libcamera/vivid.git - libcamera pipeline handler for VIVID

Age	Commit message (Expand)	Author
2021-11-15	ipa: ipu3: agc: Update previous exposure value	Jean-Michel Hautbois
2021-11-15	ipa: ipu3: agc: Improve gain calculation	Jean-Michel Hautbois
2021-11-15	ipa: ipu3: agc: Refactor ev gain calculation and testing	Jean-Michel Hautbois
2021-11-15	ipa: ipu3: agc: Compute the gain for each frame	Jean-Michel Hautbois
2021-11-15	ipa: ipu3: agc: Limit the number of saturated cells	Jean-Michel Hautbois
2021-11-15	ipa: ipu3: Use sensor controls to update frameContext	Jean-Michel Hautbois
2021-11-15	ipu3: ipa: Report effective sensor controls with statistics to IPA	Han-Lin Chen
2021-11-15	ipa: ipu3: Extend ipu3 ipa interface for sensor controls	Han-Lin Chen
2021-11-11	libcamera: pipeline: ipu3: Fix minCrop indentation	Kieran Bingham
2021-11-08	android: Camera3RequestDescriptor: Provide a constructor for StreamBuffer	Umang Jain
2021-11-08	android: mm: Null check for CameraBufferManager	Umang Jain
2021-11-06	ipa: ipu3: List the events in their order of calling	Jean-Michel Hautbois
2021-11-04	libcamera: framebuffer: Fix isContiguous log message	Kieran Bingham
2021-11-02	pipeline: raspberrypi: Fix rounding issue in findBestFormat()	Naushir Patuck
2021-11-02	libcamera: ipa: raspberrypi: Add support for imx519 sensor	Arducam info
2021-11-02	pipeline: raspberrypi: Account for a missing Unicam embedded data node	Naushir Patuck
2021-11-02	pipeline: raspberrypi: Remove "unpacked" format penalty in mode selection	Naushir Patuck
2021-11-02	pipeline: raspberrypi: Convert the pipeline handler to use media controller	Naushir Patuck
2021-11-02	pipeline: raspberrypi: Apply sensor flips at the start of configure()	Naushir Patuck
2021-11-02	libcamera: bayer_format: Add PixelFormat conversion helpers to BayerFormat class	Naushir Patuck
2021-11-02	libcamera: bayer_format: Add unpacked mono 10-bit format to the conversion table	Naushir Patuck
2021-11-02	libcamera: bayer_format: Rework BayerFormat conversion table	Naushir Patuck
2021-11-02	libcamera: bayer_format: Turn BayerFormat::Packing into scoped enum	Laurent Pinchart
2021-11-02	libcamera: formats: Add R10_CSI2P format	Naushir Patuck
2021-11-02	libcamera: formats: Add R10 and R12 formats	Laurent Pinchart
2021-10-27	libcamera: pixel_format: remove duplicated return documentation	Kieran Bingham
2021-10-27	libcamera: v4l2_videodevice: provide hasMediaController()	Kieran Bingham
2021-10-26	ipa: ipu3: agc: Clamp shutter speed	Jean-Michel Hautbois
2021-10-26	ipa: ipu3: ipa_context: Fix doxygen warnings	Kieran Bingham
2021-10-26	ipa: ipu3: Implement an empty stop() function	Jean-Michel Hautbois
2021-10-26	ipa: ipu3: Fix badly documented context variables	Jean-Michel Hautbois
2021-10-26	ipa: ipu3: Isolate ipa_context documentation	Kieran Bingham
2021-10-26	ipa: ipu3: algorithms: awb: Privatize internal structures	Kieran Bingham
2021-10-26	ipa: ipu3: Fix the IPU3 AWB doxygen references	Kieran Bingham
2021-10-26	ipa: ipu3: ipa_context: Fix file reference	Kieran Bingham
2021-10-26	Documentation: IPU3 IPA Design guide	Kieran Bingham
2021-10-26	ipa: ipu3: tonemapping: Add the documentation for ToneMapping	Jean-Michel Hautbois
2021-10-26	ipa: ipu3: tonemapping: Implement configure call	Jean-Michel Hautbois
2021-10-26	ipa: ipu3: tonemapping: Generate the LUT only on gamma change	Jean-Michel Hautbois
2021-10-26	ipa: ipu3: agc: Document AGC mean-based algorithm	Jean-Michel Hautbois
2021-10-26	ipa: ipu3: awb: Reword accumulator documentation	Jean-Michel Hautbois
2021-10-26	ipa: ipu3: awb: Add AWB class documentation	Jean-Michel Hautbois
2021-10-26	ipa: ipu3: Explicitly use the statistics parameter	Jean-Michel Hautbois
2021-10-26	ipa: ipu3: Document the IPAIPU3 class	Jean-Michel Hautbois
2021-10-26	ipa: ipu3: Document IPAIPU3::configure	Jean-Michel Hautbois
