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script.py
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script.py
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import numpy as np
import argparse
import time
import cv2
import os
import speech_recognition as sr
from gtts import gTTS
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True,
help="path to input image")
ap.add_argument("-y", "--yolo", required=True,
help="base path to YOLO directory")
ap.add_argument("-c", "--confidence", type=float, default=0.5,
help="minimum probability to filter weak detections")
ap.add_argument("-t", "--threshold", type=float, default=0.3,
help="threshold when applyong non-maxima suppression")
args = vars(ap.parse_args())
labelsPath = os.path.sep.join([args["yolo"], "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([args["yolo"], "yolov3.weights"])
configPath = os.path.sep.join([args["yolo"], "yolov3.cfg"])
print("[INFO] loading YOLO from disk...")
net = cv2.dnn.readNetFromDarknet(configPath, weightsPath)
image = cv2.imread(args["image"])
(H, W) = image.shape[:2]
ln = net.getLayerNames()
ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()]
blob = cv2.dnn.blobFromImage(image, 1 / 255.0, (416, 416),
swapRB=True, crop=False)
net.setInput(blob)
start = time.time()
layerOutputs = net.forward(ln)
end = time.time()
print("[INFO] YOLO took {:.6f} seconds".format(end - start))
boxes = []
confidences = []
classIDs = []
ID = 0
for output in layerOutputs:
for detection in output:
scores = detection[5:]
classID = np.argmax(scores)
confidence = scores[classID]
if confidence > args["confidence"]:
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, args["confidence"],
args["threshold"])
if len(idxs) > 0:
list1 = []
for i in idxs.flatten():
(x, y) = (boxes[i][0], boxes[i][1])
(w, h) = (boxes[i][2], boxes[i][3])
centerx = round((2*x + w)/2)
centery = round((2*y + h)/2)
if centerX <= W/3:
W_pos = "left "
elif centerX <= (W/3 * 2):
W_pos = "center "
else:
W_pos = "right "
if centerY <= H/3:
H_pos = "top "
elif centerY <= (H/3 * 2):
H_pos = "mid "
else:
H_pos = "bottom "
list1.append(H_pos + W_pos + LABELS[classIDs[i]])
description = ', '.join(list1)
myobj = gTTS(text=description, lang="en", slow=False)
myobj.save("object_detection.mp3")