def get_target_image(parsed, target, path=None, file=None, raw_stitch=False): if PAIR in target: (a,b) = target[PAIR] a_image = read_target_image(a, path=path, file=file) b_image = read_target_image(b, path=path, file=file) if raw_stitch: return stitch_raw((a,b),(a_image,b_image),background=180) else: try: bin_zip = get_product_file(parsed, BINZIP_PRODUCT) if os.path.exists(bin_zip): # name is target LID + png extension png_name = os.path.basename(target[PID]) + '.png' png_data = get_zip_entry_bytes(bin_zip, png_name) pil_img = PIL.Image.open(StringIO(png_data)) return np.array(pil_img.convert('L')) # convert to 8-bit grayscale except NotFound: pass im,_ = v1_stitching.stitch((a,b),(a_image,b_image)) return im else: return read_target_image(target, path=path, file=file)
def stitch(targets,images): # compute bounds relative to the camera field (x,y,w,h) = stitched_box(targets) # note that w and h are switched from here on out to rotate 90 degrees. # step 1: compute masks s = as_pil(stitch_raw(targets,images,(x,y,w,h))) # stitched ROI's with black gaps rois_mask = as_pil(mask(targets)) # a mask of where the ROI's are gaps_mask = ImageChops.invert(rois_mask) # its inverse is where the gaps are edges = edges_mask(targets,images) # edges are pixels along the ROI edges # step 2: estimate background from edges # compute the mean and variance of the edges (mean,variance) = bright_mv(s,edges) # now use that as an estimated background flat_bg = Image.new('L',(h,w),mean) # FIXME s.paste(mean,None,gaps_mask) # step 3: compute "probable background": low luminance delta from estimated bg bg = extract_background(s,flat_bg) # also mask out the gaps, which are not "probable background" bg.paste(255,None,gaps_mask) # step 3a: improve mean/variance estimate (mean,variance) = bright_mv(bg) std_dev = sqrt(variance) # step 4: sample probable background to compute RBF for illumination gradient # grid div = 6 means = [] nodes = [] rad = avg([h,w]) / div rad_step = int(rad/2)+1 for x in range(0,h+rad,rad): for y in range(0,w+rad,rad): for r in range(rad,max(h,w),int(rad/3)+1): box = (max(0,x-r),max(0,y-r),min(h-1,x+r),min(w-1,y+r)) region = bg.crop(box) nabe = region.histogram() (m,v) = bright_mv_hist(nabe) if m > 0 and m < 255: # reject outliers nodes.append((x,y)) means.append(m) break # now construct radial basis functions for mean, based on the samples mean_rbf = interpolate.Rbf([x for x,y in nodes], [y for x,y in nodes], means, epsilon=rad) # step 5: fill gaps with mean based on RBF and variance from bright_mv(edges) mask_pix = gaps_mask.load() noise = Image.new('L',(h,w),mean) noise_pix = noise.load() np.random.seed(0) gaussian = np.random.normal(0, 1.0, size=(h,w)) # it's normal std_dev *= 0.66 # err on the side of smoother rather than noisier mask_x = [] mask_y = [] for x in xrange(h): for y in xrange(w): if mask_pix[x,y] == 255: # only for pixels in the mask mask_x.append(x) mask_y.append(y) rbf_fill = mean_rbf(np.array(mask_x), np.array(mask_y)) for x,y,r in zip(mask_x, mask_y, rbf_fill): # fill is illumination gradient + noise noise_pix[x,y] = r + (gaussian[x,y] * std_dev) # step 6: final composite s.paste(noise,None,gaps_mask) return (np.array(s),rois_mask)