Merge branch 'main' of https://huggingface.co/spaces/zivpollak/ECX_V001

clinical1.py

@@ -1,102 +0,0 @@
-import gradio as gr
-import pandas as pd
-import cv2
-import mediapipe as mp
-import os
-from statistics import mean
-import numpy as np
-from mediapipe.tasks import python
-from mediapipe.tasks.python import vision
-from mediapipe.framework.formats import landmark_pb2
-from mediapipe import solutions
-
-
-import matplotlib
-matplotlib.use("Agg")
-import matplotlib.pyplot as plt
-
-cropped_image = []
-analyzed_image = []
-# take a phone
-# run face landmark on it to crop image
-# run our model on it
-# Display results
-
-# Create a FaceLandmarker object.
-base_options = python.BaseOptions(model_asset_path='face_landmarker_v2_with_blendshapes.task')
-options = vision.FaceLandmarkerOptions(base_options=base_options,
-                                       output_face_blendshapes=True,
-                                       output_facial_transformation_matrixes=True,
-                                       num_faces=1)
-detector = vision.FaceLandmarker.create_from_options(options)
-
-
-def video_identity(video):
-    return video
-
-
-#demo = gr.Interface(video_identity,
-#                    gr.Video(shape = (1000,1000), source="webcam"),
-#                    "playable_video")
-
-
-def handle_image(input_image):
-    global cropped_image, analyzed_image
-    cv2.imwrite("image.jpg", input_image) 
-    image = mp.Image.create_from_file("image.jpg")
-
-    detection_result = detector.detect(image)
-    cropped_image = image.numpy_view().copy()
-    analyzed_image = image.numpy_view().copy()
-
-    face_landmarks_list = detection_result.face_landmarks
-
-    # Draw the face landmarks.
-    face_landmarks = face_landmarks_list[0]
-    face_landmarks_proto = landmark_pb2.NormalizedLandmarkList()
-    face_landmarks_proto.landmark.extend([
-        landmark_pb2.NormalizedLandmark(x=landmark.x, y=landmark.y, z=landmark.z) for landmark in face_landmarks
-    ])
-
-    height, width, _ = cropped_image.shape
-    p1 = [int(face_landmarks_proto.landmark[70].x * width), int(face_landmarks_proto.landmark[70].y * height)]
-    cv2.circle(input_image, (p1[0], p1[1]), 10, (0, 0, 255), -1)
-    p2 = [int(face_landmarks_proto.landmark[346].x * width), int(face_landmarks_proto.landmark[346].y * height)]
-    cv2.circle(input_image, (p2[0], p2[1]), 10, (0, 0, 255), -1)
-    print(p1[0], p1[1], p2[0], p2[1], height, width)
-    cropped_image = cropped_image[p1[1]:p2[1], p1[0]:p2[0]]
-    # [row starting from the top]
-    #return ([input_image, cropped_image])
-    return (cropped_image)
-
-
-
-with gr.Blocks() as demo:
-            gr.Markdown(
-            """
-            # Iris detection
-            """)
-            #video1 = gr.Video(height=200, width=200)#source="webcam")
-
-            image1 = gr.Image()
-            b = gr.Button("Analyze")
-
-
-            gr.Markdown(
-                """
-                # Cropped image
-                """)
-            #cropped_image = gr.Gallery(
-            #    label="cropped", show_label=False, elem_id="cropped"
-            #)
-            cropped_image = gr.Image()
-
-            out = [cropped_image] 
-            b.click(fn=handle_image, inputs=image1, outputs=out)
-
-demo.launch()
-
-
-
-
-

requierments.txt

@@ -1,6 +0,0 @@
-
-numpy
-pandas
-Pillow
-opencv-python
-mediapipe