def overlay_frame(image, template, match_features, show_templates=False):
list_template = template.create()
hits = locate_templates(image, list_template, match_features)
l = len(list_template)
if l < 12 and show_templates:
f, axarr = plt.subplots(1, l)
for i in range(l):
axarr[i].imshow(list_template[i][1])
overlay = drawBoxesOnRGB(image, hits, boxThickness=5)
plt.figure(figsize=(30, 30))
plt.imshow(overlay)
cv2.imshow('No red white region', image_nored)
cv2.waitKey(1)
#In[4]: APPLY TEMPLATE MATCHING WITHOUT RED REGION
listTemplate_inwhite = [('A', BGR_featureimg_kngp),
('B', BGR_featureimg_kngp_2),
('G', BGR_featureimg_kngp_3),
('D', BGR_featureimg_kngp_4)
] #,('E', BGR_featureimg_kngp_5)]
Hits = matchTemplates(listTemplate_inwhite,
image_nored,
score_threshold=0.55,
method=cv2.TM_CCOEFF_NORMED,
maxOverlap=0)
Overlay_inwhite = drawBoxesOnRGB(image_nored, Hits, showLabel=True)
total_inwhite = len(Hits)
listTemplate_blueinwhite = [('*A', BGR_featureimg_kngn),
('*B', BGR_featureimg_kngn_2),
('*C', BGR_featureimg_kngn_3)]
Hits_blueinwhite = matchTemplates(listTemplate_blueinwhite,
image_nored,
score_threshold=0.75,
method=cv2.TM_CCOEFF_NORMED,
maxOverlap=0)
Overlay_blueinwhite = drawBoxesOnRGB(image_nored,
Hits_blueinwhite,
showLabel=True)
for i in range(0, len(br1.TemplateName)):
df1 = df[df.TemplateName == f1[i]]
score = round(max(df1.Score), 2)
score1 = round((score - 0.35), 2)
df2 = df1[(df1.Score == score) | (df1.Score >= score1)]
df3 = df3.append(df2)
df3["name"] = name1
#df3.to_csv(name1+".csv",index=False)
#Overlay1 = drawBoxesOnRGB(im, finalHits,showLabel=True,labelScale=0.4,labelColor=(0, 255, 255), boxThickness=2)
Overlay2 = drawBoxesOnRGB(
imageNoise,
df3,
showLabel=True,
labelScale=0.2,
labelColor=(0, 255, 255),
boxThickness=1) ###########draw the box on image
#path=r'D:\video_template\plots'###############path to save image
# plt.savefig(path+"\\"+name1+'.png')
plt.figure(figsize=(20, 20))
plt.axis("off")
plt.imshow(Overlay2)
plt.savefig(path + "/" + name1 + '.png')
plt.close()
br = df3[['TemplateName', 'name']]
s2 = br.pivot_table(index=['name'],
import matplotlib.pyplot as plt
import numpy as np
fileID = r"C:\Users\MattiaV\Desktop\università\Interships\DynamicsOfGranularShapes\ParticleTracking\Images\First frames\f1.png"
image = io.imread(fileID)
temp_draft = image[300:500,500:670]
temp0 = temp_draft[41:113,48:123]
image2 = io.imread(r"C:\Users\MattiaV\Desktop\università\Interships\DynamicsOfGranularShapes\ParticleTracking\Images\First frames\f2.png")
listTemplate = []
# Initialise figure
f, axarr = plt.subplots(1,4)
for i in np.arange(0, 360, 90):
#rotated1 = t.rotate(temp0, i, preserve_range=True)
#rotated = np.rint(rotated1).astype(int) # transforms array of float into array of int
rotated = np.rot90(temp0, k=int(i/90)) # NB: np.rotate not good here, turns into float!
listTemplate.append( (str(i%120), rotated ) )
axarr[int(i/90)].imshow(rotated)
# We could also do some flipping with np.fliplr, flipud
Hits = matchTemplates(listTemplate, image2, N_object=50, score_threshold=0.55, method=cv2.TM_CCOEFF_NORMED, maxOverlap=0.22).sort_index()
Overlay = drawBoxesOnRGB(image, Hits, boxThickness=5)
plt.figure(figsize = (30,30))
plt.axis("off")
plt.imshow(Overlay)
SampleImg,
method=cv2.TM_CCOEFF_NORMED,
score_threshold=0.89,
maxOverlap=0,
)
print("Found {} hits".format(len(Hits.index)))
Hits
#Is it becuase the images are not floated?
