634 lines (633 with data), 259.5 kB
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Import necessary packages (run once upon startup)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
}
],
"source": [
"from __future__ import division \n",
"import os\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"plt.style.use(\"ggplot\")\n",
"%matplotlib inline\n",
"\n",
"\n",
"from skimage.transform import resize\n",
"from skimage.morphology import skeletonize\n",
"from scipy.signal import resample, savgol_filter, butter, filtfilt\n",
"from PIL import Image, ImageDraw\n",
"import cv2\n",
"\n",
"import tensorflow as tf\n",
"\n",
"from keras import backend as K\n",
"from keras.models import Model, load_model\n",
"from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Custom function definitions (run once upon startup)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Intersection over union (IoU), a measure of labelling accuracy (sometimes also called Jaccard score)\n",
"def IoU(y_true, y_pred, smooth=1):\n",
" intersection = K.sum(K.abs(y_true * y_pred), axis=-1)\n",
" union = K.sum(y_true,-1) + K.sum(y_pred,-1) - intersection\n",
" iou = (intersection + smooth) / ( union + smooth)\n",
" return iou\n",
"\n",
"# Function to sort contours from proximal to distal (the bounding boxes are not used)\n",
"def sort_contours(cnts):\n",
" # initialize the reverse flag and sort index\n",
" i = 1\n",
" # construct the list of bounding boxes and sort them from top to bottom\n",
" boundingBoxes = [cv2.boundingRect(c) for c in cnts]\n",
" (cnts, boundingBoxes) = zip(*sorted(zip(cnts, boundingBoxes), key=lambda b:b[1][i], reverse=False))\n",
" \n",
" return (cnts, boundingBoxes)\n",
"\n",
"# Find only the coordinates representing one edge of a contour. edge: T (top) or B (bottom)\n",
"def contour_edge(edge, contour):\n",
" pts = list(contour)\n",
" ptsT = sorted(pts, key=lambda k: [k[0][0], k[0][1]])\n",
" allx = []\n",
" ally = []\n",
" for a in range(0,len(ptsT)):\n",
" allx.append(ptsT[a][0,0])\n",
" ally.append(ptsT[a][0,1])\n",
" un = np.unique(allx)\n",
" #sumA = 0\n",
" leng = len(un)-1\n",
" x = []\n",
" y = []\n",
" for each in range(5,leng-5): # Ignore 1st and last 5 points to avoid any curves\n",
" indices = [i for i, x in enumerate(allx) if x == un[each]]\n",
" if edge == 'T':\n",
" loc = indices[0]\n",
" else:\n",
" loc = indices[-1]\n",
" x.append(ptsT[loc][0,0])\n",
" y.append(ptsT[loc][0,1])\n",
" return np.array(x),np.array(y)\n",
"\n",
"def intersection(L1, L2):\n",
" D = L1[0] * L2[1] - L1[1] * L2[0]\n",
" Dx = L1[2] * L2[1] - L1[1] * L2[2]\n",
" Dy = L1[0] * L2[2] - L1[2] * L2[0]\n",
" if D != 0:\n",
" x = Dx / D\n",
" y = Dy / D\n",
" return x,y\n",
" else:\n",
" return False\n",
"\n",
"# Function to detect mouse clicks for the purpose of image calibration\n",
"def mclick(event, x, y, flags, param):\n",
" # grab references to the global variables\n",
" global mlocs\n",
"\n",
" # if the left mouse button was clicked, record the (x, y) coordinates\n",
" if event == cv2.EVENT_LBUTTONDOWN:\n",
" mlocs.append(y)\n",
" \n",
"# Function to compute the distance between 2 x,y points\n",
"def distFunc(x1, y1, x2, y2):\n",
" xdist = (x2 - x1)**2\n",
" ydist = (y2 - y1)**2\n",
" return np.sqrt(xdist + ydist)\n",
"\n",
"###############################################################################\n",
