Python random.uniform() Examples

def augment_hsv(img, hgain=0.5, sgain=0.5, vgain=0.5):
    x = (np.random.uniform(-1, 1, 3) * np.array([hgain, sgain, vgain]) + 1).astype(np.float32)  # random gains
    img_hsv = (cv2.cvtColor(img, cv2.COLOR_BGR2HSV) * x.reshape((1, 1, 3))).clip(None, 255).astype(np.uint8)
    cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=img)  # no return needed


# def augment_hsv(img, hgain=0.5, sgain=0.5, vgain=0.5):  # original version
#     # SV augmentation by 50%
#     img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)  # hue, sat, val
#
#     S = img_hsv[:, :, 1].astype(np.float32)  # saturation
#     V = img_hsv[:, :, 2].astype(np.float32)  # value
#
#     a = random.uniform(-1, 1) * sgain + 1
#     b = random.uniform(-1, 1) * vgain + 1
#     S *= a
#     V *= b
#
#     img_hsv[:, :, 1] = S if a < 1 else S.clip(None, 255)
#     img_hsv[:, :, 2] = V if b < 1 else V.clip(None, 255)
#     cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=img)  # no return needed 

def sample(self):
        """
        This is the core sampling method. Samples a state from a
        demonstration, in accordance with the configuration.
        """

        # chooses a sampling scheme randomly based on the mixing ratios
        seed = random.uniform(0, 1)
        ratio = np.cumsum(self.scheme_ratios)
        ratio = ratio > seed
        for i, v in enumerate(ratio):
            if v:
                break

        sample_method = getattr(self, self.sample_method_dict[self.sampling_schemes[i]])
        return sample_method() 

def __call__(self, data):
        image, label = data
        height, width = image.shape[:2]
        xmin = width
        ymin = height
        xmax = 0
        ymax = 0
        for lb in label:
            xmin = min(xmin, lb[0])
            ymin = min(ymin, lb[1])
            xmax = max(xmax, lb[2])
            ymax = max(ymax, lb[2])
        cropped_left = uniform(0, self.max_crop)
        cropped_right = uniform(0, self.max_crop)
        cropped_top = uniform(0, self.max_crop)
        cropped_bottom = uniform(0, self.max_crop)
        new_xmin = int(min(cropped_left * width, xmin))
        new_ymin = int(min(cropped_top * height, ymin))
        new_xmax = int(max(width - 1 - cropped_right * width, xmax))
        new_ymax = int(max(height - 1 - cropped_bottom * height, ymax))

        image = image[new_ymin:new_ymax, new_xmin:new_xmax, :]
        label = [[lb[0] - new_xmin, lb[1] - new_ymin, lb[2] - new_xmin, lb[3] - new_ymin, lb[4]] for lb in label]

        return image, label 

def random(base_price, t_gen, delta):
    return ModelParameters(
        all_s0=base_price,
        all_r0=0.5,
        all_time=t_gen,
        all_delta=delta,
        all_sigma=uniform(0.1, 0.8),
        gbm_mu=uniform(-0.3, 0.6),
        jumps_lamda=uniform(0.0071, 0.6),
        jumps_sigma=uniform(-0.03, 0.04),
        jumps_mu=uniform(-0.2, 0.2),
        cir_a=3.0,
        cir_mu=0.5,
        cir_rho=0.5,
        ou_a=3.0,
        ou_mu=0.5,
        heston_a=uniform(1, 5),
        heston_mu=uniform(0.156, 0.693),
        heston_vol0=0.06125
    ) 

def __call__(self, video):
    for attempt in range(10):
      area = video.shape[-3]*video.shape[-2]
      target_area = random.uniform(0.08, 1.0)*area
      aspect_ratio = random.uniform(3./4, 4./3)

      w = int(round(math.sqrt(target_area*aspect_ratio)))
      h = int(round(math.sqrt(target_area/aspect_ratio)))

      if random.random() < 0.5:
        w, h = h, w

      if w <= video.shape[-2] and h <= video.shape[-3]:
        x1 = random.randint(0, video.shape[-2]-w)
        y1 = random.randint(0, video.shape[-3]-h)

        video = video[..., y1:y1+h, x1:x1+w, :]

        return resize(video, (self.size, self.size), self.interpolation)

