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()