# In[6]:
#Show Image with Template Lables
get_ipython().run_line_magic('matplotlib', 'notebook')
Overlay = drawBoxesOnRGB(ColorImg, Hits, showLabel=True)
plt.imshow(Overlay)
# In[7]:
#Print Results to Excel Spread Sheet
PrintBerg = pd.DataFrame(Hits)
print(PrintBerg)
try:
PrintBerg.to_excel(r'C:/Users/Beth/Desktop/PrintBerg.xlsx',
index=False,
header=True)
except:
print("ERROR: Could Not Print")
cv2.imshow('Red Removed Region', image_nored)
cv2.waitKey(1)
# In[4]: APPLY TEMPLATE MATCHING WITHOUT RED REGION
listTemplate_inwhite = [
('A', BGR_featureimg_kngp), ('B', BGR_featureimg_kngp_2),
('G', BGR_featureimg_kngp_3), ('D', BGR_featureimg_kngp_4)
] #,('E', BGR_featureimg_kngp_5)]
Hits = matchTemplates(listTemplate_inwhite,
image_nored,
score_threshold=0.55,
method=cv2.TM_CCOEFF_NORMED,
maxOverlap=0)
Overlay_inwhite = drawBoxesOnRGB(image_nored, Hits, showLabel=True)
total_inwhite = len(Hits)
listTemplate_blueinwhite = [('*A', BGR_featureimg_kngn),
('*B', BGR_featureimg_kngn_2),
('*C', BGR_featureimg_kngn_3)]
Hits_blueinwhite = matchTemplates(listTemplate_blueinwhite,
image_nored,
score_threshold=0.75,
method=cv2.TM_CCOEFF_NORMED,
maxOverlap=0)
Overlay_blueinwhite = drawBoxesOnRGB(image_nored,
Hits_blueinwhite,
showLabel=True)
df3 = pd.DataFrame(columns=[
"index", "TemplateName", "BBox", "Score", "width", "hight"
])
for i in range(0, len(br1.TemplateName)):
df1 = df[df.TemplateName == f1[i]]
score = round(max(df1.Score), 2)
score1 = round((score - 0.35), 2)
df2 = df1[(df1.Score == score) | (df1.Score >= score1)]
df3 = df3.append(df2)
df3["name"] = name1
Overlay2 = drawBoxesOnRGB(imageNoise,
df3,
showLabel=True,
labelScale=0.4,
labelColor=(0, 255, 255),
boxThickness=2)
plt.figure(figsize=(20, 20))
plt.axis("off")
plt.imshow(Overlay2)
plt.savefig(path + name1 + '.png')
plt.close()
br = df3[['TemplateName', 'name']]
s2 = br.pivot_table(index=['name'],
columns=['TemplateName'],
aggfunc=len).fillna(0).reset_index()
br2 = br.groupby(["name",
'TemplateName']).size().reset_index(name="Time")
images = os.listdir(input_dir)
print(images)
for image in images:
imagefile = input_dir + "/" + image
host = cv2.imread(imagefile)
img = cv2.cvtColor(host, cv2.COLOR_BGR2GRAY)
h, w = img.shape
plt.imshow(host)
plt.show()
thres = float(input("enter threshold:"))
tar = cv2.imread(
"/home/younesh/Desktop/projects/TemplateMatching/tar007.png")
hits = TemplateMmatching(tar, host, thres) # thresh = 0.326
print(hits)
Overlay = drawBoxesOnRGB(host, hits, showLabel=True)
plt.imshow(Overlay)
plt.show()
conform = input("should we write the data to the file?(y/n)")
if (conform == "y"):
coordinatesToFile(hits, image, h, w)
while (conform == "n"):
thres = float(input("enter threshold:"))
hits = TemplateMmatching(tar, host, thres) # thresh = 0.326
Overlay = drawBoxesOnRGB(host, hits, showLabel=True)
plt.imshow(Overlay)
plt.show()
conform = input("should we write the data to the file?(y/n)")
if (conform == "y"):
coordinatesToFile(hits, image, h, w)
break
listTemplate = [('marker', resized)]
Hits = matchTemplates(
listTemplate,
new_copy,
N_object=4,
score_threshold=0.4,
maxOverlap=0,
method=cv2.TM_CCOEFF_NORMED) #,searchBox=(76,781,1856,353)
Overlay = paper.copy()
for index in Hits.index:
x1, y1, w, h = Hits.BBox[index]
Overlay = drawBoxesOnRGB(paper, Hits)
Sum_scores = Sum_scores + Hits.Score[index]
#print("Sum_scores : ",Sum_scores)
#cv2.imshow('template_matched_paper',cv2.resize(Overlay,(620,620)))
#cv2.waitKey(0)