"\n",
"# IMPORT THE TRAINED MODELS\n",
"\n",
"# load the aponeurosis model\n",
"model_apo = load_model('./models/model-apo2-nc.h5', custom_objects={'IoU': IoU})\n",
"\n",
"# load the fascicle model\n",
"modelF = load_model('./models/model-fascSnippets2-nc.h5', custom_objects={'IoU': IoU})"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# DEFINE SETTINGS\n",
"apo_threshold = 0.2 # Sensitivity threshold for detecting aponeuroses\n",
"fasc_threshold = 0.10 # Sensitivity threshold for detecting fascicles\n",
"fasc_cont_thresh = 40 # Minimum accepted contour length for fascicles (px) \n",
"flip = 1 # If fascicles are oriented bottom-left to top-right, leave as 0. Otherwise set to 1\n",
"min_width = 60 # Minimum acceptable distance between aponeuroses\n",
"curvature = 1 # Set to 3 for curved fascicles or 1 for a straight line\n",
"min_pennation = 14 # Minimum and maximum acceptable pennation angles\n",
"max_pennation = 40"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Import Image that you want to analyse (set location under 'image_add')"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\neilj\\AppData\\Local\\Continuum\\anaconda3\\envs\\tf_gpu\\lib\\site-packages\\skimage\\transform\\_warps.py:110: UserWarning: Anti-aliasing will be enabled by default in skimage 0.15 to avoid aliasing artifacts when down-sampling images.\n",
" warn(\"Anti-aliasing will be enabled by default in skimage 0.15 to \"\n"
]
}
],
"source": [
"# Define the image to analyse here and load it\n",
"image_add = ('D:/Unet annotations/Taija_MTJ_ToBeLabelled/10/solTest1.tif')\n",
"\n",
"filename = './analysedImages/' + os.path.splitext(os.path.basename(image_add))[0]\n",
"img = load_img(image_add, color_mode='grayscale')\n",
"if flip == 1:\n",
" img = np.fliplr(img)\n",
"img_copy = img\n",
"img = img_to_array(img)\n",
"h = img.shape[0]\n",
"w = img.shape[1]\n",
"img = np.reshape(img,[-1, h, w,1])\n",
"img = resize(img, (1, 512, 512, 1), mode = 'constant', preserve_range = True)\n",
"img = img/255.0\n",
"img2 = img\n",
"# calibDist = []"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10.0 mm corresponds to 77 pixels\n"
]
}
],
"source": [
"# OPTIONAL\n",
"# Calibrate the analysis by clicking on 2 points in the image, followed by the 'q' key. These two points should be 1cm apart\n",
"# Alternatively, change the spacing setting below\n",
"# NOTE: Here we assume that the points are spaced apart in the y/vertical direction of the image\n",
"img2 = np.uint8(img_copy)\n",
"spacing = 10.0 # Space between the two calibration markers (mm)\n",
"mlocs = []\n",
"\n",
"# display the image and wait for a keypress\n",
"cv2.imshow(\"image\", img2)\n",
"cv2.setMouseCallback(\"image\", mclick)\n",
"key = cv2.waitKey(0)\n",
" \n",
"# if the 'q' key is pressed, break from the loop\n",
"if key == ord(\"q\"):\n",
" cv2.destroyAllWindows()\n",
"\n",
"calibDist = np.abs(mlocs[0] - mlocs[1])\n",
"print(str(spacing) + ' mm corresponds to ' + str(calibDist) + ' pixels')"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\neilj\\AppData\\Local\\Continuum\\anaconda3\\envs\\tf_gpu\\lib\\site-packages\\skimage\\transform\\_warps.py:105: UserWarning: The default mode, 'constant', will be changed to 'reflect' in skimage 0.15.\n",
" warn(\"The default mode, 'constant', will be changed to 'reflect' in \"\n",