    # Fallback
    scale = Scale(self.size, interpolation=self.interpolation)
    crop = CenterCrop(self.size)
    return crop(scale(video)) 

def fake_responses(request, context):
    responses = [
        # increasing the chance of 404
        {'text': 'Not Found', 'status_code': 404},
        {'text': 'Not Found', 'status_code': 404},
        {'text': 'Not Found', 'status_code': 404},
        {'text': 'Not Found', 'status_code': 404},
        {'text': 'OK', 'status_code': 200},
        {'text': 'Gateway timeout', 'status_code': 504},
        {'text': 'Bad gateway', 'status_code': 502},
    ]
    random.shuffle(responses)
    response = responses.pop()

    context.status_code = response['status_code']
    context.reason = response['text']
    # Random float x, 1.0 <= x < 4.0 for some sleep jitter
    time.sleep(random.uniform(1, 4))
    return response['text'] 

def add_cleanup_pod(url):
    """populate the cleanup pod list"""
    # variance allows a pod to stay alive past grace period
    variance = random.uniform(0.1, 1.5)
    grace = round(settings.KUBERNETES_POD_TERMINATION_GRACE_PERIOD_SECONDS * variance)

    # save
    pods = cache.get('cleanup_pods', {})
    pods[url] = (datetime.utcnow() + timedelta(seconds=grace))
    cache.set('cleanup_pods', pods)

    # add grace period timestamp
    pod = cache.get(url)
    grace = settings.KUBERNETES_POD_TERMINATION_GRACE_PERIOD_SECONDS
    pd = datetime.utcnow() + timedelta(seconds=grace)
    timestamp = str(pd.strftime(MockSchedulerClient.DATETIME_FORMAT))
    pod['metadata']['deletionTimestamp'] = timestamp
    cache.set(url, pod) 

def get_params(img, scale, ratio):

        if type(img) == np.ndarray:
            img_h, img_w, img_c = img.shape
        else: 
            img_h, img_w = img.size
            img_c = len(img.getbands())

        s = random.uniform(*scale)
        # if you img_h != img_w you may need this.
        # r_1 = max(r_1, (img_h*s)/img_w)
        # r_2 = min(r_2, img_h / (img_w*s))
        r = random.uniform(*ratio)
        s = s * img_h * img_w
        w = int(math.sqrt(s / r))
        h = int(math.sqrt(s * r))
        left = random.randint(0, img_w - w)
        top = random.randint(0, img_h - h)

        return left, top, h, w, img_c 

def random_resize(im, min_res: int, max_scale=_DEFAULT_MAX_SCALE):
    """Scale longer side to `min_res`, but only if that scales by <= max_scale."""
    W, H = im.size
    D = min(W, H)
    scale_min = min_res / D
    # Image is too small to downscale by a factor smaller MAX_SCALE.
    if scale_min > max_scale:
        return None

    # Get a random scale for new size.
    scale = random.uniform(scale_min, max_scale)
    new_size = round(W * scale), round(H * scale)
    try:
        # Using LANCZOS!
        return im.resize(new_size, resample=PIL.Image.LANCZOS)
    except OSError as e:  # Happens for corrupted images
        print('*** Caught im.resize error', e)
        return None 

def __call__(self, img):
        for attempt in range(10):
            area = img.size[0] * img.size[1]
            target_area = random.uniform(0.08, 1.0) * area
            aspect_ratio = random.uniform(3. / 4, 4. / 3)

            w = int(round(math.sqrt(target_area * aspect_ratio)))
            h = int(round(math.sqrt(target_area / aspect_ratio)))

            if random.random() < 0.5:
                w, h = h, w

            if w <= img.size[0] and h <= img.size[1]:
                x1 = random.randint(0, img.size[0] - w)
                y1 = random.randint(0, img.size[1] - h)

                img = img.crop((x1, y1, x1 + w, y1 + h))
                assert(img.size == (w, h))

                return img.resize((self.size, self.size), self.interpolation)

