import some libraries

import cv2
import os
import numpy as np
import time
import glob
import matplotlib.pyplot as plt

### load yolo weights and cfg

cfg_path = os.path.join("yolo","yolov3.cfg.txt")
weight_path = os.path.join("yolo","yolov3.weights")

### load the neural net in cv2

net = cv2.dnn.readNetFromDarknet(cfg_path,weight_path)

### get Layers Names

names =net.getLayerNames()


### load the test image







# Importing all necessary libraries
import cv2
import os

# Read the video from specified path
cam = cv2.VideoCapture("harder_challenge_video.mp4")

try:
	
	# creating a folder named data
	if not os.path.exists('data'):
		os.makedirs('data')

# if not created then raise error
except OSError:
	print ('Error: Creating directory of data')

# frame
currentframe = 0

while(True):
	
	# reading from frame
	ret,frame = cam.read()

	if ret:
		# if video is still left continue creating images
		name = './data/frame' + str(currentframe) + '.jpg'


		# writing the extracted images
		cv2.imwrite(name, frame)

		# increasing counter so that it will
		# show how many frames are created
		currentframe += 1
	else:
		break
        

# Release all space and windows once done
cam.release()
cv2.destroyAllWindows()


Frames = []
for i in range(0,currentframe,5):
    img = cv2.imread("data/frame"+ ""+str(i)+".jpg")
    img= cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
    height, width, layers = img.shape
    size = (width,height)
    Frames.append(img)



#plt.imshow(Frames[100])









(H,W)=Frames[20].shape[:2]
layers_names =[names[i-1] for i in net.getUnconnectedOutLayers()]
layers_names

### run the interface on the test image

labels_path = os.path.join("yolo","coco.names.txt")
labels=open (labels_path).read().strip().split("\n")
labels=np.array(labels)
labels=labels[1:8]

for j in range(len(Frames)):
    boxes =[]
    confidences=[]
    classIDs=[]
    x_bef , y_bef  , w_bef , h_bef  = 0 , 0 , 1 , 1
    
    
    blob = cv2.dnn.blobFromImage(Frames[j], 1/255.0, (416,416), crop=False, swapRB=False)
    net.setInput(blob)

    #cal the time of our algo
    start_t =time.time()
    layers_output =net.forward(layers_names)
       
    
    for output in layers_output:
        for dection in output:
            scores = dection[6:13]
            classID =np.argmax(scores)
            confidence=scores[classID]

            if confidence> 0.5 :
                box = dection[:4] * np.array([W,H,W,H])
                bx,by,bw,bh= box.astype("int")

                x=int(bx-(bw/2))
                y=int(by-(bh/2))


                boxes.append([x,y,int(bw),int(bh)])
                confidences.append(float(confidence))
                classIDs.append(classID)
                
                
    
    
    idxs =cv2.dnn.NMSBoxes(boxes,confidences,0.5,0.4)
    if len(idxs) > 0:
    
        for i in idxs.flatten():
            (x,y)=[boxes[i][0],boxes[i][1]]
            (w,h)=[boxes[i][2],boxes[i][3]]
            
            '''
            dx = min(x+w, x_bef+w_bef) - max(x, x_bef)
            dy = min(y+h,y_bef+h_bef) - max(y, y_bef)
            if (dx>=0) and (dy>=0):
                intersection =  dx*dy
            else:
                intersection = 0
            #print(j," ",intersection/(w*h) ,x ," ",y," ",w," ",h," ",x_bef ," ",y_bef," ",w_bef," ",h_bef)
            if((w <= 1000) and ((intersection/(w*h))< 0.75)):
               '''
	    if(w <= 1000):   
                cv2.rectangle(Frames[j],(x,y),(x+w,y+h),(0,255,255),2)
                cv2.putText(Frames[j],"{}"":" "{}".format(labels[classIDs[i]], str(round(confidences[i], 3))),(x,y-5),cv2.FONT_HERSHEY_PLAIN,1.5,(0,139,139),2)
                x_bef , y_bef  , w_bef , h_bef  = x , y , w , h  
    

    



### plot the bounding boxes in the image

#cv2.imshow("image",cv2.cvtColor(Frames[50],cv2.COLOR_BGR2RGB))
#cv2.waitKey(0)

import os
if not os.path.exists("data6"):
    os.makedirs('data6')
for i in range(len(Frames)):
    Frames[i]= cv2.cvtColor(Frames[i],cv2.COLOR_BGR2RGB)
    name = './data6/frame'+ ""+str(i)+ '.jpg'
    cv2.imwrite(name,Frames[i])

import cv2
import numpy as np
import glob

img_array = []
i = 0
#for filename in glob.glob("data5/frame"+ ""+str(i)+".jpg"):
for i in range(len(Frames)):
    img = cv2.imread("data6/frame"+ ""+str(i)+".jpg")
    height, width, layers = img.shape
    size = (width,height)
    img_array.append(img)


out = cv2.VideoWriter('project6.avi',cv2.VideoWriter_fourcc(*'DIVX'), 5 , size)
 
for i in range(len(img_array)):
    out.write(img_array[i])
out.release()