First version

clinical1.py

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+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()
+
+
+
+
+