tryingwithvideo.py

#############################################
# Live Stream 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('-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()

def get_output_layers(net):
    layer_names = net.getLayerNames()
    try:
        output_layers = [layer_names[i - 1] for i in net.getUnconnectedOutLayers()]
    except:
        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)

# OpenCV VideoCapture for live stream from camera
cap = cv2.VideoCapture(0)  # 0 corresponds to the default camera (can be changed based on your setup)

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)

while True:
    ret, frame = cap.read()

    if not ret:
        print("Error: Couldn't read frame from the camera.")
        break

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

    blob = cv2.dnn.blobFromImage(frame, 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 = 0.5
    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)

    for i in indices:
        try:
            box = boxes[i]
        except:
            i = i[0]
            box = boxes[i]

        x = box[0]
        y = box[1]
        w = box[2]
        h = box[3]
        draw_prediction(frame, class_ids[i], confidences[i], round(x), round(y), round(x+w), round(y+h))

    cv2.imshow("object detection", frame)
    
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()