Refactor Python Modules (#29)

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Alex 2022-02-20 21:17:36 -06:00 committed by GitHub
parent c894e36855
commit f181dba964
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11 changed files with 67 additions and 680 deletions

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@ -1,2 +1,3 @@
app/__pycache__
/devenv
__pycache__/
devenv/
app/upload

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[[source]]
url = "https://pypi.python.org/simple"
verify_ssl = true
name = "pypi"
[packages]
fastapi = "<0.69.0,>=0.68.0"
pydantic = "<2.0.0,>=1.8.0"
uvicorn = "<0.16.0,>=0.15.0"
tensorflow = "~=2.8.0"
numpy = "==1.22.2"
pillow = "==9.0.1"
[dev-packages]
[requires]
python_version = "3.8"

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@ -1,629 +0,0 @@
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"version": "==1.13.3"
},
"zipp": {
"hashes": [
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],
"version": "==3.7.0"
}
},
"develop": {}
}

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@ -0,0 +1,32 @@
from tensorflow.keras.applications import InceptionV3
from tensorflow.keras.applications.inception_v3 import preprocess_input, decode_predictions
from tensorflow.keras.preprocessing import image
import numpy as np
IMG_SIZE = 299
PREDICTION_MODEL = InceptionV3(weights='imagenet')
def classify_image(image_path: str):
img_path = f'./app/{image_path}'
img = image.load_img(img_path, target_size=(IMG_SIZE, IMG_SIZE))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
preds = PREDICTION_MODEL.predict(x)
result = decode_predictions(preds, top=3)[0]
payload = []
for _, value, _ in result:
payload.append(value)
return payload
def warm_up():
img_path = f'./app/test.png'
img = image.load_img(img_path, target_size=(IMG_SIZE, IMG_SIZE))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
PREDICTION_MODEL.predict(x)

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@ -1,27 +1,17 @@
from typing import Optional
from pydantic import BaseModel
import numpy as np
from fastapi import FastAPI
import tensorflow as tf
from tensorflow.keras.applications import InceptionV3
from tensorflow.keras.applications.inception_v3 import preprocess_input, decode_predictions
from tensorflow.keras.preprocessing import image
IMG_SIZE = 299
PREDICTION_MODEL = InceptionV3(weights='imagenet')
from .object_detection import object_detection
from .image_classifier import image_classifier
from tf2_yolov4.anchors import YOLOV4_ANCHORS
from tf2_yolov4.model import YOLOv4
def warm_up():
img_path = f'./app/test.png'
img = image.load_img(img_path, target_size=(IMG_SIZE, IMG_SIZE))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
PREDICTION_MODEL.predict(x)
HEIGHT, WIDTH = (640, 960)
# Warm up model
warm_up()
image_classifier.warm_up()
app = FastAPI()
@ -31,21 +21,26 @@ class TagImagePayload(BaseModel):
@app.post("/tagImage")
async def post_root(payload: TagImagePayload):
imagePath = payload.thumbnail_path
image_path = payload.thumbnail_path
if imagePath[0] == '.':
imagePath = imagePath[2:]
if image_path[0] == '.':
image_path = image_path[2:]
img_path = f'./app/{imagePath}'
img = image.load_img(img_path, target_size=(IMG_SIZE, IMG_SIZE))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
return image_classifier.classify_image(image_path=image_path)
preds = PREDICTION_MODEL.predict(x)
result = decode_predictions(preds, top=3)[0]
payload = []
for _, value, _ in result:
payload.append(value)
return payload
@app.get("/")
async def test():
object_detection.run_detection()
# image = tf.io.read_file("./app/cars.jpg")
# image = tf.image.decode_image(image)
# image = tf.image.resize(image, (HEIGHT, WIDTH))
# images = tf.expand_dims(image, axis=0) / 255.0
# model = YOLOv4(
# (HEIGHT, WIDTH, 3),
# 80,
# YOLOV4_ANCHORS,
# "darknet",
# )

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@ -0,0 +1,4 @@
def run_detection():
print("run detection")

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@ -3,4 +3,5 @@ pydantic>=1.8.0,<2.0.0
uvicorn>=0.15.0,<0.16.0
tensorflow==2.8.0
numpy==1.22.2
pillow==9.0.1
pillow==9.0.1
tf2_yolov4==0.1.0