import numpy as np
import cv2 as cv
from google.colab.patches import cv2_imshow
import glob
import os
# Access Files
from google.colab import drive
drive.mount("gdrive") 
# Video to Images
import cv2 as cv
from google.colab.patches import cv2_imshow
video = "" # Path to video file
capture = cv.VideoCapture(video)
isTrue, frame = capture.read()
count = 0
while isTrue:
    if not isTrue:
        break
    isTrue, frame = capture.read()
    cv.imwrite("frame%03d.jpg" %count, frame) # String format '%' is placeholder
    count += 1
# Class Names
importClassNames = "coco.names"
classNames = []
with open(importClassNames, "r") as f: 
    classNames = f.read().split("\n")
# DNN
modelConfiguation = "yolov3.cfg"
modelWeights = "yolov3.weights"
net = cv.dnn.readNet(modelConfiguation, modelWeights)
# Get output layer
def getOutputs(frame):
    blob = cv.dnn.blobFromImage(frame, 1/255, (widthheight, widthheight), [0, 0, 0], 1, crop = False)
    net.setInput(blob)
    layerNames = net.getLayerNames() #gets all names of the layer
    outputNames = [layerNames[i[0] - 1] for i in net.getUnconnectedOutLayers()]
    outputs = net.forward(outputNames) # output is a list containing 3 values, within the list 'numpy'
    return outputs
# Display Box
def displayBox(outputs):
    height, width, channels = frame.shape  
    boundingBox = []   
    classIDs = []      
    confidenceValues = []   
    for output in outputs:
        for values in output:  
            scores = values[5:] 
            classID = np.argmax(scores)  
            confidence = scores[classID] 
            if confidence > confidenceThreshold: 
                w, h = int(values[2] * width), int(values[3] * height) 
                x, y = int(values[0] * width - w/2), int(values[1] * height - h/2)  
                boundingBox.append([x, y, w, h])  
                classIDs.append(classID) 
                confidenceValues.append(float(confidence)) 
    outputBox = cv.dnn.NMSBoxes(boundingBox, confidenceValues, confidenceThreshold, nmsThreshold)   
    for i in outputBox:
        index = i[0]
        color = colors[classIDs[index]]
		x, y, w, h = boundingBox[index][:4]
        cv.rectangle(frame, (x, y), (x + w, y + h), color, 2)
        label = str(classNames[classIDs[index]].capitalize()) + " " + str(int(confidenceValues[index] * 100)) + " %"
        cv.putText(frame, label,(x + 5,y + 16), font, 0.5, color, 2)
#Main Function
widthheight = 320             
confidenceThreshold = 0.5     
nmsThreshold = 0.6            
font = cv.FONT_HERSHEY_SIMPLEX
colors = np.random.uniform(0, 255, size = (len(classNames),3)) 
path = "/content/"
pathFile = path + "*.*"                                    
files = sorted(glob.glob(pathFile), key = os.path.getmtime) 
frameSize = 0.3             
for f in files:
    print(f.strip(path))  
    frame = cv.imread(f)  
    frame = cv.resize(frame, None, fx = frameSize, fy = frameSize)
    displayBox(getOutputs(frame))
    cv2_imshow(frame)