app.py

# import all the required packages for implementing the yolo model
import cv2
import argparse
import numpy as np

# handle command line arguments
ap = argparse.ArgumentParser()
#add the arguments to the argument parser
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')
arguments = ap.parse_args()

#Preparing for the input of the model


image = cv2.imread(arguments.images) # this will read the input image from the folder 

Width = image.shape[1]
Height = image.shape[0]
scale = 0.00392

# read class names from text file
classes = None
with open(args.classes, 'r') as f:
    classes = [line.strip() for line in f.readlines()]

# generate different colors for different classes 
COLORS = np.random.uniform(0, 255, size=(len(classes), 3))

# read pre-trained model and config file
net = cv2.dnn.readNet(args.weights, args.config)

# create input blob 
blob = cv2.dnn.blobFromImage(image, scale, (416,416), (0,0,0), True, crop=False)

# set input blob for the network
net.setInput(blob)

net = cv.dnn.readnet(arguments.weights,arguments.configs)
blob = cv2.dnn.blobFromImage(image, scale, (Width,Height), (0,0,0), True, crop=False)
net.setInput(blob)

#Preparing for the output of the model

# function to get the output layer names 
# in the architecture
def get_output_layers(net):
    
    layer_names = net.getLayerNames()
    
    output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]

    return output_layers

# function to draw bounding box on the detected object with class name
def draw_bounding_box(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)

#Running for the inference of the model
# run inference through the network
# and gather predictions from output layers



outs = net.forward(get_output_layers(net))

# initialization
class_ids = []
confidences = []
boxes = []
conf_threshold = 0.5
nms_threshold = 0.4

# for each detetion from each output layer 
# get the confidence, class id, bounding box params
# and ignore weak detections (confidence < 0.5)
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])

# apply non-max suppression
indices = cv2.dnn.NMSBoxes(boxes, confidences, conf_threshold, nms_threshold)

# go through the detections remaining
# after nms and draw bounding box
for i in indices:
    i = i[0]
    box = boxes[i]
    x = box[0]
    y = box[1]
    w = box[2]
    h = box[3]
    
    draw_bounding_box(image, class_ids[i], confidences[i], round(x), round(y), round(x+w), round(y+h))

# display output image    
cv2.imshow("object detection", image)

# wait until any key is pressed
cv2.waitKey()
    
 # save output image to disk
cv2.imwrite("object-detection.jpg", image)

# release resources
cv2.destroyAllWindows()