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added roboflow example #855

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510 changes: 510 additions & 0 deletions examples/roboflow-logging/README.md

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42 changes: 42 additions & 0 deletions examples/roboflow-logging/client-multiple-endpoints.py
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import cv2, requests, json, argparse
import matplotlib.pyplot as plt

parser = argparse.ArgumentParser(description="Pass a name to a file to be annotated")
parser.add_argument('--image_path', default="test/images/a9f16c_2_9_png.rf.c048a60764e56735d7465cdec974d102.jpg")

MODEL_IMG_SIZE = 640
ENDPOINT_URL_FINETUNED = "http://localhost:5543/yolov5-s-finetuned/predict/from_files"
ENDPOINT_URL_COCO = "http://localhost:5543/yolov5-s-coco/predict/from_files"

def main(image_path):
for endpoint_url in [ENDPOINT_URL_FINETUNED, ENDPOINT_URL_COCO]:
im = cv2.imread(image_path)
im_size = im.shape[:2]
assert im_size[0] == im_size[1]
scale_ratio = im_size[0] / MODEL_IMG_SIZE

resp = requests.post(
url=endpoint_url,
files=[('request', open(image_path, 'rb'))]
)

boxes = json.loads(resp.text)['boxes'][0]
for xmin, ymin, xmax, ymax in boxes:
start_point = (int(xmin * scale_ratio), int(ymin * scale_ratio))
end_point = (int(xmax * scale_ratio), int(ymax * scale_ratio))
color = (0, 255, 0)
thickness = 2
im = cv2.rectangle(im, start_point, end_point, color, thickness)

plt.figure(figsize=(15, 15))
plt.axis("off")
plt.imshow(im)

if endpoint_url == ENDPOINT_URL_FINETUNED:
plt.savefig("annotated-finetuned.png")
else:
plt.savefig("annotated-coco.png")

if __name__ == '__main__':
args = parser.parse_args()
main(args.image_path)
27 changes: 27 additions & 0 deletions examples/roboflow-logging/client-send-all.py
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import requests, os, time

# the dataset we downloaded had 3 subsets in 3 folders
paths = []
for folder_name in ['test', 'valid', 'train']:
path = f"{folder_name}/images/"
paths += [path + img_name for img_name in os.listdir(path)]


# same URL for the endpoint as before
ENDPOINT_URL = "http://localhost:5543/yolov5-s-coco/predict/from_files"


# send each image to the endpoint
i = 0
print(f"Sending {len(paths)} images to the server")
for image_path in paths:
if i % 60 == 0:
print(i)
i+=1

resp = requests.post(
url=ENDPOINT_URL,
files=[('request', open(image_path, 'rb'))]
)
print(resp)
time.sleep(1)
11 changes: 11 additions & 0 deletions examples/roboflow-logging/client-send-one.py
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import requests, json

ENDPOINT_URL = "http://localhost:5543/yolov5-s-coco/predict/from_files"
IMAGE_PATH = "test/images/4b770a_3_6_png.rf.f5d975605c1f73e1a95a1d8edc4ce5b1.jpg"

resp = requests.post(
url=ENDPOINT_URL,
files=[('request', open(IMAGE_PATH, 'rb'))]
)

print(json.loads(resp.text))
1,399 changes: 1,399 additions & 0 deletions examples/roboflow-logging/example-client.ipynb

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Binary file added examples/roboflow-logging/images/labeling.png
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2 changes: 2 additions & 0 deletions examples/roboflow-logging/server/requirements.txt
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deepsparse[server,yolo]
roboflow
28 changes: 28 additions & 0 deletions examples/roboflow-logging/server/roboflow-logger.py
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from deepsparse.loggers import BaseLogger, MetricCategories
import typing, PIL, io, requests, datetime
from requests_toolbelt.multipart.encoder import MultipartEncoder

class RoboflowLogger(BaseLogger):

# the arguments to the construction will be defined in the config file
def __init__(self, dataset_name: str, api_key: str):
# per Roboflow docs
self.upload_url = f"https://api.roboflow.com/dataset/{dataset_name}/upload?api_key={api_key}"
super(RoboflowLogger, self).__init__()

# this function will be called from DeepSparse Server, based on the config
def log(self, identifier: str, value: typing.Any, category: typing.Optional[str]=None):
if category == MetricCategories.DATA:
# unpacks value and converts to image in a buffer
img = PIL.Image.fromarray(value.images[0], mode="RGB")
buffered = io.BytesIO()
img.save(buffered, quality=90, format="JPEG")

# packs as multipart
img_name = f"production-image-{datetime.datetime.now()}.jpg"
m = MultipartEncoder(fields={'file': (img_name, buffered.getvalue(), "image/jpeg")})

# uploads to roboflow
r = requests.post(self.upload_url, data=m, headers={'Content-Type': m.content_type})

print("request_complete")
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## server-config-no-roboflow-logging.yaml
loggers:
python:

endpoints:
- task: yolo
model: zoo:cv/detection/yolov5-s/pytorch/ultralytics/coco/pruned_quant-aggressive_94
name: yolov5-s-coco
route: /yolov5-s-coco/predict
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# server-config-roboflow-logging.yaml
loggers:
roboflow_logger:
path: server/roboflow-logger.py:RoboflowLogger
api_key: YOUR_API_KEY
dataset_name: YOUR_DATASET_NAME

endpoints:
- task: yolo
model: zoo:cv/detection/yolov5-s/pytorch/ultralytics/coco/pruned_quant-aggressive_94
name: yolov5-s-coco
route: /yolov5-s-coco/predict
data_logging:
pipeline_inputs:
- func: identity
frequency: 1
target_loggers:
- roboflow_logger