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Object_Detection.py
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#================================================================
# Import libraries
from ctypes import *
import math
import random
import os
import cv2
import numpy as np
import time
import darknet
import pafy
import youtube_dl
def convertBack(x, y, w, h):
#================================================================
# Purpose : Converts center coordinates to rectangle coordinates
#================================================================
"""
:param:
x, y = midpoint of bbox
w, h = width, height of the bbox
:return:
xmin, ymin, xmax, ymax
"""
xmin = int(round(x - (w / 2)))
xmax = int(round(x + (w / 2)))
ymin = int(round(y - (h / 2)))
ymax = int(round(y + (h / 2)))
return xmin, ymin, xmax, ymax
def cvDrawBoxes(detections, img):
"""
:param:
detections = total detections in one frame
img = image from detect_image method of darknet
:return:
img with bbox
"""
# Colored labels dictionary
color_dict = {
'person' : [0, 255, 255], 'bicycle': [238, 123, 158], 'car' : [24, 245, 217], 'motorbike' : [224, 119, 227],
'aeroplane' : [154, 52, 104], 'bus' : [179, 50, 247], 'train' : [180, 164, 5], 'truck' : [82, 42, 106],
'boat' : [201, 25, 52], 'traffic light' : [62, 17, 209], 'fire hydrant' : [60, 68, 169], 'stop sign' : [199, 113, 167],
'parking meter' : [19, 71, 68], 'bench' : [161, 83, 182], 'bird' : [75, 6, 145], 'cat' : [100, 64, 151],
'dog' : [156, 116, 171], 'horse' : [88, 9, 123], 'sheep' : [181, 86, 222], 'cow' : [116, 238, 87],'elephant' : [74, 90, 143],
'bear' : [249, 157, 47], 'zebra' : [26, 101, 131], 'giraffe' : [195, 130, 181], 'backpack' : [242, 52, 233],
'umbrella' : [131, 11, 189], 'handbag' : [221, 229, 176], 'tie' : [193, 56, 44], 'suitcase' : [139, 53, 137],
'frisbee' : [102, 208, 40], 'skis' : [61, 50, 7], 'snowboard' : [65, 82, 186], 'sports ball' : [65, 82, 186],
'kite' : [153, 254, 81],'baseball bat' : [233, 80, 195],'baseball glove' : [165, 179, 213],'skateboard' : [57, 65, 211],
'surfboard' : [98, 255, 164],'tennis racket' : [205, 219, 146],'bottle' : [140, 138, 172],'wine glass' : [23, 53, 119],
'cup' : [102, 215, 88],'fork' : [198, 204, 245],'knife' : [183, 132, 233],'spoon' : [14, 87, 125],
'bowl' : [221, 43, 104],'banana' : [181, 215, 6],'apple' : [16, 139, 183],'sandwich' : [150, 136, 166],'orange' : [219, 144, 1],
'broccoli' : [123, 226, 195],'carrot' : [230, 45, 209],'hot dog' : [252, 215, 56],'pizza' : [234, 170, 131],
'donut' : [36, 208, 234],'cake' : [19, 24, 2],'chair' : [115, 184, 234],'sofa' : [125, 238, 12],
'pottedplant' : [57, 226, 76],'bed' : [77, 31, 134],'diningtable' : [208, 202, 204],'toilet' : [208, 202, 204],
'tvmonitor' : [208, 202, 204],'laptop' : [159, 149, 163],'mouse' : [148, 148, 87],'remote' : [171, 107, 183],
'keyboard' : [33, 154, 135],'cell phone' : [206, 209, 108],'microwave' : [206, 209, 108],'oven' : [97, 246, 15],
'toaster' : [147, 140, 184],'sink' : [157, 58, 24],'refrigerator' : [117, 145, 137],'book' : [155, 129, 244],
'clock' : [53, 61, 6],'vase' : [145, 75, 152],'scissors' : [8, 140, 38],'teddy bear' : [37, 61, 220],
'hair drier' : [129, 12, 229],'toothbrush' : [11, 126, 158]
}
for detection in detections:
x, y, w, h = detection[2][0],\
detection[2][1],\
detection[2][2],\
detection[2][3]
name_tag = str(detection[0].decode())
for name_key, color_val in color_dict.items():
if name_key == name_tag:
color = color_val
xmin, ymin, xmax, ymax = convertBack(
float(x), float(y), float(w), float(h))
pt1 = (xmin, ymin)
pt2 = (xmax, ymax)
cv2.rectangle(img, pt1, pt2, color, 1)
cv2.putText(img,
detection[0].decode() +
" [" + str(round(detection[1] * 100, 2)) + "]",