### Checking is it the best scale ?
if (allMaxT < Sum_scores):
centres = []
allMaxT, best_scale = Sum_scores, scale
Best_BBox = Hits.BBox.copy()
h_resized = resized.shape[0]
w_resized = resized.shape[1]
BBox, Score, templateName
"""
list_ = []
for i in os.listdir(template_dir):
template = cv2.imread(template_dir + i, 0)
list_.append((str(i), template))
match = matchTemplates(list_,
image,
score_threshold=thresh / 100,
method=cv2.TM_CCOEFF_NORMED,
maxOverlap=0.30)
print("Found {} Ellipses".format(len(match.index)))
print(match)
return match
matches = match_image_template(image_gray, template_dir, 30)
# %%
image = cv2.imread("imgg2.png", )
draw_matches = drawBoxesOnRGB(image,
matches,
boxColor=(0, 255, 0),
showLabel=False)
plt.imshow(draw_matches)
cv2.waitKey(1)
#In[4]: APPLY TEMPLATE MATCHING WITHOUT RED REGION
listTemplate_inwhite = [('A', BGR_featureimg_kngp),
('B', BGR_featureimg_kngp_2),
('G', BGR_featureimg_kngp_3),
('D', BGR_featureimg_kngp_4)
] #,('E', BGR_featureimg_kngp_5)]
Hits = matchTemplates(listTemplate_inwhite,
image_nored,
score_threshold=0.55,
method=cv2.TM_CCOEFF_NORMED,
maxOverlap=0)
Overlay_inwhite = drawBoxesOnRGB(BGR_cropimg_watershed_inred,
Hits,
showLabel=True)
#Overlay_inwhite = drawBoxesOnRGB(image_nored, Hits, showLabel=True)
total_inwhite = len(Hits)
listTemplate_blueinwhite = [('*A', BGR_featureimg_kngn),
('*B', BGR_featureimg_kngn_2),
('*C', BGR_featureimg_kngn_3)]
Hits_blueinwhite = matchTemplates(listTemplate_blueinwhite,
image_nored,
score_threshold=0.75,
method=cv2.TM_CCOEFF_NORMED,
maxOverlap=0)
Overlay_blueinwhite = drawBoxesOnRGB(BGR_cropimg_watershed_inred,
Hits_blueinwhite,
imageNoise = cv2.add(image, noise, dtype=cv2.CV_8U)
plt.figure(figsize=(10, 10))
plt.axis("off")
plt.imshow(imageNoise, cmap="gray")
# Call again matchTemplates with the noisy image this time, but the same template
Hits_Noise = matchTemplates(listTemplate,
imageNoise,
N_object=4,
score_threshold=0.3,
method=cv2.TM_CCOEFF_NORMED,
maxOverlap=0.3)
print("Initial detections")
print(Hits)
print("\nDetections with noise")
print(Hits_Noise)
Overlay1 = drawBoxesOnRGB(image, Hits, boxThickness=5)
Overlay2 = drawBoxesOnRGB(imageNoise, Hits_Noise, boxThickness=5)
plt.figure(figsize=(10, 10))
plt.axis("off")
plt.imshow(Overlay1)
plt.figure(figsize=(10, 10))
plt.axis("off")
plt.imshow(Overlay2)
smallCoin = coins()[37:37 + 38, 80:80 + 41]
plt.imshow(smallCoin, cmap="gray")
# 1st format the template into a list of tuple (label, templateImage)
listTemplate = [('small', smallCoin)]
# Then call the function matchTemplates (here a single template)
Hits = matchTemplates(listTemplate,
image,
score_threshold=0.5,
method=cv2.TM_CCOEFF_NORMED,
maxOverlap=0)
print("Found {} hits".format(len(Hits.index)))
Overlay = drawBoxesOnRGB(image, Hits, showLabel=True)
plt.imshow(Overlay)
Hits = matchTemplates(listTemplate,
image,
score_threshold=0.4,
method=cv2.TM_CCOEFF_NORMED,
maxOverlap=0)
Overlay = drawBoxesOnRGB(image, Hits, showLabel=True)
plt.imshow(Overlay)
largeCoin = coins()[14:14 + 59, 302:302 + 65]
plt.figure(0)
plt.imshow(smallCoin, cmap="gray")
plt.figure(1)
plt.imshow(largeCoin, cmap="gray")