        # Fallback
        scale = Scale(self.size, interpolation=self.interpolation)
        crop = CenterCrop(self.size)
        return crop(scale(img)) 

def get_params(img, scale, ratio):
        """Get parameters for ``crop`` for a random sized crop.
        Args:
            img (PIL Image): Image to be cropped.
            scale (tuple): range of size of the origin size cropped
            ratio (tuple): range of aspect ratio of the origin aspect ratio cropped
        Returns:
            tuple: params (i, j, h, w) to be passed to ``crop`` for a random
                sized crop.
        """
        area = img.size[0] * img.size[1]

        for attempt in range(10):
            target_area = random.uniform(*scale) * area
            log_ratio = (math.log(ratio[0]), math.log(ratio[1]))
            aspect_ratio = math.exp(random.uniform(*log_ratio))

            w = int(round(math.sqrt(target_area * aspect_ratio)))
            h = int(round(math.sqrt(target_area / aspect_ratio)))

            if w <= img.size[0] and h <= img.size[1]:
                i = random.randint(0, img.size[1] - h)
                j = random.randint(0, img.size[0] - w)
                return i, j, h, w

        # Fallback to central crop
        in_ratio = img.size[0] / img.size[1]
        if (in_ratio < min(ratio)):
            w = img.size[0]
            h = w / min(ratio)
        elif (in_ratio > max(ratio)):
            h = img.size[1]
            w = h * max(ratio)
        else:  # whole image
            w = img.size[0]
            h = img.size[1]
        i = (img.size[1] - h) // 2
        j = (img.size[0] - w) // 2
        return i, j, h, w 

def random_size_crop(src, size, min_area=0.25, ratio=(3.0/4.0, 4.0/3.0)):
    """Randomly crop src with size. Randomize area and aspect ratio"""
    h, w, _ = src.shape
    area = w*h
    for _ in range(10):
        new_area = random.uniform(min_area, 1.0) * area
        new_ratio = random.uniform(*ratio)
        new_w = int(new_area*new_ratio)
        new_h = int(new_area/new_ratio)

        if random.uniform(0., 1.) < 0.5:
            new_w, new_h = new_h, new_w

        if new_w > w or new_h > h:
            continue

        x0 = random.randint(0, w - new_w)
        y0 = random.randint(0, h - new_h)

        out = fixed_crop(src, x0, y0, new_w, new_h, size)
        return out, (x0, y0, new_w, new_h)

    return random_crop(src, size) 

def spec_augment(spec: np.ndarray,
                 num_mask=2,
                 freq_masking=0.15,
                 time_masking=0.20,
                 value=0):
    spec = spec.copy()
    num_mask = random.randint(1, num_mask)
    for i in range(num_mask):
        all_freqs_num, all_frames_num  = spec.shape
        freq_percentage = random.uniform(0.0, freq_masking)

        num_freqs_to_mask = int(freq_percentage * all_freqs_num)
        f0 = np.random.uniform(low=0.0, high=all_freqs_num - num_freqs_to_mask)
        f0 = int(f0)
        spec[f0:f0 + num_freqs_to_mask, :] = value

        time_percentage = random.uniform(0.0, time_masking)

        num_frames_to_mask = int(time_percentage * all_frames_num)
        t0 = np.random.uniform(low=0.0, high=all_frames_num - num_frames_to_mask)
        t0 = int(t0)
        spec[:, t0:t0 + num_frames_to_mask] = value
    return spec 

def describe(self, as_tuple=None, **kwargs):
        """
        This method is to retrieve test resources, method parameters are only used signature compatibility
        :param as_tuple: Set to true to return results as immutable named dictionaries instead of dictionaries
        :return: Test resource
        """

        def create_resource(r):
            return {
                actions.ops_automator_test_action.TEST_RESOURCE_ID: OpsautomatortestService.resource_id(r),
                "AwsAccount": self.aws_account,
                "Region": kwargs["region"] if "region" in kwargs else self.region,
                "Service": self.service_name,
                "ResourceTypeName": actions.ops_automator_test_action.TEST_RESOURCE_NAMES[0],
                "Tags": self.tags
            }

