def enrichEmbed(img1,img2,img3):
img1_location = api.human_locations(img1)
img2_location = api.human_locations(img2)
img3_location = api.human_locations(img3)
img_1_human = api.crop_human(img1, img1_location)
img_2_human = api.crop_human(img2, img2_location)
img_3_human = api.crop_human(img3, img3_location)
human_1_1 = img_1_human[0]
# plt.imshow(human_1_1)
human_1_2 = img_2_human[0]
# plt.imshow(human_1_2)
# human 1 photo 2
human_1_3 = img_3_human[0]
# plt.imshow(human_1_3)
human_1_1_vector = api.human_vector(human_1_1)
human_1_2_vector = api.human_vector(human_1_2)
human_1_3_vector = api.human_vector(human_1_3)
return (human_1_1_vector +human_1_2_vector +human_1_3_vector )/3
def compare(path1, path2):
img1 = cv2.imread(path1)[:, :, ::-1]
img2 = cv2.imread(path2)[:, :, ::-1]
human_1_vector = api.human_vector(img1)
human_2_vector = api.human_vector(img2)
#print(api.human_distance(human_1_vector, human_2_vector))
if (api.human_distance(human_1_vector, human_2_vector) < 20):
return True
else:
return False
def find(self, img, past_ppl, qimg_vec):
maxp = 0
fd = 1
current_img_vector = api.human_vector(img)
folders = os.listdir(past_ppl)
ans = False
dist = api.human_distance(current_img_vector, qimg_vec)
if (dist < 15):
ans = True
else:
ans = False
for folder in folders:
same = 0
diff = 0
files = os.listdir(past_ppl + '/' + folder)
file = -1
for f in files:
file += 1
#print(f)
#print(folder)
#print(Videoreid.past_ppl_vector)
#print(int(folder))
#print(file)
file_vector = Videoreid.past_ppl_vector[int(folder) - 1][file]
distance = api.human_distance(current_img_vector, file_vector)
if (distance < 15):
same = +1
else:
diff = +1
p = 100 * float(same) / float(same + diff)
if (maxp < p):
maxp = p
fd = folder
if (maxp > 90):
files = os.listdir(past_ppl + '/' + fd)
person_no = len(files) + 1
cv2.imwrite(past_ppl + '/' + fd + '/' + str(person_no) + '.jpg',
img)
print('old person detected at ' + str(fd) + ' ' +
str(datetime.now().time()))
current_folder = Videoreid.past_ppl_vector[int(fd) - 1]
current_folder.append(current_img_vector)
Videoreid.past_ppl_vector[int(fd) - 1] = current_folder
return [ans, int(fd)]
else:
l = len(folders) + 1
print('new person detected at ' + str(l) + ' ' +
str(datetime.now().time()))
os.makedirs(past_ppl + '/' + str(l))
cv2.imwrite(past_ppl + '/' + str(l) + '/1.jpg', img)
folder_vector = []
folder_vector.append(current_img_vector)
Videoreid.past_ppl_vector.append(folder_vector)
#print(Videoreid.past_ppl_vector)
return [ans, l]
def video_reid(self, path, path_qimg):
arr = []
present = False
qimg = cv2.imread(path_qimg)
qimg_vec = api.human_vector(qimg)
cap = cv2.VideoCapture(path)
frame_height = int(cap.get(4))
frame_width = int(cap.get(3))
fps = int(cap.get(5))
fourcc = cv2.VideoWriter_fourcc(*'VP80')
base = os.path.basename(path)
filename = os.path.splitext(base)[0]
out = cv2.VideoWriter('../Output_Videos/' + filename + '.webm', fourcc,
fps, (frame_width, frame_height))
videoOn = True
past_ppl = '../Identity_Gallery'
while (videoOn):
ret, frame = cap.read()
if (ret == False):
break
arr = []
img_location = api.human_locations(frame)
img_human = api.crop_human(frame, img_location)
for j in range(len(img_human)):
arr.append(self.find(img_human[j], past_ppl, qimg_vec))
f = self.draw_boxes(frame, img_location, arr)
frame = f[0]
if (f[1] == True):
present = True
cv2.imshow('Video', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
out.write(frame)
cap.release()
out.release()
cv2.destroyAllWindows()
print('end')
return ['Project/Output_Videos/' + filename + '.webm', present]
def find1(self, img, past_ppl, ans):
maxp = 0
fd = 1
current_img_vector = api.human_vector(img)
folders = os.listdir(past_ppl)
for folder in folders:
same = 0
diff = 0
files = os.listdir(past_ppl + '/' + folder)
file = -1
for f in files:
file += 1
#print(f)
#print(folder)
#print(Videoreid.past_ppl_vector)
#print(int(folder))
#print(file)
file_vector = Videoreid.past_ppl_vector[int(folder) - 1][file]
distance = api.human_distance(current_img_vector, file_vector)
if (distance < 15):
same = +1
else:
diff = +1
p = 100 * float(same) / float(same + diff)
if (maxp < p):
maxp = p
fd = folder
if (maxp > 90):
ans.add(int(fd))
print('Person ' + str(fd) + ' detected in second video' + ' ' +
str(datetime.now().time()))
return [int(fd), True]
else:
l = len(folders) + 1
return [l, False]
# human 1 photo 2
human_1_2 = img_2_human[0]
plt.imshow(human_1_2)
# In[5]:
# human 2 photo 1
human_2_1 = img_3_human[0]
plt.imshow(human_2_1)
# # Make vector of human image
# In[6]:
t1 = timeit.default_timer()
human_1_1_vector = api.human_vector(human_1_1)
human_1_2_vector = api.human_vector(human_1_2)
human_2_1_vector = api.human_vector(human_2_1)
t2 = timeit.default_timer()
print('Time elapsed: {} sec'.format(round(t2 - t1, 3)))
# # Comparing vector
# In[7]:
api.human_distance(human_1_1_vector, human_1_2_vector)
# In[8]:
api.human_distance(human_1_2_vector, human_2_1_vector)
