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from email.headerregistry import Group
import numpy as np
from psutil import getloadavg
import tensorflow as tf
import logging
from rich.logging import RichHandler
from PIL import Image
import numpy as np
from config import get_config
from deepface import DeepFace
import matplotlib.pyplot as plt
from imutils import url_to_image
import cv2
import gradio as gr
# # --------------------------------------------------------------------------
# # global variables
# # --------------------------------------------------------------------------
REFERENCE = None
QUERY = None
interpreter = tf.lite.Interpreter(model_path="IR/model_float16_quant.tflite")
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
ref = None
query = None
face_query_ = None
face_ref_ = None
# demo = gr.Interface(
# fn=image_classifier,
# inputs=[
# gr.Image(shape=(224, 224),
# image_mode='RGB',
# source='webcam',
# type="numpy",
# label="Reference Image",
# streaming=True,
# mirror_webcam=True),
# gr.Image(shape=(224, 224),
# image_mode='RGB',
# source='webcam',
# type="numpy",
# label="Query Image",
# streaming=True,
# mirror_webcam=True)
# ],
# outputs=[
# gr.Number(label="Cosine Similarity",
# precision=5),
# gr.Plot(label="Reference Embedding Histogram",
# ),
# gr.Plot(label="Query Embedding Histogram",
# )
# ],
# live=False,
# title="Face Recognition",
# # description='''
# # | feature | description |
# # | :-----:| :------------: |
# # | model | mobile-facenet |
# # | precision | fp16 |
# # |type | tflite|
# # ''',
# # article="""
# # - detects face in input image
# # - resizes face to 112x112
# # - aligns the face using **deepface MTCNN**
# # - runs inference on the aligned face
# # """,
# allow_flagging="auto",
# analytics_enabled=True,
# )
# demo.launch(inbrowser=True, auth=("talha", "123"))
def plot_images():
global face_query_, face_ref_
return face_ref_, face_query_
def predict(interpreter, input_details, input_data, output_details):
interpreter.set_tensor(input_details[0]['index'], input_data)
interpreter.invoke()
output_data = interpreter.get_tensor(output_details[0]['index'])
return output_data
def get_ref_vector(image):
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
cv2.imwrite("ref.jpg", image)
global face_ref_
face_ref_ = DeepFace.detectFace("ref.jpg", detector_backend="opencv",
align=True, target_size=(112, 112))
face_ref = face_ref_.copy()
# face_ is [0, 1] fp32 , needs to be changed to [0, 255] uint8
face_ref = cv2.normalize(face_ref, None, 0, 255,
cv2.NORM_MINMAX, cv2.CV_32FC3)
print(
f"dtype {face_ref.dtype} || max {np.max(face_ref)} || min {np.min(face_ref)}")
# calculate embeddings
face_ref = face_ref[np.newaxis, ...] # [1, 112, 112, 3]
output_data_ref = predict(interpreter, input_details,
face_ref, output_details)
global ref
ref = output_data_ref
return str(f"shape ---> {face_ref_.shape} dtype ---> {face_ref_.dtype} max {face_ref_.max()} min {face_ref_.min()}"), ref
def get_query_vector(image1):
image1 = cv2.cvtColor(image1, cv2.COLOR_BGR2RGB)
cv2.imwrite("query.jpg", image1)
global face_query_
face_query_ = DeepFace.detectFace("query.jpg", detector_backend="opencv",
align=True, target_size=(112, 112))
face_query = face_query_.copy()
# face_ is [0, 1] fp32 , needs to be changed to [0, 255] uint8
face_query = cv2.normalize(
face_query, None, 0, 255, cv2.NORM_MINMAX, cv2.CV_32FC3)
print(
f"dtype {face_query.dtype} || max {np.max(face_query)} || min {np.min(face_query)}")
# calculate embeddings
face_query = face_query[np.newaxis, ...] # [1, 112, 112, 3]
output_data_query = predict(interpreter, input_details,
face_query, output_details)
global query
query = output_data_query
return str(f"shape ---> {face_query_.shape} dtype ---> {face_query_.dtype} max {face_query_.max()} min {face_query_.min()}"), query
def get_metrics():
global ref, query
return float(np.dot(np.squeeze(ref), np.squeeze(query))), float(np.linalg.norm(np.squeeze(ref) - np.squeeze(query)))
with gr.Blocks(analytics_enabled=True, title="Face Recognition") as demo:
# draw a box around children
with gr.Box():
gr.Markdown(
"# First provide the *reference* image and then the *query* image. The **cosine similarity** will be displayed as output.")
# put both cameras under separate groups
with gr.Group():
# components under this scope will have no padding or margin between them
with gr.Row():
# reference image
with gr.Column():
inp_ref = gr.Image(shape=(224, 224),
image_mode='RGB',
source='webcam',
type="numpy",
label="Reference Image",
streaming=True,
mirror_webcam=True),
out_ref = [gr.Textbox(label="Face capture details"),
gr.Dataframe(label="Embedding",
type="pandas", max_cols=512,
headers=None),
]
# make button on left column
btn_ref = gr.Button("reference_image")
btn_ref.click(fn=get_ref_vector,
inputs=inp_ref, outputs=out_ref)
with gr.Column():
inp_query = gr.Image(shape=(224, 224),
image_mode='RGB',
source='webcam',
type="numpy",
label="Query Image",
streaming=True,
mirror_webcam=True),
out_query = [gr.Textbox(label="Face capture details"),
gr.Dataframe(label="Embedding",
type="pandas", max_cols=512,
headers=None),
]
# make button on right column
btn_query = gr.Button("query_image")
btn_query.click(fn=get_query_vector,
inputs=inp_query, outputs=out_query)
with gr.Box():
gr.Markdown("# Metrics")
with gr.Group():
gr.Markdown(
"The **cosine similarity** and **l2 norm of diff.** will be displayed as output here")
with gr.Row():
out_sim = gr.Number(label="Cosine Similarity", precision=5)
out_d = gr.Number(label="L2 norm distance", precision=5)
# make button in center, outside row
btn_sim = gr.Button("Calculate Metrics")
btn_sim.click(fn=get_metrics, inputs=[], outputs=[out_sim, out_d])
with gr.Box():
with gr.Group():
gr.Markdown("# detected face results are shown below")
with gr.Row():
out_faces = [
gr.Image(shape=(60, 60), label="Detected Face Reference"),
gr.Image(shape=(112, 112), label="Detected Face Query")
]
# make button inside row
# make button outside (below) row
button_show = gr.Button("Show detected faces")
button_show.click(fn=plot_images, inputs=[], outputs=out_faces)
demo.launch(share=True)
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