        start = datetime.now()

        self._args = kwargs
        result = [create_resource(i) for i in sorted(self._number_of_resources)]

        if self._args.get(actions.ops_automator_test_action.PARAM_TEST_SELECT_FAILING, False) in ["True", True]:
            raise Exception("Selection of resources fails")

        select_time = int(self._args.get(actions.ops_automator_test_action.PARAM_TEST_SELECT_DURATION, 0))

        if select_time != 0:
            variance = float(self._args.get(actions.ops_automator_test_action.PARAM_TEST_SELECT_DURATION_VARIANCE, 0))
            if variance != 0:
                select_time += (random.uniform(variance * -1, variance) * select_time)
            time_spend = (datetime.now() - start).total_seconds()
            if time_spend < select_time:
                time.sleep(select_time - time_spend)

        return result 

def __call__(self, data):
        image, label = data
        if uniform(0, 1) >= self.prob:
            image = cv2.flip(image, 1)
            width = image.shape[1]
            label = [[width - lb[2], lb[1], width - lb[0], lb[3], lb[4]] for lb in label]
        return image, label 

def get_data_from_chunk_v2(chunk):

    main_folder_path = '../../Data/MS2017a/'
    scans_folder_path = main_folder_path + 'scans/'

    img_type_path = 'pre/FLAIR.nii.gz'
    gt_type_path = 'wmh.nii.gz'

    scale = random.uniform(0.5, 1.3)
    dim = int(scale*321)

    images = np.zeros((dim,dim, 1,len(chunk)))
    gt = np.zeros((dim,dim,1,len(chunk)))
    for i, piece in enumerate(chunk):
        print(os.path.join(main_folder_path, piece))
        img_temp = PP.numpyFromScan(os.path.join(main_folder_path, piece))
        flip_p = random.uniform(0, 1)

        img_temp = cv2.resize(img_temp,(321,321)).astype(float)
        img_temp = img_temp.reshape([321, 321, 1])

        img_temp = scale_im(img_temp,scale)
        img_temp = flip(img_temp,flip_p)
        images[:,:,0,i] = img_temp

        piece_gt = piece.replace('slices', 'gt_slices').replace('FLAIR', 'wmh')
        gt_temp = PP.numpyFromScan(os.path.join(main_folder_path, piece_gt), makebin = onlyLesions)
        gt_temp = cv2.resize(gt_temp,(321,321) , interpolation = cv2.INTER_NEAREST)
        gt_temp = gt_temp.reshape([321,321, 1])
        gt_temp = scale_gt(gt_temp,scale)
        gt_temp = flip(gt_temp,flip_p)

        gt[:,:,0,i] = gt_temp
        a = outS(321*scale)

    labels = [resize_label_batch(gt,i) for i in [a,a,a,a]]

    #from dim1 x dim2 x 1 x batch -> batch x 1 x dim1 x dim2
    images = images.transpose((3,2,0,1))
    images = torch.from_numpy(images).float()
    return images, labels 

def __call__(self, img):
        gs = img.new().resize_as_(img).zero_()
        alpha = random.uniform(-self.var, self.var)
        return img.lerp(gs, alpha) 

def expand(image, boxes, filler):
    """
    Perform a zooming out operation by placing the image in a larger canvas of filler material.

    Helps to learn to detect smaller objects.

    :param image: image, a tensor of dimensions (3, original_h, original_w)
    :param boxes: bounding boxes in boundary coordinates, a tensor of dimensions (n_objects, 4)
    :param filler: RBG values of the filler material, a list like [R, G, B]
    :return: expanded image, updated bounding box coordinates
    """
    # Calculate dimensions of proposed expanded (zoomed-out) image
    original_h = image.size(1)
    original_w = image.size(2)
    max_scale = 4
    scale = random.uniform(1, max_scale)
    new_h = int(scale * original_h)
    new_w = int(scale * original_w)

