initial commit
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import os
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import numpy as np
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import scipy.fft
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from scipy import ndimage
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import cv2
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class Stitcher:
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def __init__(self,isstitch=False, n=[3,3], index=np.array([1,2,3,6,7,8,11,12,13])):
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# Settings
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self.isstitch = isstitch
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# stitch related
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self.stitch_valid = False
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self.boxes = self.units = None
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self.index = index
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self.stitch_calibration = self.tile_list = []
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self.ncol = n[0]
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self.nrow = n[1]
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self.nominal_ov_pos=100
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self.margin=10
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self.bound=self.nominal_ov_pos - self.margin
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# for sensor 1
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self.pos_default = [(260, 56), (577, 62), (897, 69), (1214, 75), (1530, 81), (256, 391), (570, 397), (889, 403), (1208, 409), (1523, 416), (248, 724), (563, 730), (882, 738), (1201, 743), (1516, 751)]
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# for sensor 2
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# self.pos_default = [(195, 61), (511, 61), (831, 61), (1150, 60), (1464, 58), (195, 396), (512, 395), (830, 394), (1149, 394), (1465, 393), (197, 731), (512, 728), (831, 730), (1151, 728), (1466, 728)]
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# for sensor 3
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# self.pos_default = [(207, 39), (547, 40), (887, 42), (1227, 42), (1564, 44), (207, 397), (546, 399), (884, 399), (1225, 401), (1563, 403), (200, 755), (543, 757), (883, 758), (1223, 758), (1562, 760)]
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def stitch_rt_optimized(self, change_sv=False, s_mean=28., v_mean=180.):
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tile_list = self.tile_list
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stitch_calibration = self.stitch_calibration
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ncol = self.ncol
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nrow = self.nrow
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processed_tiles = tile_list
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img_stitched_height = None
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for i in range(nrow):
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# Start with first tile of the row
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img_stitched_width = tile_list[i * ncol]
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# Stitch horizontally within the row
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for j in range(1, ncol):
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img_1 = img_stitched_width # Already stitched part
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img_2 = processed_tiles[i * ncol + j] # if change_sv else tile_list[i * ncol + j]
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ov_pos = int(stitch_calibration[i * ncol + j - 1][1])
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ov_pos, ov1, ov2 = self.__find_ov(img_1, img_2, ov_pos, dim=1)
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# Use optimized stitch function
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img_stitched_width = self.__stitch_optimized(img_1, img_2, ov_pos, ov1, ov2, dim=1, blend=True)
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# Stitch rows vertically
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if i == 0:
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img_stitched_height = img_stitched_width
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else:
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# Handle width mismatch more efficiently
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width_h = img_stitched_height.shape[1]
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width_w = img_stitched_width.shape[1]
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wdiff = width_h - width_w
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if wdiff != 0:
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# Use numpy pad instead of hstack for better performance
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if wdiff > 0:
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# Pad right side of the narrower image
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padding = ((0, 0), (0, wdiff), (0, 0)) if len(img_stitched_width.shape) == 3 else ((0, 0), (0, wdiff))
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img_stitched_width = np.pad(img_stitched_width, padding, mode='edge')
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else:
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# Crop the wider image
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img_stitched_width = img_stitched_width[:, :width_h]
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ov_pos_h = int(stitch_calibration[(i + 1) * nrow - 1][1])
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ov_pos_h, ov_h1, ov_h2 = self.__find_ov(img_stitched_height, img_stitched_width, ov_pos_h, dim=0)
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img_stitched_height = self.__stitch_optimized(
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img_stitched_height, img_stitched_width, ov_pos_h, ov_h1, ov_h2, dim=0, blend=True
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)
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return img_stitched_height
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def __find_ov(self, img1, img2, ov_pos, dim=1):
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_dim = len(img1.shape)
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if dim == 1:
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if _dim == 3:
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# RGB case
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ov_1 = img1[:, -ov_pos:, :]
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ov_2 = img2[:, :ov_pos, :]
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else:
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# gray scale
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ov_1 = img1[:, -ov_pos:]
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ov_2 = img2[:, :ov_pos]
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elif dim == 0:
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if _dim == 3:
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ov_1 = img1[-ov_pos:, :, :]
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ov_2 = img2[:ov_pos, :, :]
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else:
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# gray scale
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ov_1 = img1[-ov_pos:, :]
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ov_2 = img2[:ov_pos, :]
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return ov_pos, ov_1, ov_2
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def find_stitch_boxes(self,img_src,show = False):
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img_corr = self.__correct_edge_bloom(img_src)
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self.__detect_nonblack_regions(img_corr,show = show)
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return len(self.boxes)
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# return self.boxes
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def get_stitch_unit(self, img, flip_x=True, flip_y=True,index = np.arange(15)):
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"""
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Extract each tile given by boxes (x0,y0,x1,y1), default flip both X and Y due to inverting lens.
