def main():
features = {}
fn = r"C:\Users\Neil\Desktop\R3 crack\raw PL images\cracked wafer PL image.tif"
im = ip.open_image(fn).astype(np.float32)
if im.shape[0] > 700:
print ' WARNING: Image resized'
im_max = im.max()
im = ndimage.zoom(im, 0.5)
if im.max() > im_max:
im[im > im_max] = im_max
if False:
view = ImageViewer(im)
view.show()
features['_alg_mode'] = 'mono wafer'
crop_props = cropping.crop_wafer_cz(im, create_mask=True, skip_crop=False)
features['corners'] = crop_props['corners']
cropped = cropping.correct_rotation(
im,
crop_props,
pad=False,
border_erode=parameters.BORDER_ERODE_CZ,
fix_chamfer=False)
mono_wafer.feature_extraction(cropped, crop_props, features=features)
ip.print_metrics(features)
rgb = mono_wafer.create_overlay(features)
view = ImageViewer(rgb)
view.show()
def run_block():
fn = r"C:\Users\Neil\BT\Data\blocks\misc\brick JW - Test PL Image %28PL Image%29.tif"
#fn = r"C:\Users\Neil\BT\Data\blocks\B4\691 - PL Image B4 N2 4V (PL Image - Composite).tif"
#fn = r"C:\Users\Neil\BT\Data\blocks\P3045564-20 ten times\.tif"
#fn = r"C:\Users\Neil\BT\Data\blocks\P3045564-20 ten times\427 - P3045564-20-1 (PL Image).tif"
im_pl = ip.open_image(fn).astype(np.float32)
features = {}
features_block.feature_extraction(im_pl, features)
rgb = features_block.create_overlay(features)
ip.print_metrics(features)
view = ImageViewer(im_pl)
ImageViewer(rgb)
view.show()
def analyse_module(features):
im = np.ascontiguousarray(features["_im_ratio_cropped"])
h, w = im.shape
# mask out rows and columns
border = 20
border_cols = features['_divisions_cols'] - features['_divisions_cols'][0]
for c in border_cols:
im[:, max(c - border, 0):min(c + border + 1, w)] = 0
border_rows = features['_divisions_rows'] - features['_divisions_rows'][0]
for r in border_rows:
im[max(r - border, 0):min(r + border + 1, h), :] = 0
# scale so max is around
scale = ((2**15) / im.max())
im *= scale
f = {}
hist = ip.histogram_percentiles(im, f, skip_zero=True)
hist = hist[:f['hist_percentile_99.9']]
hist_norm = hist / hist.max()
lower = np.where(hist_norm > 0.02)[0][0]
upper = 2 * f['hist_peak'] - lower
high_vals = (hist[upper:].sum() / float(hist.sum()))
features['module_bright_area_fraction'] = high_vals
if False:
print "%s: %0.01f%%" % (features['fn'], high_vals)
ip.print_metrics(f)
plt.figure()
plt.xlabel("PL/EL ratio")
plt.ylabel("Count")
plt.title("Above threshold: %0.02f%%" % high_vals)
xs = np.arange(len(hist)) / float(scale)
plt.plot(xs, hist)
plt.vlines([upper / float(scale)], ymin=0, ymax=hist.max())
if False:
plt.savefig(
os.path.join(r"C:\Users\Neil\Desktop\M1\hist",
features['fn'] + '_1.png'))
im = features["_im_ratio_cropped"]
im[im > f['hist_percentile_99']] = f['hist_percentile_99']
ip.save_image(
os.path.join(r"C:\Users\Neil\Desktop\M1\hist",
features['fn'] + '_0.png'), im)
else:
plt.show()
view = ImageViewer(im[::3, ::3])
view.show()
sys.exit()
def run_single(fn, mode, display=True, downsize=True):
features = {}
im = ip.open_image(fn).astype(np.float32)
if downsize and im.shape[0] > 750:
print ' WARNING: Image resized'
im_max = im.max()
im = ndimage.zoom(im, 0.5)
if im.max() > im_max:
im[im > im_max] = im_max
if False:
view = ImageViewer(im)
view.show()
features['_fn'] = os.path.splitext(os.path.split(fn)[1])[0]
if mode == "multi":
features['_alg_mode'] = 'multi wafer'
multi_cell.feature_extraction(im, features=features)
elif mode == "mono":
features['_alg_mode'] = 'mono wafer'
mono_cell.feature_extraction(im, features=features)
f = ip.print_metrics(features)
if display:
rgb = multi_cell.create_overlay(features)
view = ImageViewer(im)
ImageViewer(rgb)
view.show()
return f
def run_module():
if False:
fn_pl = r"C:\Users\Neil\BT\Data\modules\REC-144\REC-144_G00_LR0086_P35_2x2_OCPL.tif"
fn_el = r"C:\Users\Neil\BT\Data\modules\REC-144\REC-144_G00_LR0086_CC7.80_2x2_EL.tif"
elif False:
fn_pl = r"C:\Users\Neil\BT\Data\modules\REC-143\REC-143_G00_LR0086_P35_2x2_OCPL.tif"
fn_el = r"C:\Users\Neil\BT\Data\modules\REC-143\REC-143_G00_LR0086_CC7.50_2x2_EL.tif"
elif False:
fn_pl = r"C:\Users\Neil\BT\Data\modules\CNY-232\CNY-232_G00_LR0106_P93_2x2_OCPL.tif"
fn_el = r"C:\Users\Neil\BT\Data\modules\CNY-232\CNY-232_G00_LR0106_CC13.00_2x2_EL.tif"
elif True:
fn_pl = r"C:\Users\Neil\BT\Data\modules\STP-410\STP-410_G00_LR0052_P53_2x2_OCPL.tif"
fn_el = r"C:\Users\Neil\BT\Data\modules\STP-410\STP-410_G00_LR0045_CC5.50_2x2_EL.tif"
elif False:
fn_pl = r"C:\Users\Neil\BT\Data\modules\WIN-555\WIN-555_LR0245_P93_2x2_OCPL.tif"
fn_el = r"C:\Users\Neil\BT\Data\modules\WIN-555\WIN-555_LR0160_CV43.00_2x2_EL.tif"
elif False:
fn_pl = r"C:\Users\Neil\BT\Data\modules\APO-217\APO-217_G00_LR0089_P93_2x2_OCPL.tif"
fn_el = r"C:\Users\Neil\BT\Data\modules\APO-217\APO-217_G00_LR0089_CC13.00_2x2_EL.tif"
elif False:
fn_pl = r"C:\Users\Neil\BT\Data\modules\CNY-098\CNY-098_G00_LR0090_P93_2x2_OCPL.tif"
fn_el = r"C:\Users\Neil\BT\Data\modules\CNY-098\CNY-098_G00_LR0090_CC10.80_2x2_EL.tif"
elif False:
fn_el = r"C:\Users\Neil\BT\Data\modules\CNY-101\CNY-101_G00_LR0090_CC10.80_2x2_EL.tif"
fn_pl = r"C:\Users\Neil\BT\Data\modules\CNY-101\CNY-101_G00_LR0090_P93_2x2_OCPL.tif"
elif False:
fn_pl = r"C:\Users\Neil\BT\Data\modules\CNY-139\CNY-139_G00_LR0106_P93_2x2_OCPL.tif"
fn_el = r"C:\Users\Neil\BT\Data\modules\CNY-139\CNY-139_G00_LR0106_CC13.00_2x2_EL.tif"
elif False:
fn_pl = r"C:\Users\Neil\BT\Data\modules\CNY-232\CNY-232_G00_LR0106_P93_2x2_OCPL.tif"
fn_el = r"C:\Users\Neil\BT\Data\modules\CNY-232\CNY-232_G00_LR0106_CC13.00_2x2_EL.tif"
elif False:
fn_pl = r"C:\Users\Neil\BT\Data\modules\CNY-449\CNY-449_G00_LR0106_P93_2x2_OCPL.tif"
fn_el = r"C:\Users\Neil\BT\Data\modules\CNY-449\CNY-449_G00_LR0106_CC13.00_2x2_EL.tif"