"C:\\Users\\neilj\\AppData\\Local\\Continuum\\anaconda3\\envs\\tf_gpu\\lib\\site-packages\\skimage\\transform\\_warps.py:110: UserWarning: Anti-aliasing will be enabled by default in skimage 0.15 to avoid aliasing artifacts when down-sampling images.\n",
" warn(\"Anti-aliasing will be enabled by default in skimage 0.15 to \"\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 1800x1800 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Get NN predictions for the image\n",
"pred_apo = model_apo.predict(img)\n",
"pred_apo_t = (pred_apo > apo_threshold).astype(np.uint8) # SET APO THRESHOLD\n",
"\n",
"pred_fasc = modelF.predict(img)\n",
"pred_fasc_t = (pred_fasc > fasc_threshold).astype(np.uint8) # SET FASC THRESHOLD\n",
"\n",
"img = resize(img, (1, h, w,1))\n",
"img = np.reshape(img, (h, w))\n",
"pred_apo = resize(pred_apo, (1, h, w,1))\n",
"pred_apo = np.reshape(pred_apo, (h, w))\n",
"pred_apo_t = resize(pred_apo_t, (1, h, w,1))\n",
"pred_apo_t = np.reshape(pred_apo_t, (h, w))\n",
"\n",
"pred_fasc = resize(pred_fasc, (1, h, w,1))\n",
"pred_fasc = np.reshape(pred_fasc, (h, w))\n",
"pred_fasc_t = resize(pred_fasc_t, (1, h, w,1))\n",
"pred_fasc_t = np.reshape(pred_fasc_t, (h, w))\n",
"\n",
"# # Uncomment these lines if you want to see the initial predictions\n",
"# fig = plt.figure(figsize=(17,17))\n",
"# ax1 = fig.add_subplot(131)\n",
"# ax1.imshow(img.squeeze(),cmap='gray')\n",
"# ax1.set_title('Original image')\n",
"# ax2 = fig.add_subplot(132)\n",
"# ax2.imshow(pred_apo_t.squeeze())\n",
"# ax2.set_title('Aponeuroses')\n",
"# ax3 = fig.add_subplot(133)\n",
"# ax3.imshow(pred_fasc_t.squeeze())\n",
"# ax3.set_title('Fascicles')\n",
"\n",
"#########################################################################\n",
"\n",
"xs = []\n",
"ys = []\n",
"fas_ext = []\n",
"fasc_l = []\n",
"pennation = []\n",
"x_low1 = []\n",
"x_high1 = []\n",
"\n",
"# Compute contours to identify the aponeuroses\n",
"_, thresh = cv2.threshold(pred_apo_t, 0, 255, cv2.THRESH_BINARY) \n",
"thresh = thresh.astype('uint8')\n",
"contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)\n",
"\n",
"contours_re = []\n",
"for contour in contours: # Remove any contours that are very small\n",
" if len(contour) > 600:\n",
" contours_re.append(contour)\n",
"contours = contours_re\n",
"contours,_ = sort_contours(contours) # Sort contours from top to bottom\n",
"\n",
"# mask_apo = np.zeros(thresh.shape,np.uint8)\n",
"contours_re2 = []\n",
"for contour in contours:\n",
"# cv2.drawContours(mask_apo,[contour],0,255,-1)\n",
" pts = list(contour)\n",
" ptsT = sorted(pts, key=lambda k: [k[0][0], k[0][1]]) # Sort each contour based on x values\n",
" allx = []\n",
" ally = []\n",
" for a in range(0,len(ptsT)):\n",
" allx.append(ptsT[a][0,0])\n",
" ally.append(ptsT[a][0,1])\n",
" app = np.array(list(zip(allx,ally)))\n",
" contours_re2.append(app)\n",
" \n",
"# Merge nearby contours\n",
"# countU = 0\n",
"xs1 = []\n",
"xs2 = []\n",
"ys1 = []\n",
"ys2 = []\n",
"maskT = np.zeros(thresh.shape,np.uint8)\n",
"for cnt in contours_re2:\n",
" ys1.append(cnt[0][1])\n",
" ys2.append(cnt[-1][1])\n",
" xs1.append(cnt[0][0])\n",
" xs2.append(cnt[-1][0])\n",
" cv2.drawContours(maskT,[cnt],0,255,-1)\n",
" \n",
"for countU in range(0,len(contours_re2)-1):\n",
" if xs1[countU+1] > xs2[countU]: # Check if x of contour2 is higher than x of contour 1\n",
" y1 = ys2[countU]\n",
" y2 = ys1[countU+1]\n",
