def blob_detector2(image, stepSize, windowSize):
blobs = []
print("loading model...")
model = load_model(
'/Users/2020shatgiskessell/Desktop/New_Mole_Detector/Mole_Detector_1_3/my_mole_model_2.h5'
)
for y in range(0, image.shape[0], stepSize):
for x in range(0, image.shape[1], stepSize):
# identify moles on current window
#blobs.append(identify_moles(image[y:y + windowSize[1], x:x + windowSize[0]]))
roi = image[y:y + windowSize[1], x:x + windowSize[0]]
try:
roi = cv2.resize(roi, (8, 8))
except Exception:
continue
roi = np.expand_dims(roi, axis=2)
roi = np.expand_dims(roi, axis=0)
pred = model.predict(roi)
pred = pred.round()
if int(pred[0][0]) == 1:
blob = Blob()
blob.x = x
blob.y = y
#blob.matrix = component_matrix
blob.roi = roi
blobs.append(blob)
return blobs
def compute_stats(num_labels, labels, stats, centroids, img, x_i, y_i, i, og):
blobs = []
#start = timeit.default_timer()
#get component centroid coordinates
x, y = centroids[i]
#compute statistics
# cv2.CC_STAT_LEFT The leftmost (x) coordinate which is the inclusive start of the bounding box in the horizontal direction.
# cv2.CC_STAT_TOP The topmost (y) coordinate which is the inclusive start of the bounding box in the vertical direction.
# cv2.CC_STAT_WIDTH The horizontal size of the bounding box
# cv2.CC_STAT_HEIGHT The vertical size of the bounding box
# cv2.CC_STAT_AREA The total area (in pixels) of the connected component
width = stats[i, cv2.CC_STAT_WIDTH]
height = stats[i, cv2.CC_STAT_HEIGHT]
if height > 20:
return None, None, None, None, None, None
radius = (height + width) / 2
area = stats[i, cv2.CC_STAT_AREA]
#these are the top left x, y coordinates = ONLY TO BE USED FOR GETTING ROI
x_l = stats[i, cv2.CC_STAT_LEFT]
y_l = stats[i, cv2.CC_STAT_TOP]
#compute line
#slope, y_int= compute_lobf(x,y,x_l,y_l)
#stop = timeit.default_timer()
#print('Time to calculate properties: ', stop - start)
#remove everything except for component i to create isolated component matrix
#get connected component roi
roi = og[y_l - 1:y_l + height + 1, x_l - 1:x_l + width + 1]
#stop = timeit.default_timer()
#print('Time to create cm and roi: ', stop - start)
#----------------MEASURES-------------------------------------------------------------------
#radius = (height + width)/2
#compute more statistics related to roundness
radius = np.sqrt((area / np.pi))
formfactor = compute_formfactor(radius, area)
bounding_box_area_ratio = area / (height * width)
if height > width:
roundness = compute_roundness(height, radius, area)
aspect_ratio = height / width
else:
roundness = compute_roundness(width, radius, area)
aspect_ratio = width / height
#print('Time to calculate heuristic properties: ', stop - start)
# if x >300 and y < 150:
# print ("(" + str(x) + ","+str(y)+") -> area ratio: " + str(area/(height*width)))
#print ("(" + str(x) + ","+str(y)+") -> " + "radius: " + str(radius) + ", formfactor: " + str(formfactor) + ", roundness: " + str(roundness) + ", aspect ratio: " + str(aspect_ratio))
#print ("(" + str(x) + ","+str(y)+")")
#print ("\n")
#calculates line of best fit and error
try:
cord1, cord2, error = compute_lobf(roi, x_l * y_l)
x1, y1 = cord1
x2, y2 = cord2
except TypeError:
print("cant calculate line of best fit")
error = 0
cv2.imwrite(
os.path.join(
"/Users/2020shatgiskessell/Desktop/New_Mole_Detector/ANN_Images_Fiverr",
"roi19" + str(i) + ".png"), roi)
#COMMENT OUT WHEN COLLECTING ANN DATA!!!!!
