python - 我找到了一个掩码检测程序，想知道是否可以用其他东西替换 tensorflow 库

问题描述

我找到了这个掩码检测代码，并希望它在树莓派 4 上运行。pi 4 不允许我安装 tensorflow。所以我想知道是否有人知道要对这段代码进行哪些更改，以便它运行相同，除非没有 tensorflow。非常感谢你们。我对这一切都很陌生，只是想在隔离区学习一些新东西。
    # USAGE
    # python detect_mask_video.py

    # import the necessary packages
    from tensorflow.keras.applications.mobilenet_v2 import preprocess_input
    from tensorflow.keras.preprocessing.image import img_to_array
    from tensorflow.keras.models import load_model
    from imutils.video import VideoStream
    import numpy as np
    import argparse
    import imutils
    import time
    import cv2
    import os

    def detect_and_predict_mask(frame, faceNet, maskNet):
            # grab the dimensions of the frame and then construct a blob
            # from it
            (h, w) = frame.shape[:2]
            blob = cv2.dnn.blobFromImage(frame, 1.0, (300, 300),
                    (104.0, 177.0, 123.0))

            # pass the blob through the network and obtain the face detections
            faceNet.setInput(blob)
            detections = faceNet.forward()

            # initialize our list of faces, their corresponding locations,
            # and the list of predictions from our face mask network
            faces = []
            locs = []
            preds = []

            # loop over the detections
            for i in range(0, detections.shape[2]):
                    # extract the confidence (i.e., probability) associated with
                    # the detection
                    confidence = detections[0, 0, i, 2]

                    # filter out weak detections by ensuring the confidence is
                    # greater than the minimum confidence
                    if confidence > args["confidence"]:
                            # compute the (x, y)-coordinates of the bounding box for
                            # the object
                            box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
                            (startX, startY, endX, endY) = box.astype("int")

                            # ensure the bounding boxes fall within the dimensions of
                            # the frame
                            (startX, startY) = (max(0, startX), max(0, startY))
                            (endX, endY) = (min(w - 1, endX), min(h - 1, endY))

                            # extract the face ROI, convert it from BGR to RGB channel
                            # ordering, resize it to 224x224, and preprocess it
                            face = frame[startY:endY, startX:endX]
                            face = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)
                            face = cv2.resize(face, (224, 224))
                            face = img_to_array(face)
                            face = preprocess_input(face)

                            # add the face and bounding boxes to their respective
                            # lists
                            faces.append(face)
                            locs.append((startX, startY, endX, endY))

            # only make a predictions if at least one face was detected
            if len(faces) > 0:
                    # for faster inference we'll make batch predictions on *all*
                    # faces at the same time rather than one-by-one predictions
                    # in the above `for` loop
                    faces = np.array(faces, dtype="float32")
                    preds = maskNet.predict(faces, batch_size=32)

            # return a 2-tuple of the face locations and their corresponding
            # locations
            return (locs, preds)

    # construct the argument parser and parse the arguments
    ap = argparse.ArgumentParser()
    ap.add_argument("-f", "--face", type=str,
            default="face_detector",
            help="path to face detector model directory")
    ap.add_argument("-m", "--model", type=str,
            default="mask_detector.model",
            help="path to trained face mask detector model")
    ap.add_argument("-c", "--confidence", type=float, default=0.5,
            help="minimum probability to filter weak detections")
    args = vars(ap.parse_args())

    # load our serialized face detector model from disk
    print("[INFO] loading face detector model...")
    prototxtPath = os.path.sep.join([args["face"], "deploy.prototxt"])
    weightsPath = os.path.sep.join([args["face"],
            "res10_300x300_ssd_iter_140000.caffemodel"])
    faceNet = cv2.dnn.readNet(prototxtPath, weightsPath)

    # load the face mask detector model from disk
    print("[INFO] loading face mask detector model...")
    maskNet = load_model(args["model"])

    # initialize the video stream and allow the camera sensor to warm up
    print("[INFO] starting video stream...")
    vs = VideoStream(src=0).start()
    time.sleep(2.0)

    # loop over the frames from the video stream
    while True:
            # grab the frame from the threaded video stream and resize it
            # to have a maximum width of 400 pixels
            frame = vs.read()
            frame = imutils.resize(frame, width=400)

            # detect faces in the frame and determine if they are wearing a
            # face mask or not
            (locs, preds) = detect_and_predict_mask(frame, faceNet, maskNet)

            # loop over the detected face locations and their corresponding
            # locations
            for (box, pred) in zip(locs, preds):
                    # unpack the bounding box and predictions
                    (startX, startY, endX, endY) = box
                    (mask, withoutMask) = pred

                    # determine the class label and color we'll use to draw
                    # the bounding box and text
                    label = "Mask" if mask > withoutMask else "No Mask"
                    color = (0, 255, 0) if label == "Mask" else (0, 0, 255)

                    # include the probability in the label
                    label = "{}: {:.2f}%".format(label, max(mask, withoutMask) * 100)

                    # display the label and bounding box rectangle on the output
                    # frame
                    cv2.putText(frame, label, (startX, startY - 10),
                            cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 2)
                    cv2.rectangle(frame, (startX, startY), (endX, endY), color, 2)

            # show the output frame
            cv2.imshow("Frame", frame)
            key = cv2.waitKey(1) & 0xFF

            # if the `q` key was pressed, break from the loop
            if key == ord("q"):
                    break

    # do a bit of cleanup
    cv2.destroyAllWindows()
    vs.stop()
标签： pythontensorflowimage-recognition
python - 我找到了一个掩码检测程序，想知道是否可以用其他东西替换 tensorflow 库

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