" if y1-10 <= y2 <= y1+10:\n",
" m = np.vstack((contours_re2[countU], contours_re2[countU+1]))\n",
" cv2.drawContours(maskT,[m],0,255,-1)\n",
" countU += 1\n",
" \n",
"maskT[maskT > 0] = 1\n",
"skeleton = skeletonize(maskT).astype(np.uint8)\n",
"kernel = np.ones((3,7), np.uint8) \n",
"dilate = cv2.dilate(skeleton, kernel, iterations=15)\n",
"erode = cv2.erode(dilate, kernel, iterations=10)\n",
"\n",
"contoursE, hierarchy = cv2.findContours(erode, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)\n",
"mask_apoE = np.zeros(thresh.shape,np.uint8)\n",
"\n",
"contoursE = [i for i in contoursE if len(i) > 600] # Remove any contours that are very small\n",
"\n",
"for contour in contoursE:\n",
" cv2.drawContours(mask_apoE,[contour],0,255,-1)\n",
"contoursE,_ = sort_contours(contoursE)\n",
"\n",
"# Only continues beyond this point if 2 aponeuroses can be detected\n",
"if len(contoursE) >= 2:\n",
" # Get the x,y coordinates of the upper/lower edge of the 2 aponeuroses\n",
" upp_x,upp_y = contour_edge('B', contoursE[0])\n",
" if contoursE[1][0,0,1] > contoursE[0][0,0,1] + min_width:\n",
" low_x,low_y = contour_edge('T', contoursE[1])\n",
" else:\n",
" low_x,low_y = contour_edge('T', contoursE[2])\n",
"\n",
" upp_y_new = savgol_filter(upp_y, 81, 2) # window size 51, polynomial order 3\n",
" low_y_new = savgol_filter(low_y, 81, 2)\n",
"\n",
" # Make a binary mask to only include fascicles within the region between the 2 aponeuroses\n",
" ex_mask = np.zeros(thresh.shape,np.uint8)\n",
" ex_1 = 0\n",
" ex_2 = np.minimum(len(low_x), len(upp_x))\n",
" for ii in range(ex_1, ex_2):\n",
" ymin = int(np.floor(upp_y_new[ii]))\n",
" ymax = int(np.ceil(low_y_new[ii]))\n",
"\n",
" ex_mask[:ymin, ii] = 0\n",
" ex_mask[ymax:, ii] = 0\n",
" ex_mask[ymin:ymax, ii] = 255\n",
"\n",
" # Calculate slope of central portion of each aponeurosis & use this to compute muscle thickness\n",
" Alist = list(set(upp_x).intersection(low_x))\n",
" Alist = sorted(Alist)\n",
" Alen = len(list(set(upp_x).intersection(low_x))) # How many values overlap between x-axes\n",
" A1 = int(Alist[0] + (.33 * Alen))\n",
" A2 = int(Alist[0] + (.66 * Alen)) \n",
" mid = int((A2-A1) / 2 + A1)\n",
" mindist = 10000\n",
" upp_ind = np.where(upp_x==mid)\n",
"\n",
" if upp_ind == len(upp_x):\n",
" upp_ind -= 1\n",
"\n",
" for val in range(A1, A2):\n",
" if val >= len(low_x):\n",
" continue\n",
" else:\n",
" dist = distFunc(upp_x[upp_ind], upp_y_new[upp_ind], low_x[val], low_y_new[val])\n",
" if dist < mindist:\n",
" mindist = dist\n",
"\n",
" # Add aponeuroses to a mask for display\n",
" # imgT = np.zeros((h,w,1), np.uint8)\n",
"\n",
" # Compute functions to approximate the shape of the aponeuroses\n",
" zUA = np.polyfit(upp_x, upp_y_new, 2)\n",
" g = np.poly1d(zUA)\n",
" zLA = np.polyfit(low_x, low_y_new, 2)\n",
" h = np.poly1d(zLA)\n",
"\n",
" mid = (low_x[-1]-low_x[0])/2 + low_x[0] # Find middle of the aponeurosis\n",
" x1 = np.linspace(low_x[0]-700, low_x[-1]+700, 10000) # Extrapolate polynomial fits to either side of the mid-point\n",
" y_UA = g(x1)\n",
" y_LA = h(x1)\n",
"\n",
" new_X_UA = np.linspace(mid-700, mid+700, 5000) # Extrapolate x,y data using f function\n",
" new_Y_UA = g(new_X_UA)\n",
" new_X_LA = np.linspace(mid-700, mid+700, 5000) # Extrapolate x,y data using f function\n",
" new_Y_LA = h(new_X_LA)\n",
"\n",