    # Create such an image with the filler
    filler = torch.FloatTensor(filler)  # (3)
    new_image = torch.ones((3, new_h, new_w), dtype=torch.float) * filler.unsqueeze(1).unsqueeze(1)  # (3, new_h, new_w)
    # Note - do not use expand() like new_image = filler.unsqueeze(1).unsqueeze(1).expand(3, new_h, new_w)
    # because all expanded values will share the same memory, so changing one pixel will change all

    # Place the original image at random coordinates in this new image (origin at top-left of image)
    left = random.randint(0, new_w - original_w)
    right = left + original_w
    top = random.randint(0, new_h - original_h)
    bottom = top + original_h
    new_image[:, top:bottom, left:right] = image

    # Adjust bounding boxes' coordinates accordingly
    new_boxes = boxes + torch.FloatTensor([left, top, left, top]).unsqueeze(
        0)  # (n_objects, 4), n_objects is the no. of objects in this image

    return new_image, new_boxes 

def photometric_distort(image):
    """
    Distort brightness, contrast, saturation, and hue, each with a 50% chance, in random order.

    :param image: image, a PIL Image
    :return: distorted image
    """
    new_image = image

    distortions = [FT.adjust_brightness,
                   FT.adjust_contrast,
                   FT.adjust_saturation,
                   FT.adjust_hue]

    random.shuffle(distortions)

    for d in distortions:
        if random.random() < 0.5:
            if d.__name__ is 'adjust_hue':
                # Caffe repo uses a 'hue_delta' of 18 - we divide by 255 because PyTorch needs a normalized value
                adjust_factor = random.uniform(-18 / 255., 18 / 255.)
            else:
                # Caffe repo uses 'lower' and 'upper' values of 0.5 and 1.5 for brightness, contrast, and saturation
                adjust_factor = random.uniform(0.5, 1.5)

            # Apply this distortion
            new_image = d(new_image, adjust_factor)

    return new_image 

def __call__(self, img):
        gs = Grayscale()(img)
        alpha = random.uniform(-self.var, self.var)
        return img.lerp(gs, alpha) 

def __call__(self, sample):
        img = sample['image']
        mask = sample['label']
        rotate_degree = random.uniform(-1*self.degree, self.degree)
        img = img.rotate(rotate_degree, Image.BILINEAR)
        mask = mask.rotate(rotate_degree, Image.NEAREST)

        return {'image': img,
                'label': mask} 

def __call__(self, img):
        gs = Grayscale()(img)
        alpha = random.uniform(0, self.var)
        return img.lerp(gs, alpha) 

def _apply_randomness(value, random_factor):
    """
    Applies a random factor to the value
    :param value: Input value
    :param random_factor: Random factor, must be between 0 (no random) and 1 (output is between 0 and 2* value)
    :return: Value with random factor applied
    """
    if random_factor < 0 or random_factor > 1:
        raise ValueError("Random factor must be in range 0 to 1")
    return value + (random.uniform(random_factor * -1, random_factor) * value) if random_factor != 0 else value 

def get_params(mean, std):
        """Get parameters for gaussian noise
        Returns:
            sequence: params to be passed to the affine transformation
        """
        mean = random.uniform(0, mean)
        std = random.uniform(0, std)

        return mean, std 

def get_params(degrees, translate, scale_ranges, shears, img_size):
        """Get parameters for affine transformation

        Returns:
            sequence: params to be passed to the affine transformation
        """
        angle = random.uniform(degrees[0], degrees[1])
        if translate is not None:
            max_dx = translate[0] * img_size[1]
            max_dy = translate[1] * img_size[0]
            translations = (np.round(random.uniform(-max_dx, max_dx)),
                            np.round(random.uniform(-max_dy, max_dy)))
        else:
            translations = (0, 0)

        if scale_ranges is not None:
            scale = random.uniform(scale_ranges[0], scale_ranges[1])
        else:
            scale = 1.0

        if shears is not None:
            shear = random.uniform(shears[0], shears[1])
        else:
            shear = 0.0

        return angle, translations, scale, shear 

def get_params(degrees):
        """Get parameters for ``rotate`` for a random rotation.

