diff options
Diffstat (limited to 'utils/raspberrypi/ctt/ctt_macbeth_locator.py')
| -rw-r--r-- | utils/raspberrypi/ctt/ctt_macbeth_locator.py | 240 |
1 files changed, 120 insertions, 120 deletions
diff --git a/utils/raspberrypi/ctt/ctt_macbeth_locator.py b/utils/raspberrypi/ctt/ctt_macbeth_locator.py index 583d5a69..591459aa 100644 --- a/utils/raspberrypi/ctt/ctt_macbeth_locator.py +++ b/utils/raspberrypi/ctt/ctt_macbeth_locator.py @@ -19,15 +19,15 @@ the clustering algorithm. This catches these warnings so they don't flood the output to the console """ def fxn(): - warnings.warn("runtime",RuntimeWarning) + 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 +def find_macbeth(Cam, img, mac_config=(0, 0)): + small_chart, show = mac_config print('Locating macbeth chart') Cam.log += '\nLocating macbeth chart' """ @@ -40,20 +40,20 @@ def find_macbeth(Cam,img,mac_config=(0,0)): 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 = 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) + 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) + cor, mac, coords, msg = get_macbeth_chart(img, ref_data) """ following bits of code tries to fix common problems with simple @@ -68,20 +68,20 @@ def find_macbeth(Cam,img,mac_config=(0,0)): """ 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) + 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 + 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) + 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 + 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 @@ -115,7 +115,7 @@ def find_macbeth(Cam,img,mac_config=(0,0)): get size of image """ shape = list(img.shape[:2]) - w,h = shape + w, h = shape """ set dimensions of the subselection and the step along each axis between selections @@ -129,9 +129,9 @@ def find_macbeth(Cam,img,mac_config=(0,0)): """ 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) + 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 @@ -139,9 +139,9 @@ def find_macbeth(Cam,img,mac_config=(0,0)): """ 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 + mac, coords, msg = mac_ij, coords_ij, msg_ij + ii, jj = i, j + w_best, h_best = w_inc, h_inc d_best = 1 @@ -151,21 +151,21 @@ def find_macbeth(Cam,img,mac_config=(0,0)): if cor < 0.75: imgs = [] shape = list(img.shape[:2]) - w,h = shape + 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) + 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 + mac, coords, msg = mac_ij, coords_ij, msg_ij + ii, jj = i, j + w_best, h_best = w_inc, h_inc d_best = 2 """ @@ -183,41 +183,41 @@ def find_macbeth(Cam,img,mac_config=(0,0)): if cor < 0.75 and d_best > 1 : imgs = [] shape = list(img.shape[:2]) - w,h = shape + 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) + 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 + 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, 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) + 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 + 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 @@ -241,7 +241,7 @@ def find_macbeth(Cam,img,mac_config=(0,0)): if msg == success_msg: coords_fit = coords Cam.log += '\nMacbeth chart vertices:\n' - Cam.log += '{}'.format(2*np.round(coords_fit[0][0]),0) + 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 @@ -253,8 +253,8 @@ def find_macbeth(Cam,img,mac_config=(0,0)): if cor < 0.75: print('Caution: Low confidence guess!') Cam.log += 'WARNING: Low confidence guess!' - # cv2.imshow('MacBeth',mac) - # represent(mac,'MacBeth chart') + # cv2.imshow('MacBeth', mac) + # represent(mac, 'MacBeth chart') """ extract data from coords_fit and plot on original image @@ -269,7 +269,7 @@ def find_macbeth(Cam,img,mac_config=(0,0)): """ for vert in verts: p = tuple(np.round(vert).astype(np.int32)) - cv2.circle(copy,p,10,1,-1) + cv2.circle(copy, p, 10, 1, -1) """ draw circles at centres of squares """ @@ -281,17 +281,17 @@ def find_macbeth(Cam,img,mac_config=(0,0)): grey circle everywhere else. """ if i == 3: - cv2.circle(copy,p,8,0,-1) + cv2.circle(copy, p, 8, 0, -1) elif i == 23: - cv2.circle(copy,p,8,1,-1) + cv2.circle(copy, p, 8, 1, -1) else: - cv2.circle(copy,p,8,0.5,-1) - copy,_ = reshape(copy,400) + cv2.circle(copy, p, 8, 0.5, -1) + copy, _ = reshape(copy, 400) represent(copy) return(coords_fit) -def get_macbeth_chart(img,ref_data): +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, @@ -316,7 +316,7 @@ def get_macbeth_chart(img,ref_data): """ get reference macbeth chart data """ - (ref,ref_w,ref_h,ref_corns) = ref_data + (ref, ref_w, ref_h, ref_corns) = ref_data """ the code will raise and catch a MacbethError in case of a problem, trying @@ -327,10 +327,10 @@ def get_macbeth_chart(img,ref_data): obtain image, convert to grayscale and normalise """ src = img - src,factor = reshape(src,200) + src, factor = reshape(src, 200) original=src.copy() a = 125/np.average(src) - src_norm = cv2.convertScaleAbs(src,alpha=a,beta=0) + 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 @@ -338,11 +338,11 @@ def get_macbeth_chart(img,ref_data): 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 + 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) + src_bw = cv2.cvtColor(src_norm, cv2.COLOR_BGR2GRAY) else: src_bw = src_norm original_bw = src_bw.copy() @@ -350,20 +350,20 @@ def get_macbeth_chart(img,ref_data): 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) + 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)) + kernel = np.ones((k_size, k_size)) its = 1 - edges = cv2.dilate(edges,kernel,iterations=its) + edges = cv2.dilate(edges, kernel, iterations=its) """ find Contours in image """ - conts,_ = cv2.findContours(edges, + conts, _ = cv2.findContours(edges, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE) if len(conts) == 0: @@ -380,11 +380,11 @@ def get_macbeth_chart(img,ref_data): epsilon = 0.07 conts_per = [] for i in range(len(conts)): - per = cv2.arcLength(conts[i],True) + per = cv2.arcLength(conts[i], True) poly = cv2.approxPolyDP(conts[i], - epsilon*per,True) + epsilon*per, True) if len(poly) == 4 and cv2.isContourConvex(poly): - conts_per.append((poly,per)) + conts_per.append((poly, per)) if len(conts_per) == 0: raise MacbethError( @@ -399,11 +399,11 @@ def get_macbeth_chart(img,ref_data): """ sort contours by perimeter and get perimeters within percent of median """ - conts_per = sorted(conts_per,key=lambda x:x[1]) + 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) + 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]: @@ -412,7 +412,7 @@ def get_macbeth_chart(img,ref_data): """ obtain coordinates of nomralised macbeth and squares """ - square_verts,mac_norm = get_square_verts(0.06) + square_verts, mac_norm = get_square_verts(0.06) """ for each square guess, find 24 possible macbeth chart centres """ @@ -432,11 +432,11 @@ def get_macbeth_chart(img,ref_data): """ 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) + 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 @@ -444,8 +444,8 @@ def get_macbeth_chart(img,ref_data): """ 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) + 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 @@ -465,7 +465,7 @@ def get_macbeth_chart(img,ref_data): if in_border: mac_mid = np.mean(mac_guess, axis=1) - mac_mids.append([mac_mid,(i,j)]) + mac_mids.append([mac_mid, (i, j)]) if len(mac_mids) == 0: raise MacbethError( @@ -497,14 +497,14 @@ def get_macbeth_chart(img,ref_data): special case of only one valid centre found (probably not needed) """ clus_list = [] - clus_list.append([mac_mids,len(mac_mids)]) + 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) + # return(0, None, None, error) """ create list of all clusters @@ -512,19 +512,19 @@ def get_macbeth_chart(img,ref_data): 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] + 