" #########################################################################\n",
"\n",
" # Compute contours to identify fascicles/fascicle orientation\n",
" _, threshF = cv2.threshold(pred_fasc_t, 0, 255, cv2.THRESH_BINARY) \n",
" threshF = threshF.astype('uint8')\n",
" contoursF, hierarchy = cv2.findContours(threshF, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
"\n",
" # Remove any contours that are very small\n",
"# contours_re = []\n",
" maskF = np.zeros(threshF.shape,np.uint8)\n",
" for contour in contoursF: # Remove any contours that are very small\n",
" if len(contour) > fasc_cont_thresh:\n",
"# contours_re.append(contour)\n",
" cv2.drawContours(maskF,[contour],0,255,-1) \n",
"\n",
" # Only include fascicles within the region of the 2 aponeuroses \n",
" mask_Fi = maskF & ex_mask \n",
" contoursF2, hierarchy = cv2.findContours(mask_Fi, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)\n",
"\n",
" # maskF = np.zeros(threshF.shape,np.uint8)\n",
"# contoursF3 = []\n",
"# for contour in contoursF2:\n",
"# if len(contour) > fasc_cont_thresh:\n",
"# # cv2.drawContours(maskF,[contour],0,255,-1)\n",
"# contoursF3.append(contour)\n",
" contoursF3 = [i for i in contoursF2 if len(i) > fasc_cont_thresh]\n",
"\n",
" fig = plt.figure(figsize=(25,25))\n",
"\n",
" xs = []\n",
" ys = []\n",
" fas_ext = []\n",
" fasc_l = []\n",
" pennation = []\n",
" x_low1 = []\n",
" x_high1 = []\n",
"\n",
" for contour in contoursF2:\n",
" x,y = contour_edge('B', contour)\n",
" if len(x) == 0:\n",
" continue\n",
" z = np.polyfit(np.array(x), np.array(y), 1)\n",
" f = np.poly1d(z)\n",
" newX = np.linspace(-400, w+400, 5000) # Extrapolate x,y data using f function\n",
" newY = f(newX)\n",
"\n",
" # Find intersection between each fascicle and the aponeuroses.\n",
" diffU = newY-new_Y_UA # Find intersections\n",
" locU = np.where(diffU == min(diffU, key=abs))[0]\n",
" diffL = newY-new_Y_LA\n",
" locL = np.where(diffL == min(diffL, key=abs))[0]\n",
"\n",
" coordsX = newX[int(locL):int(locU)] # Get coordinates of fascicle between the two aponeuroses\n",
" coordsY = newY[int(locL):int(locU)]\n",
"\n",
" # Get angle of aponeurosis in region close to fascicle intersection\n",
" if locL >= 4950:\n",
" Apoangle = int(np.arctan((new_Y_LA[locL-50]-new_Y_LA[locL-50])/(new_X_LA[locL]-new_X_LA[locL-50]))*180/np.pi)\n",
" else:\n",
" Apoangle = int(np.arctan((new_Y_LA[locL]-new_Y_LA[locL+50])/(new_X_LA[locL+50]-new_X_LA[locL]))*180/np.pi) # Angle relative to horizontal\n",
" Apoangle = 90.0 + abs(Apoangle)\n",
"\n",
" # Don't include fascicles that are completely outside of the field of view or\n",
" # those that don't pass through central 1/3 of the image\n",
" # if np.sum(coordsX) > 0 and coordsX[-1] > 0 and coordsX[0] < np.maximum(upp_x[-1],low_x[-1]) and coordsX[-1] - coordsX[0] < w and Apoangle != float('nan'):\n",
" if np.sum(coordsX) > 0 and coordsX[-1] > 0 and coordsX[0] < np.maximum(upp_x[-1],low_x[-1]) and Apoangle != float('nan'):\n",
" FascAng = float(np.arctan((coordsX[0]-coordsX[-1])/(new_Y_LA[locL]-new_Y_UA[locU]))*180/np.pi)*-1\n",
" ActualAng = Apoangle-FascAng\n",
"\n",
" if ActualAng <= max_pennation and ActualAng >= min_pennation: # Don't include 'fascicles' beyond a range of pennation angles\n",
" length1 = np.sqrt((newX[locU] - newX[locL])**2 + (y_UA[locU] - y_LA[locL])**2)\n",
" fasc_l.append(length1[0]) # Calculate fascicle length\n",