(pt1[0], pt1[1] - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5,
color, 2)
return img
netMain = None
metaMain = None
altNames = None
def YOLO():
global metaMain, netMain, altNames
configPath = "./cfg/yolov4.cfg" # Path to cfg
weightPath = "./yolov4.weights" # Path to weights
metaPath = "./cfg/coco.data" # Path to meta data
if not os.path.exists(configPath): # Checks whether file exists otherwise return ValueError
raise ValueError("Invalid config path `" +
os.path.abspath(configPath)+"`")
if not os.path.exists(weightPath):
raise ValueError("Invalid weight path `" +
os.path.abspath(weightPath)+"`")
if not os.path.exists(metaPath):
raise ValueError("Invalid data file path `" +
os.path.abspath(metaPath)+"`")
if netMain is None: # Checks the metaMain, NetMain and altNames. Loads it in script
netMain = darknet.load_net_custom(configPath.encode(
"ascii"), weightPath.encode("ascii"), 0, 1) # batch size = 1
if metaMain is None:
metaMain = darknet.load_meta(metaPath.encode("ascii"))
if altNames is None:
try:
with open(metaPath) as metaFH:
metaContents = metaFH.read()
import re
match = re.search("names *= *(.*)$", metaContents,
re.IGNORECASE | re.MULTILINE)
if match:
result = match.group(1)
else:
result = None
try:
if os.path.exists(result):
with open(result) as namesFH:
namesList = namesFH.read().strip().split("\n")
altNames = [x.strip() for x in namesList]
except TypeError:
pass
except Exception:
pass
#cap = cv2.VideoCapture(0) # Uncomment to use Webcam
#cap = cv2.VideoCapture("Video_for_Testing.mp4") # Uncomment for Local Stored video detection - Set input video
#url = "https://www.youtube.com/watch?v=isveXCH4NcM" # Uncomment these lines for video from youtube
#video = pafy.new(url)
#best = video.getbest(preftype="mp4")
#cap = cv2.VideoCapture()
#cap.open(best.url)
#cap = cv2.VideoCapture('http://192.168.0.102:4747/mjpegfeed') # Uncomment for Video from Mobile Camera (DroidCam Hosted Camera)
frame_width = int(cap.get(3)) # Returns the width and height of capture video
frame_height = int(cap.get(4))
# Set out for video writer
#out = cv2.VideoWriter( # Uncomment to save the output video # Set the Output path for video writer
#"./Demo/output.avi", cv2.VideoWriter_fourcc(*"MJPG"), 10.0,
#(frame_width, frame_height))
print("Starting the YOLO loop...")
# Create an image we reuse for each detect
darknet_image = darknet.make_image(frame_width, frame_height, 3) # Create image according darknet for compatibility of network
while True: # Load the input frame and write output frame.
prev_time = time.time()
ret, frame_read = cap.read() # Capture frame and return true if frame present
# For Assertion Failed Error in OpenCV
if not ret: # Check if frame present otherwise he break the while loop
break
frame_rgb = cv2.cvtColor(frame_read, cv2.COLOR_BGR2RGB) # Convert frame into RGB from BGR and resize accordingly
frame_resized = cv2.resize(frame_rgb,
(frame_width, frame_height),
interpolation=cv2.INTER_LINEAR)
darknet.copy_image_from_bytes(darknet_image,frame_resized.tobytes()) # Copy that frame bytes to darknet_image
detections = darknet.detect_image(netMain, metaMain, darknet_image, thresh=0.25) # Detection occurs at this line and return detections, for customize we can change the threshold.
image = cvDrawBoxes(detections, frame_resized) # Call the function cvDrawBoxes() for colored bounding box per class
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
print(1/(time.time()-prev_time)) # Prints frames per second
cv2.imshow('Demo', image) # Display Image window
cv2.waitKey(3)
#out.write(image) # Write that frame into output video
cap.release() # For releasing cap and out.
#out.release() #Uncomment to save the output video
print(":::Video Write Completed")
if __name__ == "__main__":
YOLO() # Calls the main function YOLO()