import time
import cv2
import google_ocr
import dyanmo_ocr
import dynamodb
camera_port = 0
def image_capture():
camera = cv2.VideoCapture(camera_port)
time.sleep(5) # After 5sec camera will open
return_value, image = camera.read()
cv2.imwrite("C:/Users/Rupali Singh/PycharmProjects/Drishti/opencv.png",
image)
del (camera)
return image
if __name__ == '__main__':
image_capture()
i = 1
while (i == 1):
google_ocr.detect_text(
path="C:/Users/Rupali Singh/PycharmProjects/Drishti/opencv.png")
dynamodb.main("TextImage")
i = i - 1
def find_devide_point(dirId, n):
dirpath = "images{0:02d}".format(dirId)
df = pd.DataFrame(index=[], columns=['id', 'time', 'text', 'state'])
imId = 1
state = 0 # text: exist = 1, none = 0
y = np.zeros(150)
pbar = tqdm(total=120)
cnt = 0
hists = np.array([])
before_text = ""
while (os.path.isfile(dirpath + "/image{}.jpg".format(imId))):
pbar.update(1)
path = dirpath + "/image{}.jpg".format(imId)
img = cv2.imread(path)
mask = extract_text.extract_white(img)
rects = extract_text.get_rects(mask)
height, width = img.shape[:2]
rects = [
rect for rect in rects if rect[2] * rect[3] > height * width / n
]
# textが存在しない場合
if not rects:
if state:
state = 0
y = np.zeros(150)
series = pd.Series(
[imId - 1, (imId - 1) * 0.5, before_text, -1],
index=df.columns)
df = df.append(series, ignore_index=True)
imId += 1
continue
x = whitehist(rects, mask, n)
min_x = min(rects, key=(lambda x: x[0]))
min_y = min(rects, key=(lambda x: x[1]))
max_w = max(rects, key=(lambda x: x[0] + x[2]))
max_h = max(rects, key=(lambda x: x[1] + x[3]))
max_rect = np.array([
min_x[0], min_y[1], max_w[0] - min_x[0] + max_w[2],
max_h[1] - min_y[1] + max_h[3]
])
# 画面がホワイトアウトした場合
if max_rect[2] * max_rect[3] >= height * width:
if state:
state = 0
y = x
series = pd.Series(
[imId - 1, (imId - 1) * 0.5, before_text, -1],
index=df.columns)
df = df.append(series, ignore_index=True)
imId += 1
continue
if isChange(x, y):
cnt += 1
text = google_ocr.detect_text(dirId, imId)
text = text.replace(" ",
"").replace("\n",
"").replace(u' ',
"").replace("\t", "")
if mojimoji.zen_to_han(text) == mojimoji.zen_to_han(before_text):
imId += 1
y = x
continue
if state == 0:
if text == "":
imId += 1
y = x
before_text = text
continue
state = 1
y = x
series = pd.Series([imId, imId * 0.5, text, 1],
index=df.columns)
df = df.append(series, ignore_index=True)
before_text = text
else:
state = 1
series = pd.Series(
[imId - 1, (imId - 1) * 0.5, before_text, -1],
index=df.columns)
df = df.append(series, ignore_index=True)
y = x
before_text = text
if text:
series = pd.Series([imId, imId * 0.5, text, 1],
index=df.columns)
df = df.append(series, ignore_index=True)
y = x
imId += 1
datadir = "data"
if not os.path.isdir(datadir):
os.makedirs(datadir)
df.to_csv(datadir + "/" + dirpath + ".csv")
pbar.close()
print(cnt)
#face_image = cv2.rectangle( face_image, (left,top), (right, bottom), (255,0,0))
# Put the blurred face region back into the frame image
#frame[top:bottom, left:right] = face_image
# Display the resulting image
cv2.imshow('Face detect', frame)