2021-10-26	ipa: ipu3: Document IPAIPU3 class interface	Jean-Michel Hautbois
2021-10-26	android: post_processor: Make post processing async	Umang Jain
2021-10-26	android: post_processor: Drop return value for process()	Umang Jain
2021-10-26	android: Track and notify post processing of streams	Umang Jain
2021-10-26	android: post_processor: Consolidate contextual information	Umang Jain

# SPDX-License-Identifier: BSD-2-Clause # # Copyright (C) 2019, Raspberry Pi (Trading) Limited # # ctt_macbeth_locator.py - camera tuning tool Macbeth chart locator from ctt_ransac import * from ctt_tools import * import warnings """ NOTE: some custom functions have been used here to make the code more readable. These are defined in tools.py if they are needed for reference. """ """ Some inconsistencies between packages cause runtime warnings when running the clustering algorithm. This catches these warnings so they don't flood the output to the console """ def fxn(): warnings.warn("runtime", RuntimeWarning) """ Define the success message """ success_msg = 'Macbeth chart located successfully' def find_macbeth(Cam, img, mac_config=(0, 0)): small_chart, show = mac_config print('Locating macbeth chart') Cam.log += '\nLocating macbeth chart' """ catch the warnings """ warnings.simplefilter("ignore") fxn() """ Reference macbeth chart is created that will be correlated with the located macbeth chart guess to produce a confidence value for the match. """ ref = cv2.imread(Cam.path + 'ctt_ref.pgm', flags=cv2.IMREAD_GRAYSCALE) ref_w = 120 ref_h = 80 rc1 = (0, 0) rc2 = (0, ref_h) rc3 = (ref_w, ref_h) rc4 = (ref_w, 0) ref_corns = np.array((rc1, rc2, rc3, rc4), np.float32) ref_data = (ref, ref_w, ref_h, ref_corns) """ locate macbeth chart """ cor, mac, coords, msg = get_macbeth_chart(img, ref_data) """ following bits of code tries to fix common problems with simple techniques. If now or at any point the best correlation is of above 0.75, then nothing more is tried as this is a high enough confidence to ensure reliable macbeth square centre placement. """ """ brighten image 2x """ if cor < 0.75: a = 2 img_br = cv2.convertScaleAbs(img, alpha=a, beta=0) cor_b, mac_b, coords_b, msg_b = get_macbeth_chart(img_br, ref_data) if cor_b > cor: cor, mac, coords, msg = cor_b, mac_b, coords_b, msg_b """ brighten image 4x """ if cor < 0.75: a = 4 img_br = cv2.convertScaleAbs(img, alpha=a, beta=0) cor_b, mac_b, coords_b, msg_b = get_macbeth_chart(img_br, ref_data) if cor_b > cor: cor, mac, coords, msg = cor_b, mac_b, coords_b, msg_b """ In case macbeth chart is too small, take a selection of the image and attempt to locate macbeth chart within that. The scale increment is root 2 """ """ These variables will be used to transform the found coordinates at smaller scales back into the original. If ii is still -1 after this section that means it was not successful """ ii = -1 w_best = 0 h_best = 0 d_best = 100 """ d_best records the scale of the best match. Macbeth charts are only looked for at one scale increment smaller than the current best match in order to avoid unecessarily searching for macbeth charts at small scales. If a macbeth chart ha already been found then set d_best to 0 """ if cor != 0: d_best = 0 """ scale 3/2 (approx root2) """ if cor < 0.75: imgs = [] """ get size of image """ shape = list(img.shape[:2]) w, h = shape """ set dimensions of the subselection and the step along each axis between selections """ w_sel = int(2*w/3) h_sel = int(2*h/3) w_inc = int(w/6) h_inc = int(h/6) """ for each subselection, look for a macbeth chart """ for i in range(3): for j in range(3): w_s, h_s = i*w_inc, j*h_inc img_sel = img[w_s:w_s+w_sel, h_s:h_s+h_sel] cor_ij, mac_ij, coords_ij, msg_ij = get_macbeth_chart(img_sel, ref_data) """ if the correlation is better than the best then record the scale and current subselection