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- img: source image (use corrected image if you prefer)
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- boxes: list of (x0,y0,x1,y1)
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- flip_x, flip_y: booleans (performed sequentially: X then Y)
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- overlay_original: if True paste onto original image copy, otherwise onto blank background
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- bg_color: background color for blank canvas (B,G,R)
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Returns: canvas image and list of flipped tiles as (x0,y0,flipped_tile)
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"""
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h, w = img.shape[:2]
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if self.units is None:
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flipped_units = []
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for i, (x0, y0, x1, y1) in enumerate(self.boxes):
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# clamp coordinates
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x0 = max(0, int(x0)); y0 = max(0, int(y0))
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x1 = min(w, int(x1)); y1 = min(h, int(y1))
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if x1 <= x0 or y1 <= y0:
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continue
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tile = img[y0:y1, x0:x1].copy()
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# flip X then Y in default
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if flip_x:
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tile = cv2.flip(tile, 1) # horizontal
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if flip_y:
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tile = cv2.flip(tile, 0) # vertical
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flipped_units.append((x0, y0, tile))
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self.units = flipped_units
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else:
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flipped_units = self.units
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index = self.index
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for i, (x0, y0, x1, y1) in enumerate(self.boxes):
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if i not in index:
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continue
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# clamp coordinates
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x0 = max(0, int(x0)); y0 = max(0, int(y0))
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x1 = min(w, int(x1)); y1 = min(h, int(y1))
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if x1 <= x0 or y1 <= y0:
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continue
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tile = img[y0:y1, x0:x1].copy()
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# flip X then Y in default
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if flip_x:
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tile = cv2.flip(tile, 1) # horizontal
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if flip_y:
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tile = cv2.flip(tile, 0) # vertical
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# Assign instead of append
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flipped_units[i] = (x0, y0, tile)
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self.units = flipped_units
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return True
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def extract_tiles(self):
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if not self.isstitch:
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tile_idx = self.__get_tile_index_by_position(self.pos_default[7][0], self.pos_default[7][1])
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if tile_idx is None:
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tile_idx = 7
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# Ensure index is always iterable (list or array)
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self.index = [tile_idx] if isinstance(tile_idx, int) else tile_idx
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self.ncol = 1
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self.nrow = 1
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tile_list = self.__get_tile_by_index(self.index)
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self.tile_list = tile_list
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return tile_list
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def stitch_cal(self):
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# init
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tile_list = self.tile_list
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img_stitched_height = None
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stitch_calibration = []
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# Flip col and row for porting
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nrow, ncol = self.ncol, self.nrow
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if not self.isstitch:
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return self.tile_list[0], None
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for i in range(ncol):
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img_stitched_width = None
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for j in range(nrow):
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if j == 0:
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img_j0 = tile_list[i*ncol]
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img_stitched_width = img_j0
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else:
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img_1 = tile_list[i*ncol + j-1]
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img_2 = tile_list[i*ncol + j]
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ov_pos, ov1, ov2, c_corr = self.__get_ov(img_1.astype(np.float32), img_2.astype(np.float32), dim=1)
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stitch_calibration.append(["hor {}".format(j-1),ov_pos])
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# stitch images with stacking
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img_stitched_width = self.__stitch_optimized(img_stitched_width, img_2, ov_pos, ov1, ov2, blend=True, dim=1)