# Hit 'q' on the keyboard to quit!
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cv2.imwrite("face.jpg", frame)
# Initialize some variables
while True:
# Grab a single frame of video
ret, frame = video_capture.read()
cv2.rectangle(frame, (100, 100), (600, 400), (0, 0, 255), 3)
# Display the resulting image
cv2.imshow('document', frame)
# Hit 'q' on the keyboard to quit!
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cv2.imwrite("document.jpg", frame[100:400, 100:600])
google_ocr.detect_text("document.jpg")
# Release handle to the webcam
video_capture.release()
cv2.destroyAllWindows()
import time
import cv2
import google_ocr
import dynamodb
camera_port = 0
def image_capture():
camera = cv2.VideoCapture(camera_port)
time.sleep(5) # After 5sec camera will open
return_value, image = camera.read()
cv2.imwrite("D:/Drishti-ocr/opencv.png", image)
del (camera)
return image
if __name__ == '__main__':
image_capture()
i = 1
while (i == 1):
google_ocr.detect_text(path="D:/Drishti-ocr/opencv.png")
dynamodb.main("TextImage")
i = i - 1
def captureimage():
video_capture = cv2.VideoCapture(0)
# Initialize some variables
face_locations = []
count = 30
while True and count > 0:
count -= 1
# Grab a single frame of video
ret, frame = video_capture.read()
# Resize frame of video to 1/4 size for faster face detection processing
small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
# Find all the faces and face encodings in the current frame of video
face_locations = face_recognition.face_locations(small_frame,
model="cnn")
# Display the results
for top, right, bottom, left in face_locations:
# Scale back up face locations since the frame we detected in was scaled to 1/4 size
top *= 4
right *= 4
bottom *= 4
left *= 4
# Extract the region of the image that contains the face
frame = frame[top:bottom, left:right]
# Blur the face image
#face_image = cv2.GaussianBlur(face_image, (99, 99), 30)
#face_image = cv2.rectangle( face_image, (left,top), (right, bottom), (255,0,0))
# Put the blurred face region back into the frame image
#frame[top:bottom, left:right] = face_image
font = cv2.FONT_HERSHEY_SIMPLEX
bottomLeftCornerOfText = (0, 30)
fontScale = 1
fontColor = (255, 255, 255)
lineType = 2
# Display the resulting image
cv2.putText(frame, str(count), bottomLeftCornerOfText, font, fontScale,
fontColor, lineType)
cv2.imshow('Video', frame)
# Display the resulting image
#cv2.imshow('Face detect', frame)
# Hit 'q' on the keyboard to quit!
if cv2.waitKey(1) & 0xFF == ord('q'):
break
face_frame = frame
video_capture.release()
cv2.destroyAllWindows()
video_capture = cv2.VideoCapture(0)
# Initialize some variables
count = 100
while True and count > 0:
count -= 1
# Grab a single frame of video
ret, frame = video_capture.read()
cv2.rectangle(frame, (100, 100), (600, 400), (0, 0, 255), 3)
font = cv2.FONT_HERSHEY_SIMPLEX
bottomLeftCornerOfText = (80, 80)
fontScale = 1
fontColor = (255, 255, 255)
lineType = 2
# Display the resulting image
cv2.putText(frame, str(count), bottomLeftCornerOfText, font, fontScale,
fontColor, lineType)
# Display the resulting image
cv2.imshow('document', frame)
# Hit 'q' on the keyboard to quit!
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cv2.imwrite("document.jpg", frame[100:400, 100:600])
result = google_ocr.detect_text("document.jpg")
# Release handle to the webcam
video_capture.release()
cv2.destroyAllWindows()
T.insert(END, "Is this your name?: {}\n".format(result["PERSON"][0]))
print(result["PERSON"][0])
print(face_frame)
if result["PERSON"][0]:
new_face_image = config.IMAGE_PATH + result["PERSON"][0] + ".jpg"
cv2.imwrite(new_face_image, face_frame)
image = face_recognition.load_image_file(new_face_image)
face_encoding = face_recognition.face_encodings(image)[0]
face_encoding.tofile(config.ENCODED_IMAGE + result["PERSON"][0] +
".enc")
T.insert(END, "We have recorded you!\n")