im_pl = ip.open_image(fn_pl).astype(np.float32)
im_el = ip.open_image(fn_el).astype(np.float32)
features = {'fn': os.path.splitext(os.path.split(fn_pl)[1])[0]}
features_module.feature_extraction(im_pl, im_el, features)
ip.print_metrics(features)
ratio = features['im_pl_el']
view = ImageViewer(ratio[::4, ::4])
view.show()
def run_stripe():
if True:
mode = "mono"
# crack
fn = r"C:\Users\Neil\BT\Data\stripe\2017-09-07 Baccini 1 in 1\S0041_20170907.120013_Baccini 1 in 1 test_ID2_raw.tif"
# corner
#fn = r"C:\Users\Neil\BT\Data\stripe\2017-09-07 Baccini 1 in 1\S0041_20170907.113711_Baccini 1 in 1_ID5_raw.tif"
else:
mode = "multi"
fn = r"C:\Users\Neil\BT\Data\stripe\2017-09-07 Baccini 1 in 1\S0041_20170907.121040_Baccini 1 in 1 test_ID8_raw.tif"
im_pl = ip.open_image(fn).astype(np.float32)
features = {"mode": mode}
features_stripes.feature_extraction(im_pl, features)
rgb = features_stripes.create_overlay(features)
ip.print_metrics(features)
print ip.list_images(features)
view = ImageViewer(im_pl)
ImageViewer(features['bl_cropped_u8'])
ImageViewer(rgb)
view.show()
def run_plir():
fn = r"C:\Users\Neil\BT\Data\2017-09-06 TransferFunctions.TXT"
vals = features_block.load_transfer(fn)
spline_plir, spline_nf, spline_sp, spline_lp = features_block.interpolate_transfer(
vals, debug=False)
if False:
fn_sp = r"C:\Users\Neil\BT\Data\blocks\PLIR\Trina\2016-05-12\5.4V W (Uncalibrated PL Image) west short pass.tif"
fn_lp = r"C:\Users\Neil\BT\Data\blocks\PLIR\Trina\2016-05-12\5.4V W (Uncalibrated PL Image) west long pass.tif"
fn_nf = r"C:\Users\Neil\BT\Data\blocks\PLIR\Trina\2016-05-12\5.4V W (Uncalibrated PL Image) west no filter.tif"
elif False:
fn_sp = r"C:\Users\Neil\BT\Data\blocks\PLIR\marker\S0069_20170807.033044_ID4624_plg.meas.block.b3BL.north.sp.img.tif"
fn_lp = r"C:\Users\Neil\BT\Data\blocks\PLIR\marker\S0069_20170807.033044_ID4624_plg.meas.block.b3BL.north.lp.img.tif"
fn_nf = r"C:\Users\Neil\BT\Data\blocks\PLIR\marker\S0069_20170807.033044_ID4624_plg.meas.block.b3BL.north.std.img.tif"
else:
fn_sp = r"C:\Users\Neil\Desktop\Rietech.2.1172\tifs\plg.meas.block.b3bl.north.sp.img.tif"
fn_lp = r"C:\Users\Neil\Desktop\Rietech.2.1172\tifs\plg.meas.block.b3bl.north.lp.img.tif"
fn_nf = r"C:\Users\Neil\Desktop\Rietech.2.1172\tifs\plg.meas.block.b3pl.img.tif"
im_sp = ip.open_image(fn_sp, cast_long=False).astype(np.float32)
im_lp = ip.open_image(fn_lp, cast_long=False).astype(np.float32)
im_pl = ip.open_image(fn_nf, cast_long=False).astype(np.float32)
if False:
im_sp = ndimage.zoom(im_sp, zoom=0.5)
im_lp = ndimage.zoom(im_lp, zoom=0.5)
features = {}
features_block.plir(im_sp,
im_lp,
im_pl,
features,
spline_plir=spline_plir,
spline_plc=spline_nf)
ip.print_metrics(features)
log = np.log(features['im_tau_bulk_f32'])
view = ImageViewer(features['im_tau_bulk_f32'])
#ImageViewer(log)
