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yolo_opencv.py
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yolo_opencv.py
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#############################################
# Object detection - YOLO - OpenCV
# Author : Arun Ponnusamy (July 16, 2018)
# Website : http://www.arunponnusamy.com
############################################
import cv2
import argparse
import numpy as np
ap = argparse.ArgumentParser()
ap.add_argument('-i', '--image', required=True,
help = 'path to input image')
ap.add_argument('-c', '--config', required=True,
help = 'path to yolo config file')
ap.add_argument('-w', '--weights', required=True,
help = 'path to yolo pre-trained weights')
ap.add_argument('-cl', '--classes', required=True,
help = 'path to text file containing class names')
args = ap.parse_args()
#open output text file
number_cars = open("./caroutput/number_cars.txt", "w")
space_available = [True, True, True, True, True]
#this is the formatting for the specfic image and car parking space
space_min_h = 2100
space_max_h = 2900
space_1_min_x = 70
space_1_max_x = 750
space_2_min_x = 751
space_2_max_x = 1300
space_3_min_x = 1301
space_3_max_x = 2040
space_4_min_x = 2041
space_4_max_x = 2800
space_5_min_x = 2801
space_5_max_x = 2900
def get_output_layers(net):
layer_names = net.getLayerNames()
output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
return output_layers
def draw_prediction(img, class_id, confidence, x, y, x_plus_w, y_plus_h):
label = str(classes[class_id])
color = COLORS[class_id]
cv2.rectangle(img, (x,y), (x_plus_w,y_plus_h), color, 2)
cv2.putText(img, label, (x-10,y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
def image_output(img, x, y, x_plus_w, y_plus_h, space):
crop_img = img[y:y_plus_h, x:x_plus_w]
cv2.imwrite("./caroutput/car_space_%s.jpg" %(space), crop_img)
def available_space( img, x, y, x_plus_w, y_plus_h):
object_location_x = (x_plus_w + x)/2
object_location_y = (y_plus_h + y)/2
if object_location_y > space_min_h and object_location_y < space_max_h:
if object_location_x > space_1_min_x and object_location_x < space_1_max_x:
space = "1"
image_output(img, x, y, x_plus_w, y_plus_h, space)
space_available[0]= False
return space_available
elif object_location_x > space_2_min_x and object_location_x < space_2_max_x:
space = "2"
image_output(img, x, y, x_plus_w, y_plus_h, space)
space_available[1]= False
return space_available
elif object_location_x > space_3_min_x and object_location_x < space_3_max_x:
space = "3"
image_output(img, x, y, x_plus_w, y_plus_h, space)
space_available[2]= False
return space_available
elif object_location_x > space_4_min_x and object_location_x < space_4_max_x:
space = "4"
image_output(img, x, y, x_plus_w, y_plus_h, space)
space_available[3]= False
return space_available
elif object_location_x > space_5_min_x and object_location_x < space_5_max_x:
space = "5"
image_output(img, x, y, x_plus_w, y_plus_h, space)
space_available[4]= False
return space_available
return space_available
image = cv2.imread(args.image)
Width = image.shape[1]
Height = image.shape[0]
scale = 0.00392
classes = None
with open(args.classes, 'r') as f:
classes = [line.strip() for line in f.readlines()]
COLORS = np.random.uniform(0, 255, size=(len(classes), 3))
net = cv2.dnn.readNet(args.weights, args.config)
blob = cv2.dnn.blobFromImage(image, scale, (416,416), (0,0,0), True, crop=False)
net.setInput(blob)
outs = net.forward(get_output_layers(net))
class_ids = []
confidences = []
boxes = []
conf_threshold = 0.1
nms_threshold = 0.4
for out in outs:
for detection in out:
scores = detection[5:]
class_id = np.argmax(scores)
confidence = scores[class_id]
if confidence > 0.5:
center_x = int(detection[0] * Width)
center_y = int(detection[1] * Height)
w = int(detection[2] * Width)
h = int(detection[3] * Height)
x = center_x - w / 2
y = center_y - h / 2
class_ids.append(class_id)
confidences.append(float(confidence))
boxes.append([x, y, w, h])
indices = cv2.dnn.NMSBoxes(boxes, confidences, conf_threshold, nms_threshold)
car_counter = 0
for i in indices:
i = i[0]
if class_ids[i]==2 :
car_counter = car_counter +1
box = boxes[i]
x = box[0]
y = box[1]
w = box[2]
h = box[3]
draw_prediction(image, class_ids[i], confidences[i], round(x), round(y), round(x+w), round(y+h))
space_available = available_space( image, round(x), round(y), round(x+w), round(y+h))
no_parking = 5
no_free = no_parking - car_counter
cv2.imwrite("./caroutput/object-detection.jpg", image)
#writing the informmation to a .txt file
#number of cars
#number of free spaces
number_cars = open("./caroutput/number_cars.txt", "r+")
number_cars_string = "number of cars: " + str(car_counter)
number_cars.write(number_cars_string)
number_cars_string = "\nnumber of free spaces: " + str(no_free)
number_cars.write(number_cars_string)
number_cars.write("\n"+str(space_available))
number_cars.close()
print("Complete")
cv2.destroyAllWindows()