#if the error is below 16, (the line of best fit closely matches connect component) return none
if error < 16:
return None, None, None, None, None, None
# print ("(" + str(x1) + ","+str(y1)+")")
# print ("(" + str(x2) + ","+str(y2)+")")
# print ("\n")
#next step: calculate residuals and do some sort of analysis
#try and bounding_box_area_ratio >= 0.5
if roundness > 0.2 and 0.9 < aspect_ratio < 3 and 1.1 >= formfactor > 0.9:
blob = Blob()
blob.radius = radius
blob.x = x + x_i
blob.y = y + y_i
#blob.matrix = component_matrix
blob.roi = roi
blobs.append(blob)
return blobs
def blob_detector(img, x_i, y_i):
og = img.copy()
img = cv2.Canny(img, 100, 200)
blobs = []
#get connected components of fg
#print ("calculating connected components...")
output = cv2.connectedComponentsWithStats(img, 4, cv2.CV_32S)
# Get the results
# The first cell is the number of labels
#print ("calculating statistics...")
num_labels = output[0]
# The second cell is the label matrix
labels = output[1]
# The third cell is the stat matrix
stats = output[2]
# The fourth cell is the centroid matrix
centroids = output[3]
#print (centroids)
#print(np.where(labels == 2))
roundnesses = []
aspect_ratios = []
formfactors = []
errors = []
rois = []
#imshow_components(labels, og)
#print ("calculating connected components properties...")
# with concurrent.futures.ThreadPoolExecutor(5) as executor:
# future_moles = {executor.submit(compute_stats, num_labels,labels, stats, centroids, img, x_i, y_i, i, og):i for i in range(num_labels)}
# for future in concurrent.futures.as_completed(future_moles):
# found_blobs, roundnesses1, aspect_ratios1, formfactors1, errors1, roi1 = future.result()
# if found_blobs != None:
# blobs.extend(found_blobs)
# roundnesses.append(roundnesses1)
# aspect_ratios.append(aspect_ratios1)
# formfactors.append(formfactors1)
# errors.append(errors1)
# rois.append(roi1)
print("loading model...")
model = load_model(
'/Users/2020shatgiskessell/Desktop/New_Mole_Detector/Mole_Detector_1_3/my_mole_model_2.h5'
)
for i in range(num_labels):
#start = timeit.default_timer()
#get component centroid coordinates
x, y = centroids[i]
#compute statistics
# cv2.CC_STAT_LEFT The leftmost (x) coordinate which is the inclusive start of the bounding box in the horizontal direction.
# cv2.CC_STAT_TOP The topmost (y) coordinate which is the inclusive start of the bounding box in the vertical direction.
# cv2.CC_STAT_WIDTH The horizontal size of the bounding box
# cv2.CC_STAT_HEIGHT The vertical size of the bounding box
# cv2.CC_STAT_AREA The total area (in pixels) of the connected component
width = stats[i, cv2.CC_STAT_WIDTH]
height = stats[i, cv2.CC_STAT_HEIGHT]
if height > 20:
continue
radius = (height + width) / 2
area = stats[i, cv2.CC_STAT_AREA]
#these are the top left x, y coordinates = ONLY TO BE USED FOR GETTING ROI
x_l = stats[i, cv2.CC_STAT_LEFT]
y_l = stats[i, cv2.CC_STAT_TOP]
#compute line
#slope, y_int= compute_lobf(x,y,x_l,y_l)
#stop = timeit.default_timer()
#print('Time to calculate properties: ', stop - start)
#remove everything except for component i to create isolated component matrix
#get connected component roi
roi = og[y_l - 1:y_l + height + 1, x_l - 1:x_l + width + 1]
try:
roi = cv2.resize(roi, (8, 8))
except Exception:
continue
roi = np.expand_dims(roi, axis=2)
roi = np.expand_dims(roi, axis=0)
pred = model.predict(roi)
pred = pred.round()
if int(pred[0][0]) == 1:
print("found blob")
blob = Blob()
blob.radius = radius
blob.x = x + x_i
blob.y = y + y_i
#blob.matrix = component_matrix
blob.roi = roi
blobs.append(blob)
# #----------------MEASURES-------------------------------------------------------------------
#
# #compute more statistics related to roundness
# radius = np.sqrt((area/np.pi))
# formfactor = compute_formfactor (radius, area)
# bounding_box_area_ratio = area/(height*width)
# if height > width:
# roundness = compute_roundness (height, radius, area)
# aspect_ratio = height/width
# else:
# roundness = compute_roundness (width, radius, area)
# aspect_ratio = width/height
#
# #calculates line of best fit and error
# try:
# cord1, cord2, error = compute_lobf(roi, x_l*y_l)
# x1,y1 = cord1
# x2,y2 = cord2
# except TypeError:
# print ("cant calculate line of best fit")
# error = 0
# #COMMENT OUT WHEN COLLECTING ANN DATA!!!!!
# if error < 16:
# continue
return blobs