view.show()
def run_plir2():
fn = r"C:\Users\Neil\BT\Data\2017-09-06 TransferFunctions.TXT"
vals = features_block.load_transfer(fn)
spline_plir, spline_nf, spline_sp, spline_lp = features_block.interpolate_transfer(
vals, debug=False)
if False:
fn_sp = r"C:\Users\Neil\BT\Data\blocks\PLIR\2017-11-01\plg.meas.block.b3bl.north.sp.img.tif"
fn_lp = r"C:\Users\Neil\BT\Data\blocks\PLIR\2017-11-01\plg.meas.block.b3bl.north.lp.img.tif"
elif False:
fn_sp = r"C:\Users\Neil\Desktop\1172\plg.meas.block.b3bl.north.sp.img.tif"
fn_lp = r"C:\Users\Neil\Desktop\1172\plg.meas.block.b3bl.north.lp.img.tif"
else:
fn_sp = r"C:\Users\Neil\Desktop\Rietech.2.1172\tifs\plg.meas.block.b3bl.north.sp.img.tif"
fn_lp = r"C:\Users\Neil\Desktop\Rietech.2.1172\tifs\plg.meas.block.b3bl.north.lp.img.tif"
im_sp = ip.open_image(fn_sp).astype(np.float32)
im_lp = ip.open_image(fn_lp).astype(np.float32)
if False:
im_sp = ndimage.zoom(im_sp, zoom=0.5)
im_lp = ndimage.zoom(im_lp, zoom=0.5)
features = {}
features_block.plir2(im_sp,
im_lp,
features,
spline_plir=spline_plir,
spline_sp=spline_sp)
ip.print_metrics(features)
log = np.log(features['im_tau_bulk_f32'])
view = ImageViewer(features['im_tau_bulk_f32'])
ImageViewer(log)
plt.figure()
plt.plot(features['im_tau_bulk_f32'].mean(axis=0))
view.show()
def run_single(fn, display=True, downsize=True):
features = {}
im = ip.open_image(fn).astype(np.float32)
if downsize and im.shape[0] > 750:
print ' WARNING: Image resized'
im_max = im.max()
im = ndimage.zoom(im, 0.5)
if im.max() > im_max:
im[im > im_max] = im_max
if False:
view = ImageViewer(im)
view.show()
parameters.SLOPE_MULTI_WAFER = True
parameters.BORDER_ERODE = 3
parameters.MIN_IMPURE_AREA = 0.01
features['_alg_mode'] = 'multi wafer'
features['_fn'] = os.path.splitext(os.path.split(fn)[1])[0]
crop_props = cropping.crop_wafer(im, create_mask=True)
features['corners'] = crop_props['corners']
cropped = cropping.correct_rotation(im,
crop_props,
pad=False,
border_erode=parameters.BORDER_ERODE)
multi_wafer.feature_extraction(cropped, crop_props, features=features)
multi_wafer.combined_features(features)
rgb = multi_wafer.create_overlay(features)
f = ip.print_metrics(features, display=display)
if display:
print "Wafer type: %s" % multi_wafer.WaferType.types[
features['wafer_type']]
view = ImageViewer(rgb)
ImageViewer(im)
view.show()
return f, features['im_cropped_u8'], rgb
def run_single(fn, display=True, downsize=True):
if False:
mode = "mono"
else:
mode = "multi"
features = {"_cell_type": mode}
im = ip.open_image(fn).astype(np.float32)
if False:
view = ImageViewer(im)
view.show()
skip_crop = True
features_stripes.feature_extraction(im, features, skip_crop)
f = ip.print_metrics(features)
if display:
view = ImageViewer(im)
rgb = features_stripes.create_overlay(features)
ImageViewer(rgb)
view.show()
return f