" pennation.append(Apoangle-FascAng)\n",
" x_low1.append(coordsX[0].astype('int32'))\n",
" x_high1.append(coordsX[-1].astype('int32'))\n",
" coords = np.array(list(zip(coordsX.astype('int32'), coordsY.astype('int32'))))\n",
" plt.plot(coordsX,coordsY,':w', linewidth = 6)\n",
" # cv2.polylines(imgT, [coords], False, (20, 15, 200), 3)\n",
"\n",
" #########################################################################\n",
" # DISPLAY THE RESULTS\n",
"\n",
" plt.imshow(img_copy, cmap='gray')\n",
" plt.plot(low_x,low_y_new, marker='p', color='w', linewidth = 15) # Plot the aponeuroses\n",
" plt.plot(upp_x,upp_y_new, marker='p', color='w', linewidth = 15)\n",
" \n",
" xplot = 125\n",
" yplot = 250\n",
"\n",
" # Store the results for each frame and normalise using scale factor (if calibration was done above)\n",
" try:\n",
" midthick = mindist[0] # Muscle thickness\n",
" except:\n",
" midthick = mindist\n",
"\n",
" if 'calibDist' in locals():\n",
" fasc_l = fasc_l / (calibDist/10)\n",
" midthick = midthick / (calibDist/10)\n",
"\n",
" plt.text(xplot, yplot, ('Fascicle length: ' + str('%.2f' % np.median(fasc_l)) + ' mm'), fontsize=26, color='white')\n",
" plt.text(xplot, yplot+50, ('Pennation angle: ' + str('%.1f' % np.median(pennation)) + ' deg'), fontsize=26, color='white')\n",
" plt.text(xplot, yplot+100, ('Thickness at centre: ' + str('%.1f' % midthick) + ' mm'), fontsize=26, color='white')\n",
" plt.grid(False)\n",
"\n",
" else:\n",
" plt.text(xplot, yplot, ('Fascicle length: ' + str('%.1f' % np.median(fasc_l)) + ' px'), fontsize=26, color='white')\n",
" plt.text(xplot, yplot+50, ('Pennation angle: ' + str('%.1f' % np.median(pennation)) + ' deg'), fontsize=26, color='white')\n",
" plt.text(xplot, yplot+100, ('Thickness at centre: ' + str('%.1f' % midthick) + ' px'), fontsize=26, color='white')\n",
" plt.grid(False)\n",
" \n",
"else:\n",
" print('***************************************************')\n",
" print(\"Couldn't detect two aponeuroses!\")\n",
" print(\"Try reducing 'apo_threshold' in the settings above\")\n",
" print('***************************************************')\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {},
"outputs": [],
"source": [
"# COMPILE RESULTS AND SAVE (SAVES TO CURRENT DIRECTORY)\n",
"data = pd.DataFrame({'Fasc_l': fasc_l, 'Pennation': pennation, 'xLow': x_low1, 'xHigh': x_high1, 'Thickness': midthick})\n",
"data.to_excel(filename + '.xlsx')"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 864x864 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# fig = plt.figure(figsize=(12,12)) # ORIGINAL IMAGE\n",
"# plt.imshow(img.squeeze(), cmap='gray')\n",
"# plt.title('Original image')\n",
"# plt.grid(False)\n",
"\n",
"# fig = plt.figure(figsize=(12,12)) # APO PREDICTIONS\n",
"# plt.imshow(pred_apo_t.squeeze(), cmap='gray')\n",
"# # plt.title('Aponeurosis predictions')\n",
"# plt.grid(False)\n",
"# frame1 = plt.gca()\n",
"# frame1.axes.get_xaxis().set_visible(False)\n",
"# frame1.axes.get_yaxis().set_visible(False)\n",
"\n",
"fig = plt.figure(figsize=(12,12)) # FASC PREDICTIONS\n",
"plt.imshow(pred_fasc_t.squeeze(), cmap='gray')\n",
"# plt.title('Fascicle predictions')\n",
"plt.grid(False)\n",
"frame1 = plt.gca()\n",
"frame1.axes.get_xaxis().set_visible(False)\n",
"frame1.axes.get_yaxis().set_visible(False)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}