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if i == 0:
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img_stitched_height = img_stitched_width
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stitch_calibration.append(["ver {}".format(i),0])
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pass
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else:
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width = img_stitched_height.shape[1]
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width_new = img_stitched_width.shape[1]
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wdiff = width - width_new
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if wdiff > 0:
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img_stitched_width = np.hstack((img_stitched_width, img_stitched_width[:, -wdiff:]))
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elif wdiff < 0:
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img_stitched_width = img_stitched_width[:, :wdiff]
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ov_pos_h, ov_h1, ov_h2, c_corr = self.__get_ov(img_stitched_height, img_stitched_width, dim=0)
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stitch_calibration.append(["ver {}".format(i),ov_pos_h])
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img_stitched_height = self.__stitch_optimized(img_stitched_height, img_stitched_width, ov_pos_h, ov_h1, ov_h2, blend=True, dim=0)
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self.stitch_calibration = stitch_calibration
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return img_stitched_height, stitch_calibration
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def __stitch_optimized(self, img1, img2, ov_pos, ov1, ov2, blend=True, dim=1):
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_dim = len(img1.shape)
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# Early return if no blending needed
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if not blend:
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ov = ov1
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elif ov_pos <= 1:
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ov = ov1
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else:
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# Vectorized blending
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blend_coef = np.linspace(1.0, 0.0, ov_pos, dtype=np.float32)
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# Prepare blend_coef for broadcasting
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if dim == 1: # Horizontal blending
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if _dim == 3:
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# Shape: (1, ov_pos, 1)
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blend_coef = blend_coef.reshape(1, ov_pos, 1)
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else:
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# Shape: (1, ov_pos)
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blend_coef = blend_coef.reshape(1, ov_pos)
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else: # Vertical blending
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if _dim == 3:
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# Shape: (ov_pos, 1, 1)
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blend_coef = blend_coef.reshape(ov_pos, 1, 1)
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else:
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# Shape: (ov_pos, 1)
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blend_coef = blend_coef.reshape(ov_pos, 1)
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# Compute blended overlap in one operation
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ov = (ov1.astype(np.float32) * blend_coef +
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ov2.astype(np.float32) * (1 - blend_coef)).astype(np.uint8)
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# Avoid unnecessary .copy() calls when possible
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if dim == 1:
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if _dim == 3:
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# Use np.take or slicing without copy when safe
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left = img1[:, :img1.shape[1] - ov_pos, :] if img1.shape[1] > ov_pos else img1[:, :0, :]
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right = img2[:, ov_pos:, :]
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stitched_img = np.concatenate((left, ov, right), axis=1)
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else:
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left = img1[:, :img1.shape[1] - ov_pos] if img1.shape[1] > ov_pos else img1[:, :0]
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right = img2[:, ov_pos:]
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stitched_img = np.concatenate((left, ov, right), axis=1)
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else:
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if _dim == 3:
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top = img1[:img1.shape[0] - ov_pos, :, :] if img1.shape[0] > ov_pos else img1[:0, :, :]
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bottom = img2[ov_pos:, :, :]
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stitched_img = np.concatenate((top, ov, bottom), axis=0)
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else:
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top = img1[:img1.shape[0] - ov_pos, :] if img1.shape[0] > ov_pos else img1[:0, :]
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bottom = img2[ov_pos:, :]
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stitched_img = np.concatenate((top, ov, bottom), axis=0)
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return stitched_img
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def __get_ov(self, img1, img2, dim=1):
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'''
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default dimension is along axis 1 (width of the images)
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'''
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# pass parameters
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nominal_ov = self.nominal_ov_pos
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bound_ov = self.bound
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cross_corr = []
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h1, w1 = img1.shape[:2]