        Returns:
            sequence: params to be passed to ``rotate`` for random rotation.
        """
        angle = random.uniform(degrees[0], degrees[1])

        return angle 

def get_params(img, scale, ratio):
        """Get parameters for ``crop`` for a random sized crop.

        Args:
            img (CV Image): Image to be cropped.
            scale (tuple): range of size of the origin size cropped
            ratio (tuple): range of aspect ratio of the origin aspect ratio cropped

        Returns:
            tuple: params (i, j, h, w) to be passed to ``crop`` for a random
                sized crop.
        """
        area = img.shape[0] * img.shape[1]

        for attempt in range(10):
            target_area = random.uniform(*scale) * area
            aspect_ratio = random.uniform(*ratio)

            w = int(round(math.sqrt(target_area * aspect_ratio)))
            h = int(round(math.sqrt(target_area / aspect_ratio)))

            if w <= img.shape[1] and h <= img.shape[0]:
                i = random.randint(0, img.shape[0] - h)
                j = random.randint(0, img.shape[1] - w)
                return i, j, h, w

        # Fallback to central crop
        in_ratio = img.shape[1] / img.shape[0]
        if (in_ratio < min(ratio)):
            w = img.shape[1]
            h = int(round(w / min(ratio)))
        elif (in_ratio > max(ratio)):
            h = img.shape[0]
            w = int(round(h * max(ratio)))
        else:  # whole image
            w = img.shape[1]
            h = img.shape[0]
        i = (img.shape[0] - h) // 2
        j = (img.shape[1] - w) // 2
        return i, j, h, w 

def get_params(
            fov_range,
            anglex_ranges,
            angley_ranges,
            anglez_ranges,
            shear_ranges,
            translate,
            scale_ranges,
            img_size):
        """Get parameters for ``perspective`` for a random perspective transform.

        Returns:
            sequence: params to be passed to the perspective transformation
        """
        fov = 90 + random.uniform(-fov_range, fov_range)
        anglex = random.uniform(anglex_ranges[0], anglex_ranges[1])
        angley = random.uniform(angley_ranges[0], angley_ranges[1])
        anglez = random.uniform(anglez_ranges[0], anglez_ranges[1])
        shear = random.uniform(shear_ranges[0], shear_ranges[1])

        max_dx = translate[0] * img_size[1]
        max_dy = translate[1] * img_size[0]
        translations = (np.round(random.uniform(-max_dx, max_dx)),
                        np.round(random.uniform(-max_dy, max_dy)))

        scale = (random.uniform(1 / scale_ranges[0], scale_ranges[0]),
                 random.uniform(1 / scale_ranges[1], scale_ranges[1]))

        return fov, anglex, angley, anglez, shear, translations, scale 

def __call__(self, img_group):
        for attempt in range(10):
            area = img_group[0].size[0] * img_group[0].size[1]
            target_area = random.uniform(0.08, 1.0) * area
            aspect_ratio = random.uniform(3. / 4, 4. / 3)

            w = int(round(math.sqrt(target_area * aspect_ratio)))
            h = int(round(math.sqrt(target_area / aspect_ratio)))

            if random.random() < 0.5:
                w, h = h, w

            if w <= img_group[0].size[0] and h <= img_group[0].size[1]:
                x1 = random.randint(0, img_group[0].size[0] - w)
                y1 = random.randint(0, img_group[0].size[1] - h)
                found = True
                break
        else:
            found = False
            x1 = 0
            y1 = 0

        if found:
            out_group = list()
            for img in img_group:
                img = img.crop((x1, y1, x1 + w, y1 + h))
                assert(img.size == (w, h))
                out_group.append(img.resize((self.size, self.size), self.interpolation))
            return out_group
        else:
            # Fallback
            scale = GroupScale(self.size, interpolation=self.interpolation)
            crop = GroupRandomCrop(self.size)
            return crop(scale(img_group)) 

def __init__(self, name, goal, min_tol, max_tol, max_move=100,
                 max_detect=1):
        super().__init__(name, goal, max_move=max_move, max_detect=max_detect)
        self.tolerance = random.uniform(max_tol, min_tol)
        self.stance = None
        self.orientation = None
        self.visible_pre = None