at which macbeth chart was found. Also record the coordinates, macbeth chart and message. """ if cor_ij > cor: cor = cor_ij mac, coords, msg = mac_ij, coords_ij, msg_ij ii, jj = i, j w_best, h_best = w_inc, h_inc d_best = 1 """ scale 2 """ if cor < 0.75: imgs = [] shape = list(img.shape[:2]) w, h = shape w_sel = int(w/2) h_sel = int(h/2) w_inc = int(w/8) h_inc = int(h/8) for i in range(5): for j in range(5): w_s, h_s = i*w_inc, j*h_inc img_sel = img[w_s:w_s+w_sel, h_s:h_s+h_sel] cor_ij, mac_ij, coords_ij, msg_ij = get_macbeth_chart(img_sel, ref_data) if cor_ij > cor: cor = cor_ij mac, coords, msg = mac_ij, coords_ij, msg_ij ii, jj = i, j w_best, h_best = w_inc, h_inc d_best = 2 """ The following code checks for macbeth charts at even smaller scales. This slows the code down significantly and has therefore been omitted by default, however it is not unusably slow so might be useful if the macbeth chart is too small to be picked up to by the current subselections. Use this for macbeth charts with side lengths around 1/5 image dimensions (and smaller...?) it is, however, recommended that macbeth charts take up as large as possible a proportion of the image. """ if small_chart: if cor < 0.75 and d_best > 1: imgs = [] shape = list(img.shape[:2]) w, h = shape w_sel = int(w/3) h_sel = int(h/3) w_inc = int(w/12) h_inc = int(h/12) for i in range(9): for j in range(9): w_s, h_s = i*w_inc, j*h_inc img_sel = img[w_s:w_s+w_sel, h_s:h_s+h_sel] cor_ij, mac_ij, coords_ij, msg_ij = get_macbeth_chart(img_sel, ref_data) if cor_ij > cor: cor = cor_ij mac, coords, msg = mac_ij, coords_ij, msg_ij ii, jj = i, j w_best, h_best = w_inc, h_inc d_best = 3 if cor < 0.75 and d_best > 2: imgs = [] shape = list(img.shape[:2]) w, h = shape w_sel = int(w/4) h_sel = int(h/4) w_inc = int(w/16) h_inc = int(h/16) for i in range(13): for j in range(13): w_s, h_s = i*w_inc, j*h_inc img_sel = img[w_s:w_s+w_sel, h_s:h_s+h_sel] cor_ij, mac_ij, coords_ij, msg_ij = get_macbeth_chart(img_sel, ref_data) if cor_ij > cor: cor = cor_ij mac, coords, msg = mac_ij, coords_ij, msg_ij ii, jj = i, j w_best, h_best = w_inc, h_inc """ Transform coordinates from subselection to original image """ if ii != -1: for a in range(len(coords)): for b in range(len(coords[a][0])): coords[a][0][b][1] += ii*w_best coords[a][0][b][0] += jj*h_best """ initialise coords_fit variable """ coords_fit = None # print('correlation: {}'.format(cor)) """ print error or success message """ print(msg) Cam.log += '\n' + msg if msg == success_msg: coords_fit = coords Cam.log += '\nMacbeth chart vertices:\n' Cam.log += '{}'.format(2*np.round(coords_fit[0][0]), 0) """ if correlation is lower than 0.75 there may be a risk of macbeth chart corners not having been located properly. It might be worth running with show set to true to check where the macbeth chart centres have been located. """ print('Confidence: {:.3f}'.format(cor)) Cam.log += '\nConfidence: {:.3f}'.format(cor) if cor < 0.75: print('Caution: Low confidence guess!') Cam.log += 'WARNING: Low confidence guess!' # cv2.imshow('MacBeth', mac) # represent(mac, 'MacBeth chart') """ extract data from coords_fit and plot on original image """ if show and coords_fit is not None: copy = img.copy() verts = coords_fit[0][0] cents = coords_fit[1][0] """ draw circles at vertices of macbeth chart """ for vert in verts: p = tuple(np.round(vert).astype(np.int32)) cv2.circle(copy, p, 10, 1, -1) """ draw circles at centres of squares """ for i in range(len(cents)): cent = cents[i] p = tuple(np.round(cent).astype(np.int32)) """ draw black circle on white square, white circle on black square an grey circle everywhere else. """ if i == 3: cv2.circle(copy, p, 8, 0, -1) elif i == 23: cv2.circle(copy, p, 8, 1, -1) else: cv2.circle(copy, p, 8, 0.5, -1) copy, _ = reshape(copy, 400) represent(copy) return(coords_fit) def