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h2, w2 = img2.shape[:2]
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# clamp tested overlap to valid range (1..min dimension)
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max_possible = w1 if dim == 1 else h1
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max_possible = min(max_possible, w2 if dim == 1 else h2)
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lo = max(1, nominal_ov - bound_ov)
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hi = min(max_possible, nominal_ov + bound_ov)
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ov_space = range(lo, hi + 1)
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for ov in ov_space:
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if dim == 1:
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ov_area1 = img1[:, max(0, w1 - ov):, :]
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ov_area2 = img2[:, :min(ov, w2), :]
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elif dim == 0:
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ov_area1 = img1[max(0, h1 - ov):, :, :]
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ov_area2 = img2[:min(ov, h2), :, :]
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# require exact shape match (detect_nonblack_regions guarantees uniform tile size)
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if ov_area1.size == 0 or ov_area2.size == 0 or ov_area1.shape != ov_area2.shape:
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cross_corr.append(-1.0)
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continue
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# multi-channel cross correlation (average over channels)
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corr = 0.0
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for i in range(min(3, ov_area1.shape[2])):
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a = ov_area1[:, :, i].astype(np.float32)
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b = ov_area2[:, :, i].astype(np.float32)
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denom = np.sqrt(np.sum(a * a) * np.sum(b * b)) + 1e-12
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corr += float(np.sum(a * b) / denom)
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corr = corr / float(min(3, ov_area1.shape[2]))
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cross_corr.append(corr)
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# pick best overlap (ignore invalid entries)
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best_idx = int(np.argmax(cross_corr))
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ov_pos = list(ov_space)[best_idx]
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# extract the overlap regions (assume shapes match)
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if dim == 1:
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ov_1 = img1[:, max(0, w1 - ov_pos):, :].copy()
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ov_2 = img2[:, :min(ov_pos, w2), :].copy()
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else:
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ov_1 = img1[max(0, h1 - ov_pos):, :, :].copy()
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ov_2 = img2[:min(ov_pos, h2), :, :].copy()
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return ov_pos, ov_1, ov_2, cross_corr
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# Helper functions for stitching
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def __get_tile_index_by_position(self, x, y, tol=20):
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"""
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Find flipped tile whose top-left matches (x,y) within tol pixels.
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flipped_units: list of (x,y,tile)
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"""
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i= 0
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for xi, yi, tile in self.units:
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if abs(int(x) - int(xi)) <= tol and abs(int(y) - int(yi)) <= tol:
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# return tile
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return i
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i += 1
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return None
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def __get_tile_by_index(self, idx):
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"""
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Retrieve tile by index
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"""
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if isinstance(idx, int):
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idx = [idx]
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if len(idx) > len(self.units):
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raise ValueError("Index exceeds number of tiles.")
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tile_list = []
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for i in range(len(idx)):
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[xi, yi, tile] = self.units[idx[i]]
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tile_list.append(tile)
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return tile_list
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def __correct_edge_bloom(self, img, method="divide", kernel_size=None, clip=True):
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"""
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Simple illumination correction to reduce edge bloom / vignetting. Only for getting tile boxes.
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- method: "divide" (default) or "subtract"
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* divide: img_corrected = (img / (bg + eps)) * gain -- preserves colors
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* subtract: img_corrected = img - (bg - mean_bg) -- removes low-freq background
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- kernel_size: size for large-kernel blur used as background estimate (odd int).
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If None, chosen from image size.
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Returns corrected uint8 image.
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"""
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if img is None:
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raise ValueError("Input image is None.")
