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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) | |