get_macbeth_chart(img, ref_data): """ function returns coordinates of macbeth chart vertices and square centres, along with an error/success message for debugging purposes. Additionally, it scores the match with a confidence value. Brief explanation of the macbeth chart locating algorithm: - Find rectangles within image - Take rectangles within percentage offset of median perimeter. The assumption is that these will be the macbeth squares - For each potential square, find the 24 possible macbeth centre locations that would produce a square in that location - Find clusters of potential macbeth chart centres to find the potential macbeth centres with the most votes, i.e. the most likely ones - For each potential macbeth centre, use the centres of the squares that voted for it to find macbeth chart corners - For each set of corners, transform the possible match into normalised space and correlate with a reference chart to evaluate the match - Select the highest correlation as the macbeth chart match, returning the correlation as the confidence score """ """ get reference macbeth chart data """ (ref, ref_w, ref_h, ref_corns) = ref_data """ the code will raise and catch a MacbethError in case of a problem, trying to give some likely reasons why the problem occred, hence the try/except """ try: """ obtain image, convert to grayscale and normalise """ src = img src, factor = reshape(src, 200) original = src.copy() a = 125/np.average(src) src_norm = cv2.convertScaleAbs(src, alpha=a, beta=0) """ This code checks if there are seperate colour channels. In the past the macbeth locator ran on jpgs and this makes it robust to different filetypes. Note that running it on a jpg has 4x the pixels of the average bayer channel so coordinates must be doubled. This is best done in img_load.py in the get_patches method. The coordinates and image width, height must be divided by two if the macbeth locator has been run on a demosaicked image. """ if len(src_norm.shape) == 3: src_bw = cv2.cvtColor(src_norm, cv2.COLOR_BGR2GRAY) else: src_bw = src_norm original_bw = src_bw.copy() """ obtain image edges """ sigma = 2 src_bw = cv2.GaussianBlur(src_bw, (0, 0), sigma) t1, t2 = 50, 100 edges = cv2.Canny(src_bw, t1, t2) """ dilate edges to prevent self-intersections in contours """ k_size = 2 kernel = np.ones((k_size, k_size)) its = 1 edges = cv2.dilate(edges, kernel, iterations=its) """ find Contours in image """ conts, _ = cv2.findContours(edges, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE) if len(conts) == 0: raise MacbethError( '\nWARNING: No macbeth chart found!' '\nNo contours found in image\n' 'Possible problems:\n' '- Macbeth chart is too dark or bright\n' '- Macbeth chart is occluded\n' ) """ find quadrilateral contours """ epsilon = 0.07 conts_per = [] for i in range(len(conts)): per = cv2.arcLength(conts[i], True) poly = cv2.approxPolyDP(conts[i], epsilon*per, True) if len(poly) == 4 and cv2.isContourConvex(poly): conts_per.append((poly, per)) if len(conts_per) == 0: raise MacbethError( '\nWARNING: No macbeth chart found!' '\nNo quadrilateral contours found' '\nPossible problems:\n' '- Macbeth chart is too dark or bright\n' '- Macbeth chart is occluded\n' '- Macbeth chart is out of camera plane\n' ) """ sort contours by perimeter and get perimeters within percent of median """ conts_per = sorted(conts_per, key=lambda x: x[1]) med_per = conts_per[int(len(conts_per)/2)][1] side = med_per/4 perc = 0.1 med_low, med_high = med_per*(1-perc), med_per*(1+perc) squares = [] for i in conts_per: if med_low <= i[1] and med_high >= i[1]: squares.append(i[0]) """ obtain coordinates of nomralised macbeth and squares """ square_verts, mac_norm = get_square_verts(0.06) """ for each square guess, find 24 possible macbeth chart centres """ mac_mids = [] squares_raw = [] for i in