Spaces:

Kroy
/

face_blur

Runtime error

App Files Files Community

face_blur / app.py

Kroy

Upload 3 files

63b4b93 about 1 year ago

raw history blame contribute delete

No virus

5.02 kB

	from fastapi import FastAPI
	import os
	from typing import Any, Union,Dict, List
	import numpy as np
	import io
	import base64
	import requests

	import cv2
	from PIL import Image

	# Create a new FastAPI app instance
	app = FastAPI()

	# Initialize the text generation pipeline
	# This function will be able to generate text
	# given an input.
	prototxtPath = os.path.sep.join(["face_detector", "deploy.prototxt"])
	weightsPath = os.path.sep.join(["face_detector",
	"res10_300x300_ssd_iter_140000.caffemodel"])
	net = cv2.dnn.readNet(prototxtPath, weightsPath)

	# Define a function to handle the GET request at `/generate`
	# The generate() function is defined as a FastAPI route that takes a
	# string parameter called text. The function generates text based on the # input using the pipeline() object, and returns a JSON response
	# containing the generated text under the key "output"
	args = {
	"method": "simple",
	"blocks": 20,
	"confidence": 0.5
	}
	def anonymize_face_simple(image, factor=3.0):
	# automatically determine the size of the blurring kernel based
	# on the spatial dimensions of the input image
	(h, w) = image.shape[:2]
	kW = int(w / factor)
	kH = int(h / factor)

	# ensure the width of the kernel is odd
	if kW % 2 == 0:
	kW -= 1

	# ensure the height of the kernel is odd
	if kH % 2 == 0:
	kH -= 1

	# apply a Gaussian blur to the input image using our computed
	# kernel size
	return cv2.GaussianBlur(image, (kW, kH), 0)

	def anonymize_face_pixelate(image, blocks=3):
	# divide the input image into NxN blocks
	(h, w) = image.shape[:2]
	xSteps = np.linspace(0, w, blocks + 1, dtype="int")
	ySteps = np.linspace(0, h, blocks + 1, dtype="int")

	# loop over the blocks in both the x and y direction
	for i in range(1, len(ySteps)):
	for j in range(1, len(xSteps)):
	# compute the starting and ending (x, y)-coordinates
	# for the current block
	startX = xSteps[j - 1]
	startY = ySteps[i - 1]
	endX = xSteps[j]
	endY = ySteps[i]

	# extract the ROI using NumPy array slicing, compute the
	# mean of the ROI, and then draw a rectangle with the
	# mean RGB values over the ROI in the original image
	roi = image[startY:endY, startX:endX]
	(B, G, R) = [int(x) for x in cv2.mean(roi)[:3]]
	cv2.rectangle(image, (startX, startY), (endX, endY),
	(B, G, R), -1)

	# return the pixelated blurred image
	return image

	@app.get("/generate")
	def generate(path: str):
	"""
	Using the text summarization pipeline from `transformers`, summerize text
	from the given input text. The model used is `philschmid/bart-large-cnn-samsum`, which
	can be found [here](<https://huggingface.co/philschmid/bart-large-cnn-samsum>).
	"""
	r = requests.get(path, stream=True)
	img = Image.open(io.BytesIO(r.content)).convert('RGB')
	open_cv_image = np.array(img)
	# Convert RGB to BGR
	open_cv_image = open_cv_image[:, :, ::-1].copy() # numpy array (width, hight, 3)
	image = open_cv_image # numpy array (width, hight, 3)
	orig = image.copy()
	(h, w) = image.shape[:2]

	# construct a blob from the image
	blob = cv2.dnn.blobFromImage(image, 1.0, (300, 300),(104.0, 177.0, 123.0))

	# pass the blob through the network and obtain the face detections
	logger.info("computing face detections...")
	net.setInput(blob)
	detections = net.forward()

	# loop over the detections
	for i in range(0, detections.shape[2]):
	# extract the confidence (i.e., probability) associated with the
	# detection
	confidence = detections[0, 0, i, 2]

	# filter out weak detections by ensuring the confidence is greater
	# than the minimum confidence
	if confidence > args["confidence"]:
	# compute the (x, y)-coordinates of the bounding box for the
	# object
	box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
	(startX, startY, endX, endY) = box.astype("int")

	# extract the face ROI
	face = image[startY:endY, startX:endX]

	# check to see if we are applying the "simple" face blurring
	# method
	if args["method"] == "simple":
	face = anonymize_face_simple(face, factor=3.0)

	# otherwise, we must be applying the "pixelated" face
	# anonymization method
	else:
	face = anonymize_face_pixelate(face,blocks=args["blocks"])

	# store the blurred face in the output image
	image[startY:endY, startX:endX] = face

	image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
	img = Image.fromarray(image)

	im_file = io.BytesIO()
	img.save(im_file, format="PNG")
	im_bytes = base64.b64encode(im_file.getvalue()).decode("utf-8")

	# Return the generated text in a JSON response
	return {"output": im_bytes}