def get_document_bounds(videoIO, feature, folderPath, youtube_id):
client = MongoClient(
'mongodb+srv://qhacks:[email protected]/test?retryWrites=true'
)
db = client['qhacks']
global notes_screenshots
global glob_data
global length
global prev_file
global prev_time
global secs
global temp
"""Returns document bounds given an image."""
# setting up frame by frame per 5 secs
myclip = VideoFileClip(videoIO)
frames = []
for frame in myclip.iter_frames(fps=0.2):
frames.append(frame)
# print("hi")
for count, single_frame in enumerate(frames, start=1):
# print("stephen")
#print(i)
img = Image.fromarray(single_frame, 'RGB')
dir_path = os.path.dirname(os.path.realpath(__file__))
#print(dir_path)
file = "/file%d.png" % count
#print(file)
#print(folderPath)
filename = dir_path + "/" + folderPath + "/" + file
#print(filename)
img.save(filename)
#img.show()
# if length is 5:
# break
# img_process = img.tobytes()
#runnin the image processor
first = True
build_word = ""
words = []
# words.append("a")
client = vision.ImageAnnotatorClient()
bounds = []
temp = filename
#CHANGED HERE
# content = img_process
# content = Image.open(filename)
with io.open(filename, 'rb') as image_file:
content = image_file.read()
# print(content)
image = types.Image(content=content)
response = client.document_text_detection(image=image)
document = response.full_text_annotation
# Collect specified feature bounds by enumerating all document features
for page in document.pages:
for block in page.blocks:
for paragraph in block.paragraphs:
for word in paragraph.words:
# for symbol in word.symbols:
#if (feature == FeatureType.SYMBOL):
#bounds.append(symbol.bounding_box)
#if (feature == FeatureType.WORD):
if first and feature == FeatureType.WORD:
bounds.append(word.bounding_box)
first = False
for i in word.symbols:
if hasattr(i, "text"):
if i.property.detected_break.type is 0:
build_word += i.text
else:
build_word += i.text
# print(build_word)
words.append(build_word)
build_word = ""
#if (feature == FeatureType.PARA):
#bounds.append(paragraph.bounding_box)
#if (feature == FeatureType.BLOCK):
#bounds.append(block.bounding_box)
#if (feature == FeatureType.PAGE):
#bounds.append(block.bounding_box)
# The list `bounds` contains the coordinates of the bounding boxes.
# temp = {
# "bound": bounds
# }
# bound_data = {
# "v0x": temp['bound'][0].vertices[0].x,
# "v0y": temp['bound'][0].vertices[0].y,
# "v1x": temp['bound'][0].vertices[1].x,
# "v1y": temp['bound'][0].vertices[1].y,
# "v2x": temp['bound'][0].vertices[2].x,
# "v2y": temp['bound'][0].vertices[2].y,
# "v3x": temp['bound'][0].vertices[3].x,
# "v3y": temp['bound'][0].vertices[3].y,
# }
collection_texts = db['timestamps']
for i in words:
db_data = {"secs": secs, "keyword": i, "youtube_id": "tasty"}
collection_texts.insert_one(db_data)
# db_data = {
# "secs": secs,
# "keyword": words,
# "youtube_id": "hello"
# }
data = {secs: words}
if (len(data[secs]) == 0):
data = {secs: "a"}
#print(data)
# print(data['bound'])
# print(data['bound'])
# print(type(data['bound'][0]))
# print(type(data['bound'][0].vertices))
# print(type(data['bound'][0].vertices[0].x))
glob_data.append(data)
length += 1
if length > 1:
if glob_data[length - 1][secs][0] and glob_data[length -
2][prev_time][0]:
if glob_data[length -
1][secs][0] == glob_data[length -
2][prev_time][0]:
prev_file = temp
prev_time = secs
else:
screenshot_data = {
"secs": secs,
"file": prev_file,
"youtube_id": "tasty"
}
notes_screenshots.append(screenshot_data)
prev_file = temp
prev_time = secs #HERE BABY
# imagerino = Image.open(prev_file)
# imagerino.show()
secs += 5
# print(glob_data)
# print("STEPHENNNNNN")
# print(screenshot_data)
collection_screenshots = db['screenshots']
collection_screenshots.insert_many(notes_screenshots)