range(len(squares)): square = squares[i] squares_raw.append(square) """ convert quads to rotated rectangles. This is required as the 'squares' are usually quite irregular quadrilaterls, so performing a transform would result in exaggerated warping and inaccurate macbeth chart centre placement """ rect = cv2.minAreaRect(square) square = cv2.boxPoints(rect).astype(np.float32) """ reorder vertices to prevent 'hourglass shape' """ square = sorted(square, key=lambda x: x[0]) square_1 = sorted(square[:2], key=lambda x: x[1]) square_2 = sorted(square[2:], key=lambda x: -x[1]) square = np.array(np.concatenate((square_1, square_2)), np.float32) square = np.reshape(square, (4, 2)).astype(np.float32) squares[i] = square """ find 24 possible macbeth chart centres by trasnforming normalised macbeth square vertices onto candidate square vertices found in image """ for j in range(len(square_verts)): verts = square_verts[j] p_mat = cv2.getPerspectiveTransform(verts, square) mac_guess = cv2.perspectiveTransform(mac_norm, p_mat) mac_guess = np.round(mac_guess).astype(np.int32) """ keep only if candidate macbeth is within image border (deprecated) """ in_border = True # for p in mac_guess[0]: # pptest = cv2.pointPolygonTest( # img_con, # tuple(p), # False # ) # if pptest == -1: # in_border = False # break if in_border: mac_mid = np.mean(mac_guess, axis=1) mac_mids.append([mac_mid, (i, j)]) if len(mac_mids) == 0: raise MacbethError( '\nWARNING: No macbeth chart found!' '\nNo possible macbeth charts found within image' '\nPossible problems:\n' '- Part of the macbeth chart is outside the image\n' '- Quadrilaterals in image background\n' ) """ reshape data """ for i in range(len(mac_mids)): mac_mids[i][0] = mac_mids[i][0][0] """ find where midpoints cluster to identify most likely macbeth centres """ clustering = cluster.AgglomerativeClustering( n_clusters=None, compute_full_tree=True, distance_threshold=side*2 ) mac_mids_list = [x[0] for x in mac_mids] if len(mac_mids_list) == 1: """ special case of only one valid centre found (probably not needed) """ clus_list = [] clus_list.append([mac_mids, len(mac_mids)]) else: clustering.fit(mac_mids_list) # try: # clustering.fit(mac_mids_list) # except RuntimeWarning as error: # return(0, None, None, error) """ create list of all clusters """ clus_list = [] if clustering.n_clusters_ > 1: for i in range(clustering.labels_.max()+1): indices = [j for j, x in enumerate(clustering.labels_) if x == i] clus = [] for index in indices: clus.append(mac_mids[index]) clus_list.append([clus, len(clus)]) clus_list.sort(key=lambda x: -x[1]) elif clustering.n_clusters_ == 1: """ special case of only one cluster found """ # print('only 1 cluster') clus_list.append([mac_mids, len(mac_mids)]) else: raise MacbethError( '\nWARNING: No macebth chart found!' '\nNo clusters found' '\nPossible problems:\n' '- NA\n' ) """ keep only clusters with enough votes """ clus_len_max = clus_list[0][1] clus_tol = 0.7 for i in range(len(clus_list)): if clus_list[i][1] < clus_len_max * clus_tol: clus_list = clus_list[:i] break cent = np.mean(clus_list[i][0], axis=0)[0] clus_list[i].append(cent) """ represent most popular cluster centroids """ # copy = original_bw.copy() # copy = cv2.cvtColor(copy, cv2.COLOR_GRAY2RGB) # copy = cv2.resize(copy, None, fx=2, fy=2) # for clus in clus_list: # centroid = tuple(2*np.round(clus[2]).astype(np.int32)) # cv2.circle(copy, centroid, 7, (255, 0, 0), -1) # cv2.circle(copy, centroid, 2, (0, 0, 255), -1) # represent(copy) """ get centres of each normalised square """ reference = get_square_centres(0.06) """ for each possible macbeth chart, transform image into normalised space and find correlation with reference """ max_cor = 0 best_map = None best_fit = None best_cen_fit = None best_ref_mat = None for clus in clus_list: clus = clus[0] sq_cents = [] ref_cents = [] i_list = [p[1][0] for p in clus] for point in clus: i, j = point[1] """ remove any square that voted for two different points within the same cluster. This causes the same point in the image to be mapped to two different reference square centres, resulting in a very distorted perspective transform since cv2.findHomography simply minimises error. This phenomenon is not particularly likely to occur due to the enforced distance threshold in the clustering fit but it is best to keep this in just in case. """ if i_list.count(i) == 1: square = squares_raw[i] sq_cent = np.mean(square, axis=0) ref_cent = reference[j] sq_cents.append(sq_cent) ref_cents.append(ref_cent) """ At least three squares need to have voted for a centre in order for a transform to be found """ if len(sq_cents) < 3: raise MacbethError( '\nWARNING: No macbeth chart found!' '\nNot enough squares found' '\nPossible problems:\n' '- Macbeth chart is occluded\n' '- Macbeth chart is too dark of bright\n' ) ref_cents = np.array(ref_cents) sq_cents = np.array(sq_cents) """ find best fit transform from normalised centres to image """ h_mat, mask = cv2.findHomography(ref_cents, sq_cents) if 'None' in str(type(h_mat)): raise MacbethError( '\nERROR\n' ) """ transform normalised corners and centres into image space """ mac_fit = cv2.perspectiveTransform(mac_norm, h_mat) mac_cen_fit = cv2.perspectiveTransform(np.array([reference]), h_mat) """ transform located corners into reference space """ ref_mat = cv2.getPerspectiveTransform( mac_fit, np.array([ref_corns]) ) map_to_ref = cv2.warpPerspective( original_bw, ref_mat, (ref_w, ref_h) ) """ normalise brigthness """ a = 125/np.average(map_to_ref) map_to_ref = cv2.convertScaleAbs(map_to_ref, alpha=a, beta=0) """ find correlation with bw reference macbeth """ cor = correlate(map_to_ref, ref) """ keep only if best correlation """ if cor > max_cor: max_cor = cor best_map = map_to_ref best_fit = mac_fit best_cen_fit = mac_cen_fit best_ref_mat = ref_mat """ rotate macbeth by pi and recorrelate in case macbeth chart is upside-down """ mac_fit_inv = np.array( ([[mac_fit[0][2], mac_fit[0][3], mac_fit[0][0], mac_fit[0][1]]]) ) mac_cen_fit_inv = np.flip(mac_cen_fit, axis=1) ref_mat = cv2.getPerspectiveTransform( mac_fit_inv, np.array([ref_corns]) ) map_to_ref = cv2.warpPerspective( original_bw, ref_mat, (ref_w, ref_h) ) a = 125/np.average(map_to_ref) map_to_ref = cv2.convertScaleAbs(map_to_ref, alpha=a, beta=0) cor = correlate(map_to_ref, ref) if cor > max_cor: max_cor = cor best_map = map_to_ref best_fit = mac_fit_inv best_cen_fit = mac_cen_fit_inv best_ref_mat = ref_mat """ Check best match is above threshold """ cor_thresh = 0.6 if max_cor < cor_thresh: raise MacbethError( '\nWARNING: Correlation too low' '\nPossible problems:\n' '- Bad lighting conditions\n' '- Macbeth chart is occluded\n' '- Background is too noisy\n' '- Macbeth chart is out of camera plane\n' ) """ Following code is mostly representation for debugging purposes """ """ draw macbeth corners and centres on image """ copy = original.copy() copy = cv2.resize(original, None, fx=2, fy=2) # print('correlation = {}'.format(round(max_cor, 2))) for point in best_fit[0]: point = np.array(point, np.float32) point = tuple(2*np.round(point).astype(np.int32)) cv2.circle(copy, point, 4, (255, 0, 0), -1) for point in best_cen_fit[0]: point = np.array(point, np.float32) point = tuple(2*np.round(point).astype(np.int32)) cv2.circle(copy, point, 4, (0, 0, 255), -1) copy = copy.copy() cv2.circle(copy, point, 4, (0, 0, 255), -1) """ represent coloured macbeth in reference space """ best_map_col = cv2.warpPerspective( original, best_ref_mat, (ref_w, ref_h) ) best_map_col = cv2.resize( best_map_col, None, fx=4, fy=4 ) a = 125/np.average(best_map_col) best_map_col_norm = cv2.convertScaleAbs( best_map_col, alpha=a, beta=0 )