How to use the imutils.acquire_image function in imutils

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github ox-vgg / vgg_face_search / pipeline / compute_pos_features.py View on Github external
# import and create face feature extractor
    import face_features
    feature_extractor = face_features.FaceFeatureExtractor()

    # Compute features for all image paths in args.images_list
    all_feats = {'paths': [], 'rois': [], 'feats': []}
    with open(args.images_list) as fin:
        for img_path in fin:
            img_path = img_path.replace('\n', '')
            if len(img_path) > 0:
                full_path = os.path.join(args.dataset_base_path, img_path)
                print ('Computing features for file %s' % (full_path))

                # read image
                img = imutils.acquire_image(full_path)

                # run face detector
                detections = face_detector.detect_faces(img)

                if numpy.all(detections != None):

                    for det in detections:

                        # The coordinates should be already integers, but some basic
                        # conversion is need for compatibility with all face detectors.
                        # Plus we have to get rid of the detection score det[4]
                        det = [int(det[0]), int(det[1]), int(det[2]), int(det[3])]

                        # crop image to detected face area.
                        crop_img = img[det[1]:det[3], det[0]:det[2], :]
github ox-vgg / vgg_face_search / service / face_retrieval.py View on Github external
else:
            from_dataset = False
            roi = None
            uri = -1

        # create empty dictionary for the image information
        img = dict()

        # if the image is brand new ..
        if uri == -1:

            # and no roi was specified ...
            if roi == None:

                # read image
                theim = imutils.acquire_image(impath)
                # run face detector, but only get the best detection.
                # multiple detections are not supported for on-the-fly training images
                det = self.face_detector.detect_faces(theim, return_best=True)

                if numpy.all(det != None):

                    # if a face is found, save it
                    print ('Single ROI detected')
                    # The coordinates should be already integers, but some basic
                    # conversion is need for compatibility with all face detectors.
                    # Plus we have to get rid of the detection score det[4]
                    det = [int(det[0][0]), int(det[0][1]), int(det[0][2]), int(det[0][3])]
                    print ('final det ' + str(det))

                    img["path"] = impath
                    img["roi"] = det
github ox-vgg / vgg_face_search / service / face_retrieval.py View on Github external
if 'uri' in req_params['extra_params']:
                uri = req_params['extra_params']['uri']
            else:
                uri = -1

            if 'roi' in req_params['extra_params']:
                # if request specifies a ROI ...
                roi = req_params['extra_params']['roi']
                roi = numpy.array([int(x) for x in roi]).reshape(len(roi)//2, 2)
                xl, yl = roi.min(axis=0)
                xu, yu = roi.max(axis=0)
                roi = [xl, yl, xu, yu]
                print ('Request specifies ROI ' + str(roi))
                # ... check there is a face on the roi
                theim = imutils.acquire_image(impath)
                crop_img = theim[yl:yu, xl:xu, :]
                det = self.face_detector.detect_faces(crop_img, return_best=True)
                if numpy.all(det == None):
                    print ('No detection found in specified ROI')
                    return self.prepare_success_json_str_(False)
                else:
                    # If found, replace the previous with a more accurate one
                    det = det[0]
                    # The coordinates should be already integers, but some basic
                    # conversion is need for compatibility with all face detectors.
                    # Plus we have to get rid of the detection score det[4]
                    det = [int(det[0]), int(det[1]), int(det[2]), int(det[3])]
                    roi = [det[0]+xl, det[1]+yl, det[2]+xl, det[3]+yl]
                    print ('Automatically adjusting ROI to more accurate region ' + str(roi))
            else:
                roi = None
github ox-vgg / vgg_face_search / pipeline / compute_pos_features_video.py View on Github external
shot_end = shot[1] + '.jpg'
        shot_begin_index = video_frames_list.index(shot_begin)
        shot_end_index = video_frames_list.index(shot_end)
        shot_detections = []
        shot_tracks = []
        shot_images = []

        #####
        # Compute face detections in shot
        #####
        for index in range(shot_begin_index, shot_end_index+1):
            img_name = video_frames_list[index]
            full_path = os.path.join(args.video_frames_path, img_name)

            # read image
            img = imutils.acquire_image(full_path)
            shot_images.append(img)

            # run face detector
            detections = face_detector.detect_faces(img)
            shot_detections.append(detections)
            if numpy.all(detections != None):
                shot_tracks.append([-1] * len(detections)) # init all tracks number with -1 ...
            else:
                shot_tracks.append(None) # ... or None if there are no detections

        #####
        # Compute face tracks in shot
        #####

        # The code below uses two pointers to the array of images: index A and index B.
        # Index A points to the current image
github ox-vgg / vgg_face_search / service / face_retrieval.py View on Github external
Body of the thread that runs the face feature extraction for
        a list of images
        Arguments:
            image_list: List of images to be processed. Each item in the list corresponds to a dictionary with
                        at least two keys: "path" and "roi". The "path" should contain the full path to the image
                        file to be processed and "roi" the coordinates of the bounding-box of a face detected on
                        the image.
    """
    list_of_feats = []
    if len(image_list) > 0:
        try:
            # init feature extractor
            feature_extractor = face_features.FaceFeatureExtractor()
            for image in image_list:
                # read image
                theim = imutils.acquire_image(image["path"])
                det = image["roi"]
                # crop image to face detection bounding-box
                crop_img = theim[det[1]:det[3], det[0]:det[2], :]
                # extract features
                feat = feature_extractor.feature_compute(crop_img)
                # reshape for compatibility with ranking function
                feat = numpy.reshape(feat, (1, settings.FEATURES_VECTOR_SIZE))
                # add to list of features to be returned
                list_of_feats.append(feat)
        except Exception as e:
            print ('Exception in group_feature_extractor: ' + str(e))
            list_of_feats = []
            pass
    return list_of_feats

imutils

A series of convenience functions to make basic image processing functions such as translation, rotation, resizing, skeletonization, displaying Matplotlib images, sorting contours, detecting edges, and much more easier with OpenCV and both Python 2.7 and Python 3.

MIT
Latest version published 4 years ago

Package Health Score

64 / 100
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