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h, w = img.shape[:2]
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if kernel_size is None:
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# choose large kernel relative to image size, ensure odd
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k = max(31, min(h, w) // 2)
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if k % 2 == 0:
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k += 1
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kernel_size = k
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# convert to float32 for stable ops
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img_f = img.astype(np.float32)
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# estimate background by large Gaussian blur (works well for smooth bloom)
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bg = cv2.GaussianBlur(img_f, (kernel_size, kernel_size), 0)
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if method == "subtract":
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# remove low-frequency background but keep overall brightness
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mean_bg = bg.mean(axis=(0, 1), keepdims=True)
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corr = img_f - (bg - mean_bg)
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else:
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# divide: normalized gain correction
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eps = 1e-6
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# compute gain so that mid-level stays near 128
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mean_bg = bg.mean(axis=(0, 1), keepdims=True) + eps
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gain = (mean_bg * 0.5) # target scale; 0.5 keeps values reasonable
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corr = (img_f / (bg + eps)) * gain
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if clip:
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corr = np.clip(corr, 0, 255).astype(np.uint8)
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else:
|
||||
corr = corr.astype(np.uint8)
|
||||
return corr
|
||||
|
||||
def __get_unit_pos(self,idx):
|
||||
|
||||
if isinstance(idx, int):
|
||||
idx = [idx]
|
||||
|
||||
if len(idx) > len(self.units):
|
||||
raise ValueError("Index exceeds number of vision units.")
|
||||
|
||||
pos_list = []
|
||||
for i in range(len(idx)):
|
||||
[xi, yi, tile] = self.units[idx[i]]
|
||||
pos_list.append((xi, yi))
|
||||
|
||||
return pos_list
|
||||
|
||||
# Computation Functions
|
||||
def __detect_nonblack_regions(self, img, morph_k=3, n_boxes_target=15, bk_thresh_step=10, bk_thresh_max=100,show = False):
|
||||
"""
|
||||
Return binary mask of non-black regions and list of bounding boxes (x,y,w,h).
|
||||
Uses adaptive thresholding on grayscale + small morphology to be robust to illumination.
|
||||
|
||||
New behavior: after finding candidate boxes, center-crop all boxes to the same
|
||||
width/height (smallest found) so returned boxes share a common size.
|
||||
"""
|
||||
if img is None:
|
||||
raise ValueError("Input image is None.")
|
||||
|
||||
h, w = img.shape[:2]
|
||||
n_boxes = 0
|
||||
bk_thresh = 35 # initial black threshold
|
||||
bk_thresh_step = 5
|
||||
bk_thresh_max = 100
|
||||
|
||||
# Also include a low global V threshold to capture colored but dark tiles
|
||||
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
|
||||
v = hsv[:,:,2]
|
||||
ww = hh = 0
|
||||
while n_boxes != n_boxes_target and bk_thresh <= bk_thresh_max:
|
||||
_, v_mask = cv2.threshold(v, bk_thresh, 255, cv2.THRESH_BINARY) # tune 50 if needed
|
||||
|
||||
mask = v_mask
|
||||
|
||||
# Morphology: remove small specks, fill small holes
|
||||
k = max(1, morph_k)
|
||||
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (k, k))
|
||||
mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel, iterations=1)
|
||||
mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel, iterations=2)
|
||||
|
||||
# find contours of candidate regions
|
||||
contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
|
||||
|
||||
boxes = []
|
||||
for c in contours:
|
||||
x,y,ww,hh = cv2.boundingRect(c)
|
||||
boxes.append((x,y,ww,hh))
|
||||
|
||||
# filter boxes by size (ignore too small or too large tiles)
|
||||
boxes = self.__filter_boxes_by_size(boxes, (h, w))
|
||||
n_boxes = len(boxes)
|
||||
bk_thresh += bk_thresh_step
|
||||
|
||||
# print("Detected {} candidate boxes with bk_thresh={}".format(n_boxes, bk_thresh - bk_thresh_step))
|
||||
if n_boxes == 0:
|
||||
self.boxes = []
|
||||
return n_boxes
|
||||
# compute common center-crop size: use minimum width/height among boxes
|
||||
widths = [b[2] for b in boxes]
|
||||
heights = [b[3] for b in boxes]
|
||||
target_w = max(1,int(min(widths)))
|
||||
target_h = max(1,int(min(heights)))
|
||||
|
||||
# center-crop each box to (target_w, target_h) inside original box bounds
|
||||
cropped_boxes = []
|
||||
for (x, y, ww, hh) in boxes:
|
||||
cx = x + ww // 2
|
||||
cy = y + hh // 2
|
||||
nx0 = int(cx - target_w // 2)
|
||||
ny0 = int(cy - target_h // 2)
|
||||
# clamp to image bounds
|
||||
nx0 = max(0, min(nx0, w - target_w))
|
||||
ny0 = max(0, min(ny0, h - target_h))
|
||||
# Convert to (x0, y0, x1, y1) format for direct image region retrieval
|
||||
nx1 = nx0 + target_w
|
||||
ny1 = ny0 + target_h
|
||||
cropped_boxes.append((nx0, ny0, nx1, ny1))
|
||||
|
||||
# sort boxes left-to-right, top-to-bottom (useful for grid)
|
||||
cropped_boxes = sorted(cropped_boxes, key=lambda b: (b[1], b[0]))
|
||||
|
||||
self.boxes = cropped_boxes
|
||||
img_with_boxes = self.draw_boxes(img= img)
|
||||
if show:
|
||||
cv2.imshow("Detected Boxes", img_with_boxes)
|
||||
cv2.waitKey(0)
|
||||
|
||||
img_path = "./config/compound_eye.png"
|
||||
if os.path.exists(img_path):
|
||||
cv2.imwrite(img_path, img_with_boxes)
|
||||
else:
|
||||
os.makedirs(os.path.dirname(img_path), exist_ok=True)
|
||||
cv2.imwrite(img_path, img_with_boxes)
|
||||
# print("Check if compound eye detection image valid: {}".format(img_path))
|
||||
return cropped_boxes
|
||||
|
||||
def draw_boxes(self, img, thickness=2):
|
||||
"""
|
||||
Draw bounding boxes on the image.
|
||||
- img: input image (will be copied, not modified in-place)
|
||||
- thickness: line thickness
|
||||
Returns: image with drawn boxes
|
||||
"""
|
||||
if self.boxes is None or len(self.boxes) == 0:
|
||||
print("No boxes to draw")
|
||||
return img
|
||||
|
||||
img_draw = img.copy()
|
||||
color=(0, 255, 0)
|
||||
|
||||
for i, (x0, y0, x1, y1) in enumerate(self.boxes):
|
||||
# Draw rectangle
|
||||
cv2.rectangle(img_draw, (x0, y0), (x1, y1), color, thickness)
|
||||
|
||||
# Draw index
|
||||
text_size = cv2.getTextSize(str(i), cv2.FONT_HERSHEY_SIMPLEX, 0.6, 2)[0]
|
||||
text_pos = (x0 + 5, y0 + text_size[1] + 5)
|
||||
# Draw text background and text
|
||||
cv2.rectangle(img_draw, (text_pos[0] - 2, text_pos[1] - text_size[1] - 2),
|
||||
(text_pos[0] + text_size[0] + 2, text_pos[1] + 2), (0, 0, 0), -1)
|
||||
cv2.putText(img_draw, str(i), text_pos, cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2)
|
||||
|
||||
return img_draw
|
||||
|
||||
|
||||
|
||||
def __filter_boxes_by_size(self, boxes, img_shape, min_area_frac=0.018, max_area_frac=0.035):
|
||||
"""
|
||||
Remove boxes that are too small or too large relative to the image.
|
||||
- boxes: list of (x,y,w,h)
|
||||
- img_shape: (height, width)
|
||||
- min_area_frac / max_area_frac: allowed area fraction of image
|
||||
Returns filtered list of boxes.
|
||||
"""
|
||||
h, w = img_shape
|
||||
img_area = h * w
|
||||
min_area = img_area * float(min_area_frac)
|
||||
max_area = img_area * float(max_area_frac)
|
||||
filtered = []
|
||||
for (x,y,ww,hh) in boxes:
|
||||
area = ww * hh
|
||||
if area < min_area or area > max_area or (abs(ww - hh) > 20):
|
||||
continue
|
||||
filtered.append((x,y,ww,hh))
|
||||
return filtered
|
||||
Reference in New Issue
Block a user