Python實現隨機從圖像中獲取多個patch
經常有一些圖像任務需要從一張大圖中截取固定大小的patch來進行訓練。這裡面常常存在下面幾個問題:
- patch的位置盡可能隨機,不然數據豐富性可能不夠,容易引起過擬合
- 如果原圖較大,讀圖帶來的IO開銷可能會非常大,影響訓練速度,所以最好一次能夠截取多個patch
- 我們經常不太希望因為隨機性的存在而使得圖像中某些區域沒有被覆蓋到,所以還需要註意patch位置的覆蓋程度
基於以上問題,我們可以使用下面的策略從圖像中獲取位置隨機的多個patch:
- 以固定的stride獲取所有patch的左上角坐標
- 對左上角坐標進行隨機擾動
- 對patch的左上角坐標加上寬和高得到右下角坐標
- 檢查patch的坐標是否超出圖像邊界,如果超出則將其收進來,收的過程應保證patch尺寸不變
- 加入ROI(Region Of Interest)功能,也就是說patch不一定非要在整張圖中獲取,而是可以指定ROI區域
下面是實現代碼和例子:
註意下面代碼隻是獲取瞭patch的bounding box,並沒有把patch截取出來。
# -*- coding: utf-8 -*- import cv2 import numpy as np def get_random_patch_bboxes(image, bbox_size, stride, jitter, roi_bbox=None): """ Generate random patch bounding boxes for a image around ROI region Parameters ---------- image: image data read by opencv, shape is [H, W, C] bbox_size: size of patch bbox, one digit or a list/tuple containing two digits, defined by (width, height) stride: stride between adjacent bboxes (before jitter), one digit or a list/tuple containing two digits, defined by (x, y) jitter: jitter size for evenly distributed bboxes, one digit or a list/tuple containing two digits, defined by (x, y) roi_bbox: roi region, defined by [xmin, ymin, xmax, ymax], default is whole image region Returns ------- patch_bboxes: randomly distributed patch bounding boxes, n x 4 numpy array. Each bounding box is defined by [xmin, ymin, xmax, ymax] """ height, width = image.shape[:2] bbox_size = _process_geometry_param(bbox_size, min_value=1) stride = _process_geometry_param(stride, min_value=1) jitter = _process_geometry_param(jitter, min_value=0) if bbox_size[0] > width or bbox_size[1] > height: raise ValueError('box_size must be <= image size') if roi_bbox is None: roi_bbox = [0, 0, width, height] # tl is for top-left, br is for bottom-right tl_x, tl_y = _get_top_left_points(roi_bbox, bbox_size, stride, jitter) br_x = tl_x + bbox_size[0] br_y = tl_y + bbox_size[1] # shrink bottom-right points to avoid exceeding image border br_x[br_x > width] = width br_y[br_y > height] = height # shrink top-left points to avoid exceeding image border tl_x = br_x - bbox_size[0] tl_y = br_y - bbox_size[1] tl_x[tl_x < 0] = 0 tl_y[tl_y < 0] = 0 # compute bottom-right points again br_x = tl_x + bbox_size[0] br_y = tl_y + bbox_size[1] patch_bboxes = np.concatenate((tl_x, tl_y, br_x, br_y), axis=1) return patch_bboxes def _process_geometry_param(param, min_value): """ Process and check param, which must be one digit or a list/tuple containing two digits, and its value must be >= min_value Parameters ---------- param: parameter to be processed min_value: min value for param Returns ------- param: param after processing """ if isinstance(param, (int, float)) or \ isinstance(param, np.ndarray) and param.size == 1: param = int(np.round(param)) param = [param, param] else: if len(param) != 2: raise ValueError('param must be one digit or two digits') param = [int(np.round(param[0])), int(np.round(param[1]))] # check data range using min_value if not (param[0] >= min_value and param[1] >= min_value): raise ValueError('param must be >= min_value (%d)' % min_value) return param def _get_top_left_points(roi_bbox, bbox_size, stride, jitter): """ Generate top-left points for bounding boxes Parameters ---------- roi_bbox: roi region, defined by [xmin, ymin, xmax, ymax] bbox_size: size of patch bbox, a list/tuple containing two digits, defined by (width, height) stride: stride between adjacent bboxes (before jitter), a list/tuple containing two digits, defined by (x, y) jitter: jitter size for evenly distributed bboxes, a list/tuple containing two digits, defined by (x, y) Returns ------- tl_x: x coordinates of top-left points, n x 1 numpy array tl_y: y coordinates of top-left points, n x 1 numpy array """ xmin, ymin, xmax, ymax = roi_bbox roi_width = xmax - xmin roi_height = ymax - ymin # get the offset between the first top-left point of patch box and the # top-left point of roi_bbox offset_x = np.arange(0, roi_width, stride[0])[-1] + bbox_size[0] offset_y = np.arange(0, roi_height, stride[1])[-1] + bbox_size[1] offset_x = (offset_x - roi_width) // 2 offset_y = (offset_y - roi_height) // 2 # get the coordinates of all top-left points tl_x = np.arange(xmin, xmax, stride[0]) - offset_x tl_y = np.arange(ymin, ymax, stride[1]) - offset_y tl_x, tl_y = np.meshgrid(tl_x, tl_y) tl_x = np.reshape(tl_x, [-1, 1]) tl_y = np.reshape(tl_y, [-1, 1]) # jitter the coordinates of all top-left points tl_x += np.random.randint(-jitter[0], jitter[0] + 1, size=tl_x.shape) tl_y += np.random.randint(-jitter[1], jitter[1] + 1, size=tl_y.shape) return tl_x, tl_y if __name__ == '__main__': image = cv2.imread('1.bmp') patch_bboxes = get_random_patch_bboxes( image, bbox_size=[64, 96], stride=[128, 128], jitter=[32, 32], roi_bbox=[500, 200, 1500, 800]) colors = [ (255, 0, 0), (0, 255, 0), (0, 0, 255), (255, 255, 0), (255, 0, 255), (0, 255, 255)] color_idx = 0 for bbox in patch_bboxes: color_idx = color_idx % 6 pt1 = (bbox[0], bbox[1]) pt2 = (bbox[2], bbox[3]) cv2.rectangle(image, pt1, pt2, color=colors[color_idx], thickness=2) color_idx += 1 cv2.namedWindow('image', 0) cv2.imshow('image', image) cv2.waitKey(0) cv2.destroyAllWindows() cv2.imwrite('image.png', image)
在實際應用中可以進一步增加一些簡單的功能:
1.根據位置增加一些過濾功能。比如說太靠近邊緣的給剔除掉,有些算法可能有比較嚴重的邊緣效應,所以此時我們可能不太想要邊緣的數據加入訓練
2.也可以根據某些簡單的算法策略進行過濾。比如在超分辨率這樣的任務中,我們可能一般不太關心面積非常大的平坦區域,比如純色墻面,大片天空等,此時可以使用方差進行過濾
3.設置最多保留數目。有時候原圖像的大小可能有很大差異,此時利用上述方法得到的patch數量也就隨之有很大的差異,然而為瞭保持訓練數據的均衡性,我們可以設置最多保留數目,為瞭確保覆蓋程度,一般需要在截取之前對patch進行shuffle,或者計算stride
以上就是Python實現隨機從圖像中獲取多個patch的詳細內容,更多關於Python圖像獲取patch的資料請關註WalkonNet其它相關文章!
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