def mplot(img, img2=none):
cv2.namedwindow('img', cv2.window_normal)
cv2.movewindow('img', 600, 300)
cv2.imshow('img', img)
if img2 is not none:
cv2.namedwindow('img2', cv2.window_normal)
cv2.movewindow('img', 600, 600)
cv2.imshow('img2', img2)
cv2.waitkey(0)
cv2.destroyallwindows()
def get_frame():
cap =cv2.VideoCapture(0+cv2.CAP_DSHOW)
while True:
_, frame = cap.read()
frame=cv2.flip(frame,1)
x = FRDist(frame,enck,Names)
imgencode = cv2.imencode('.jpg',frame)[1]
stringData= imgencode.tobytes()
yield(b'--frame\r\n'b'Content-Type: image/jpeg\r\n\r\n'+ stringData +b'\r\n')
cap.release()
cv2.destroyallwindows()
def take_snapshot():
number = random.randint(0, 100)
videoCaptureObject = cv2.VideoCapture(0)
result = True
while (result):
ret, frame = videoCaptureObject.read()
image_name = "img" + str(number) + ".png"
cv2.imwrite(image_name, frame)
start_time = time.time
result = False
return img_name
print("snapshot taken")
videoCaptureObject.release()
cv2.destroyallwindows()
def clean(path, show=False):
image = cv2.imread(path)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
image = cv2.medianBlur(gray, 5)
kernel = np.ones((5, 5), np.uint8)
alpha, beta = 1, 25
image = cv2.erode(image, kernel, iterations=1)
image = cv2.dilate(image, kernel, iterations=1)
image = cv2.morphologyEx(image, cv2.MORPH_OPEN, kernel)
adjusted = cv2.convertScaleAbs(image, alpha=alpha, beta=beta)
if show:
cv2.imshow("result", image)
cv2.waitKey(0)
cv2.destroyallwindows()
# Save Result
cv2.imwrite('temp.jpg', image)
def display_image(self, img: np.ndarray):
"""Display an image until window is closed."""
cv2.imshow('Image', img)
cv2.waitKey(0)
cv2.destroyallwindows()
cap = cv2.VideoCapture("http://192.168.43.1:8080/video")
while True:
ret, frame = cap.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
canvas = detect(gray, frame)
# show the output montage
#montage = build_montages(results, (1024, 1024), (1, 1))[0]
#cv2.imshow("Results", montage)
cv2.imshow("results", canvas)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyallwindows()
"""
# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--images", required=True,
help="path to out input directory of images")
ap.add_argument("-m", "--model", required=True,
help="path to pre-trained model")
args = vars(ap.parse_args())
# load the pre-trained network
print("[INFO] loading pre-trained network...")
model = load_model(args["model"])
# grab all image paths in the input directory and randomly sample them
#imagePaths = list(paths.list_images(args["images"]))
def run():
net = cv2.dnn.readNet(opt.weights, opt.cfg)
classes = load_classes(opt.names)
layer_names = net.getLayerNames()
outputlayers = [
layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()
]
colors = np.random.uniform(0, 255, size=(len(classes), 3))
#loading video from webcam
cap = cv2.VideoCapture(0)
font = cv2.FONT_HERSHEY_SIMPLEX
while True:
_, frame = cap.read()
height, width, channels = frame.shape
blob = cv2.dnn.blobFromImage(
frame, 0.00392, (320, 320), (0, 0, 0), True,
crop=False) #reduce the frame to 320 * 320 pixels
net.setInput(blob)
outs = net.forward(outputlayers)
#Showing info on screen/ get confidence score of algorithm in detecting an object in blob
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 > 0.3:
#object detected
center_x = int(detection[0] * width)
center_y = int(detection[1] * height)
w = int(detection[2] * width)
h = int(detection[3] * height)
#cv2.circle(img,(center_x,center_y),10,(0,255,0),2)
#rectangle co-ordinaters
x = int(center_x - w / 2)
y = int(center_y - h / 2)
#cv2.rectangle(img,(x,y),(x+w,y+h),(0,255,0),2)
boxes.append([x, y, w, h]) #put all rectangle areas
confidences.append(
float(confidence)
) #how confidence was that object detected and show that percentage
class_ids.append(
class_id) #name of the object that was detected
indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.4, 0.6)
for i in range(len(boxes)):
if i in indexes:
x, y, w, h = boxes[i]
if i == 0:
label = str(classes[class_ids[i]])
confidence = confidences[i]
color = colors[class_ids[i]]
cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)
cv2.putText(frame, label + " " + str(round(confidence, 2)),
(x, y + 30), font, 0.5, (255, 255, 255), 1)
cv2.imshow("Image", frame)
key = cv2.waitKey(
1
) #wait 1ms before the loop starts again and we process the next frame
if key == 27:
break
#esc key stops the process
cap.release()
cv2.destroyallwindows()
