hello.py

import numpy as np
import imutils
import time
import cv2
import os

iconfidence = 0.5
ithreshold = 0.3

labelsPath = os.path.sep.join(["yolo-coco", "coco.names"])
LABELS = open(labelsPath).read().strip().split("\n")

np.random.seed(42)
COLORS = np.random.randint(0, 255, size=(len(LABELS), 3), dtype="uint8")

weightsPath = os.path.sep.join(["yolo-coco", "yolov3.weights"])
configPath = os.path.sep.join(["yolo-coco", "yolov3.cfg"])

net = cv2.dnn.readNetFromDarknet(configPath, weightsPath)

ln = net.getLayerNames()

ln = [ln[i[0]-1] for i in net.getUnconnectedOutLayers()]

vc = cv2.VideoCapture(0)


(W, H) = (None, None)

if imutils.is_cv2():
    prop = cv2.cv.CV_CAP_PROP_FRAME_COUNT

while True:
    (grabbed, frame) = vc.read()

    if not grabbed:
        break

    if W is None or H is None:
        (H, W) = frame.shape[:2]

    blob = cv2.dnn.blobFromImage(
        frame, 1/255.0, (416, 416), swapRB=True, crop=False)
    net.setInput(blob)
    start = time.time()
    layerOutputs = net.forward(ln)
    end = time.time()

    boxes = []
    confidences = []
    classIDs = []

    for output in layerOutputs:
        for detection in output:

            scores = detection[5:]
            classID = np.argmax(scores)
            confidence = scores[classID]

            if confidence > iconfidence:
                box = detection[0:4]*np.array([W, H, W, H])
                (centerX, centerY, width, height) = box.astype('int')

                x = int(centerX-(width/2))
                y = int(centerY-(height/2))

                boxes.append([x, y, int(width), int(height)])
                confidences.append(float(confidence))
                classIDs.append(classID)
    idxs = cv2.dnn.NMSBoxes(boxes, confidences, iconfidence, ithreshold)

    if len(idxs) > 0:
        for i in idxs.flatten():
            (x, y) = (boxes[i][0], boxes[i][1])
            (w, h) = (boxes[i][2], boxes[i][3])

            color = [int(c) for c in COLORS[classIDs[i]]]
            cv2.rectangle(frame, (x, y), (x+w, y+h), color, 2)
            text = "{}:{:.4f}".format(LABELS[classIDs[i]], confidences[i])
            cv2.putText(frame, text, (x, y-5),
                        cv2.FONT_HERSHEY_SCRIPT_SIMPLEX, 0.5, color, 2)
    cv2.imshow("My App", frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break