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]) + 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)]) + clus_list.append([mac_mids, len(mac_mids)]) else: raise MacbethError( '\nWARNING: No macebth chart found!' @@ -542,19 +542,19 @@ def get_macbeth_chart(img,ref_data): 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] + 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) + # 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) + # cv2.circle(copy, centroid, 7, (255, 0, 0), -1) + # cv2.circle(copy, centroid, 2, (0, 0, 255), -1) # represent(copy) """ @@ -578,7 +578,7 @@ def get_macbeth_chart(img,ref_data): ref_cents = [] i_list = [p[1][0] for p in clus] for point in clus: - i,j = point[1] + 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 @@ -591,7 +591,7 @@ def get_macbeth_chart(img,ref_data): """ if i_list.count(i) == 1: square = squares_raw[i] - sq_cent = np.mean(square,axis=0) + sq_cent = np.mean(square, axis=0) ref_cent = reference[j] sq_cents.append(sq_cent) ref_cents.append(ref_cent) @@ -614,7 +614,7 @@ def get_macbeth_chart(img,ref_data): """ find best fit transform from normalised centres to image """ - h_mat,mask = cv2.findHomography(ref_cents,sq_cents) + h_mat, mask = cv2.findHomography(ref_cents, sq_cents) if 'None' in str(type(h_mat)): raise MacbethError( '\nERROR\n' @@ -623,8 +623,8 @@ def get_macbeth_chart(img,ref_data): """ 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) + mac_fit = cv2.perspectiveTransform(mac_norm, h_mat) + mac_cen_fit = cv2.perspectiveTransform(np.array([reference]), h_mat) """ transform located corners into reference space """ @@ -633,18 +633,18 @@ def get_macbeth_chart(img,ref_data): np.array([ref_corns]) ) map_to_ref = cv2.warpPerspective( - original_bw,ref_mat, - (ref_w,ref_h) + 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) + map_to_ref = cv2.convertScaleAbs(map_to_ref, alpha=a, beta=0) """ find correlation with bw reference macbeth """ - cor = correlate(map_to_ref,ref) + cor = correlate(map_to_ref, ref) """ keep only if best correlation """ @@ -660,21 +660,21 @@ def get_macbeth_chart(img,ref_data): upside-down """ mac_fit_inv = np.array( - ([[mac_fit[0][2],mac_fit[0][3], - mac_fit[0][0],mac_fit[0][1]]]) + ([[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) + 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) + 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) + 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 @@ -704,45 +704,45 @@ def get_macbeth_chart(img,ref_data): 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))) + 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 = np.array(point, np.float32) point = tuple(2*np.round(point).astype(np.int32)) - cv2.circle(copy,point,4,(255,0,0),-1) + cv2.circle(copy, point, 4, (255, 0, 0), -1) for point in best_cen_fit[0]: - point = np.array(point,np.float32) + point = np.array(point, np.float32) point = tuple(2*np.round(point).astype(np.int32)) - cv2.circle(copy,point,4,(0,0,255),-1) + cv2.circle(copy, point, 4, (0, 0, 255), -1) copy = copy.copy() - cv2.circle(copy,point,4,(0,0,255),-1) + 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) + original, best_ref_mat, (ref_w, ref_h) ) best_map_col = cv2.resize( - best_map_col,None,fx=4,fy=4 + 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 + best_map_col, alpha=a, beta=0 ) - # cv2.imshow('Macbeth',best_map_col) + # cv2.imshow('Macbeth', best_map_col) # represent(copy) """ rescale coordinates to original image size """ - fit_coords = (best_fit/factor,best_cen_fit/factor) + fit_coords = (best_fit/factor, best_cen_fit/factor) - return(max_cor,best_map_col_norm,fit_coords,success_msg) + return(max_cor, best_map_col_norm, fit_coords, success_msg) """ catch macbeth errors and continue with code """ except MacbethError as error: - return(0,None,None,error) + return(0, None, None, error) |