Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

some code and simplyffy #15

Open
wants to merge 1 commit into
base: master
Choose a base branch
from
Open

some code and simplyffy #15

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
15 changes: 8 additions & 7 deletions src/main/deep_learning_service.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -93,11 +93,10 @@ async def run_model(self, model_name, input_data, draw, predict_batch):
try:
if predict_batch:
return await self.models_dict[model_name].run_batch(input_data, draw, predict_batch)
if not draw:
return await self.models_dict[model_name].infer(input_data, draw, predict_batch)
else:
if not draw:
return await self.models_dict[model_name].infer(input_data, draw, predict_batch)
else:
await self.models_dict[model_name].infer(input_data, draw, predict_batch)
await self.models_dict[model_name].infer(input_data, draw, predict_batch)
except ApplicationError as e:
raise e
else:
Expand Down Expand Up @@ -147,9 +146,11 @@ def get_labels_custom(self, model_name):
model_name = key
models = self.list_models()
if model_name not in self.labels_hash_dict:
model_dict = {}
for label in self.models_dict[model_name].labels:
model_dict[label] = str(uuid.uuid4())
model_dict = {
label: str(uuid.uuid4())
for label in self.models_dict[model_name].labels
}

self.labels_hash_dict[model_name] = model_dict
for key in list(self.labels_hash_dict):
if key not in models:
Expand Down
21 changes: 9 additions & 12 deletions src/main/object_detection/utils/visualization_utils.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -258,9 +258,7 @@ def draw_bounding_boxes_on_image(image,
if len(boxes_shape) != 2 or boxes_shape[1] != 4:
raise ValueError('Input must be of size [N, 4]')
for i in range(boxes_shape[0]):
display_str_list = ()
if display_str_list_list:
display_str_list = display_str_list_list[i]
display_str_list = display_str_list_list[i] if display_str_list_list else ()
draw_bounding_box_on_image(image, boxes[i, 0], boxes[i, 1], boxes[i, 2],
boxes[i, 3], color, thickness, display_str_list)

Expand Down Expand Up @@ -497,8 +495,8 @@ def draw_keypoints_on_image(image,
keypoints_x = [k[1] for k in keypoints]
keypoints_y = [k[0] for k in keypoints]
if use_normalized_coordinates:
keypoints_x = tuple([im_width * x for x in keypoints_x])
keypoints_y = tuple([im_height * y for y in keypoints_y])
keypoints_x = tuple(im_width * x for x in keypoints_x)
keypoints_y = tuple(im_height * y for y in keypoints_y)
for keypoint_x, keypoint_y in zip(keypoints_x, keypoints_y):
draw.ellipse([(keypoint_x - radius, keypoint_y - radius),
(keypoint_x + radius, keypoint_y + radius)],
Expand Down Expand Up @@ -617,13 +615,12 @@ def visualize_boxes_and_labels_on_image_array(
box_to_color_map[box] = groundtruth_box_visualization_color
else:
display_str = ''
if not skip_labels:
if not agnostic_mode:
if classes[i] in category_index.keys():
class_name = category_index[classes[i]]['name']
else:
class_name = 'N/A'
display_str = str(class_name)
if not skip_labels and not agnostic_mode:
if classes[i] in category_index.keys():
class_name = category_index[classes[i]]['name']
else:
class_name = 'N/A'
display_str = str(class_name)
if not skip_scores:
if not display_str:
display_str = '{}%'.format(int(100*scores[i]))
Expand Down
46 changes: 28 additions & 18 deletions src/main/ocr.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -2,66 +2,75 @@
import unicodedata
import re
import numpy as np
#from PIL import Image, ImageFont, ImageDraw, ImageEnhance

# from PIL import Image, ImageFont, ImageDraw, ImageEnhance


# Define class variables

bounding_box_order = ["left", "top", "right", "bottom"]


# This method will take the model bounding box predictions and return the extracted text inside each box
def one_shot_ocr_service(image, output):
# iterate over detections
response = []
detections = output['bounding-boxes']

for i in range(0, len(detections)):
for i in range(len(detections)):

# crop image for every detection:
coordinates = (detections[i]["coordinates"])
cropped = image.crop((float(coordinates["left"]), float(
coordinates["top"]), float(coordinates["right"]), float(coordinates["bottom"])))

# convert image to grayscale for better accuracy
processed_img=cropped.convert('L')
# convert image to grayscale for better accuracy
processed_img = cropped.convert('L')

# extract text with positive confidence from cropped image
df = pytesseract.image_to_data(processed_img, output_type='data.frame')
valid_df = df[df["conf"] > 0]
extracted_text = " ".join(valid_df["text"].values)

# process text
extracted_text = str(unicodedata.normalize('NFKD', extracted_text).encode('ascii', 'ignore').decode()).strip().replace("\n", " ").replace(
"...", ".").replace("..", ".").replace('”', ' ').replace('“', ' ').replace("'", ' ').replace('\"', '').replace("alt/1m", "").strip()
extracted_text = str(
unicodedata.normalize('NFKD', extracted_text).encode('ascii', 'ignore').decode()).strip().replace("\n",
" ").replace(
"...", ".").replace("..", ".").replace('”', ' ').replace('“', ' ').replace("'", ' ').replace('\"',
'').replace(
"alt/1m", "").strip()
extracted_text = re.sub(
'[^A-Za-z0-9.!?,;%:=()\[\]$€&/\- ]+', '', extracted_text)
extracted_text = " ".join(extracted_text.split())

# wrap each prediction inside a dictionary
if len(extracted_text) is not 0:
prediction = dict()
prediction["text"] = extracted_text
prediction = {'text': extracted_text}
bounding_box = [coordinates[el] for el in bounding_box_order]
prediction["box"] = bounding_box
prediction["score"] = valid_df["conf"].mean()/100.0
prediction["score"] = valid_df["conf"].mean() / 100.0

response.append(prediction)

return response


# This method will take an image and return the extracted text from the image
def ocr_service(image):
# convert image to grayscale for better accuracy
processed_img=image.convert('L')
# convert image to grayscale for better accuracy
processed_img = image.convert('L')

# Get data including boxes, confidences, line and page numbers
df = pytesseract.image_to_data(processed_img, output_type='data.frame')
valid_df = df[df["conf"] > 0]

# process text
extracted_text = " ".join(valid_df["text"].values)
extracted_text = str(unicodedata.normalize('NFKD', extracted_text).encode('ascii', 'ignore').decode()).strip().replace("\n", " ").replace(
"...", ".").replace("..", ".").replace('”', ' ').replace('“', ' ').replace("'", ' ').replace('\"', '').replace("alt/1m", "").strip()
extracted_text = str(
unicodedata.normalize('NFKD', extracted_text).encode('ascii', 'ignore').decode()).strip().replace("\n",
" ").replace(
"...", ".").replace("..", ".").replace('”', ' ').replace('“', ' ').replace("'", ' ').replace('\"', '').replace(
"alt/1m", "").strip()
extracted_text = re.sub(
'[^A-Za-z0-9.!?,;%:=()\[\]$€&/\- ]+', '', extracted_text)
extracted_text = " ".join(extracted_text.split())
Expand All @@ -78,9 +87,10 @@ def ocr_service(image):
bounding_box = [coordinates[el].item() for el in bounding_box_order]

# wrap each prediction inside a dictionary
response = {}
response["text"] = extracted_text
response["box"] = bounding_box
response["score"] = valid_df["conf"].mean()/100.0
response = {
'text': extracted_text,
'box': bounding_box,
'score': valid_df["conf"].mean() / 100.0,
}

return [response]