face recog fixed

pages/4_Face Recognition.py

@@ -7,6 +7,7 @@ import time
 import face_recognition
 import cv2
 import numpy as np
+from PIL import Image
 
 initialize_login()
 initialize_level()
@@ -14,18 +15,6 @@ initialize_level()
 LEVEL = 4
 
 
-def infer(image):
-    time.sleep(1)
-    output = query(image)
-    cols = st.columns(2)
-    cols[0].image(image, use_column_width=True)
-    with cols[1]:
-        for item in output:
-            st.progress(item["score"], text=item["label"])
-
-# Get a reference to webcam #0 (the default one)
-video_capture = cv2.VideoCapture(0)
-
 def step4_page():
     st.header("Face Recognition: Trying It Out")
     st.write(
@@ -50,51 +39,53 @@ def step4_page():
             known_face_encodings.append(known_face_encoding)
             known_face_names.append(face_name)
 
-    while True:
-            # Grab a single frame of video
-            ret, frame = video_capture.read()
-
-            # Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
-            rgb_frame = frame[:, :, ::-1]
-
-            # Find all the faces and face encodings in the frame of video
-            face_locations = face_recognition.face_locations(rgb_frame)
-            face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)
-
-            # Loop through each face in this frame of video
-            for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
-                # See if the face is a match for the known face(s)
-                matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
-
-                name = "Unknown"
-
-                # If a match was found in known_face_encodings, just use the first one.
-                # if True in matches:
-                #     first_match_index = matches.index(True)
-                #     name = known_face_names[first_match_index]
-
-                # Or instead, use the known face with the smallest distance to the new face
-                face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)
-                best_match_index = np.argmin(face_distances)
-                if matches[best_match_index]:
-                    name = known_face_names[best_match_index]
-
-                # Draw a box around the face
-                cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
-
-                # Draw a label with a name below the face
-                cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
-                font = cv2.FONT_HERSHEY_DUPLEX
-                cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)
-
-            # Display the resulting image
-            cv2.imshow('Video', frame)
-            # Hit 'q' on the keyboard to quit!
-            if cv2.waitKey(1) & 0xFF == ord('q'):
-                break
-    # Release handle to the webcam
-    video_capture.release()
-    cv2.destroyAllWindows()
+    st.info("Select an image to analyze!")
+    input_type = st.radio("Select the Input Type", ["Image", "Camera"])
+
+    if input_type == "Camera":
+        picture = st.camera_input("Take a picture")
+    else:
+        picture = st.file_uploader("Upload an image", type=["png", "jpg", "jpeg"])
+    if picture:
+        image = face_recognition.load_image_file(picture)
+        face_locations = face_recognition.face_locations(image)
+        face_encodings = face_recognition.face_encodings(image, face_locations)
+
+        # Loop through each face in this image
+        cols = st.columns(len(face_encodings))
+        i = 0
+        for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
+            # See if the face is a match for the known face(s)
+            matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
+
+            name = "Unknown"
+            # If a match was found in known_face_encodings, just use the first one.
+            if True in matches:
+                first_match_index = matches.index(True)
+                name = known_face_names[first_match_index]
+
+            face_image = image[top:bottom, left:right]
+            pil_image = Image.fromarray(face_image)
+            cols[i].image(pil_image, use_column_width=True)
+            cols[i].write("Person name: " +name)
+            i+=1
+
+    #             # Draw a box around the face
+    #             cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
+    #
+    #             # Draw a label with a name below the face
+    #             cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
+    #             font = cv2.FONT_HERSHEY_DUPLEX
+    #             cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)
+    #
+    #         # Display the resulting image
+    #         cv2.imshow('Video', frame)
+    #         # Hit 'q' on the keyboard to quit!
+    #         if cv2.waitKey(1) & 0xFF == ord('q'):
+    #             break
+    # # Release handle to the webcam
+    # video_capture.release()
+    # cv2.destroyAllWindows()
 
     st.info("Click on the button below to complete this level!")
     if st.button("Complete Level"):