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import numpy as np
import tensorflow as tf
from PIL import Image
import numpy as np
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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