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objectDetection.py
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objectDetection.py
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import cv2
import numpy as np
import mimetypes
import sys
# Load Yolo
net = cv2.dnn.readNet("yolov3.weights", "yolov3.cfg")
classes = []
with open("coco.names", "r") as f:
classes = [line.strip() for line in f.readlines()]
layer_names = net.getLayerNames()
output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
colors = np.random.uniform(0, 255, size=(len(classes), 3))
if(sys.argv[1] == 'webcam'):
mimestart = 'webcam'
else:
mimetypes.init()
mimestart = mimetypes.guess_type(sys.argv[1])[0]
if mimestart != None:
mimestart = mimestart.split('/')[0]
if mimestart == 'video' or mimestart == 'webcam':
counter = -1
#read video file
if mimestart == 'video':
cap = cv2.VideoCapture(sys.argv[1])
else:
cap = cv2.VideoCapture(0)
while (True):
#if ret is true than no error with cap.isOpened
ret, frame = cap.read()
if ret==True:
frame = cv2.resize(frame, None, fx=2.2, fy=2.2)
height, width, channels = frame.shape
counter = counter + 1
if (counter%3 == 0):
# Detecting objects
blob = cv2.dnn.blobFromImage(frame, 0.00392, (416, 416), (0, 0, 0), True, crop=False)
net.setInput(blob)
outs = net.forward(output_layers)
# Showing informations on the screen
class_ids = []
confidences = []
boxes = []
for out in outs:
for detection in out:
scores = detection[5:]
class_id = np.argmax(scores)
confidence = scores[class_id]
if confidence > 0.5:
# Object detected
center_x = int(detection[0] * width)
center_y = int(detection[1] * height)
w = int(detection[2] * width)
h = int(detection[3] * height)
# Rectangle coordinates
x = int(center_x - w / 2)
y = int(center_y - h / 2)
boxes.append([x, y, w, h])
confidences.append(float(confidence))
class_ids.append(class_id)
indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
#print(indexes)
font = cv2.FONT_HERSHEY_PLAIN
for i in range(len(boxes)):
if i in indexes:
x, y, w, h = boxes[i]
label = str(classes[class_ids[i]] + " " + str(round(confidences[i], 2)))
color = colors[i]
cv2.rectangle(frame, (x, y), (x + w, y + h), color, 1)
cv2.putText(frame, label, (x, y + 30), font, 2, color, 3)
cv2.imshow("frame", frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
else:
# Loading image
img = cv2.imread(sys.argv[1])
img = cv2.resize(img, None, fx=0.8, fy=0.8)
height, width, channels = img.shape
# Detecting objects
blob = cv2.dnn.blobFromImage(img, 0.00392, (416, 416), (0, 0, 0), True, crop=False)
net.setInput(blob)
outs = net.forward(output_layers)
# Showing informations on the screen
class_ids = []
confidences = []
boxes = []
for out in outs:
for detection in out:
scores = detection[5:]
class_id = np.argmax(scores)
confidence = scores[class_id]
if confidence > 0.5:
# Object detected
center_x = int(detection[0] * width)
center_y = int(detection[1] * height)
w = int(detection[2] * width)
h = int(detection[3] * height)
# Rectangle coordinates
x = int(center_x - w / 2)
y = int(center_y - h / 2)
boxes.append([x, y, w, h])
confidences.append(float(confidence))
class_ids.append(class_id)
indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
font = cv2.FONT_HERSHEY_PLAIN
for i in range(len(boxes)):
if i in indexes:
x, y, w, h = boxes[i]
label = str(classes[class_ids[i]] + " " + str(round(confidences[i], 2)))
color = colors[i]
cv2.rectangle(img, (x, y), (x + w, y + h), color, 1)
cv2.putText(img, label, (x, y + 30), font, 2, color, 3)
cv2.imshow("Image", img)
cv2.waitKey(0)
cv2.destroyAllWindows()