updated face recognition; added support for ipykernel and ipywidgets; included .gitignore

.gitignore

@@ -0,0 +1,2 @@
+FER/models/__pycache__/
+FER/__pycache__

FER/detectfaces.py

@@ -1,9 +1,18 @@
-from main import *
+from models.PosterV2_7cls import pyramid_trans_expr2
 import cv2
+import torch
+import os
 import time
+from PIL import Image
 
-model_path = "raf-db-model_best.pth"
+# Define the path to the model checkpoint
+# Get the directory of the current file (models/PosterV2_7cls.py)
+script_dir = os.path.dirname(os.path.abspath(__file__))
 
+# Construct the full path to the model file
+model_path = os.path.join(script_dir, r"models\checkpoints\raf-db-model_best.pth")
+
+# Determine the available device for model execution
 if torch.backends.mps.is_available():
     device = "mps"
 elif torch.cuda.is_available():
@@ -11,15 +20,22 @@ elif torch.cuda.is_available():
 else:
     device = "cpu"
 
+# Initialize the model with specified image size and number of classes
 model = pyramid_trans_expr2(img_size=224, num_classes=7)
 
+# Wrap the model with DataParallel for potential multi-GPU usage
 model = torch.nn.DataParallel(model)
+
+# Move the model to the chosen device
 model = model.to(device)
+
+# Print the current time
 currtime = time.strftime("%H:%M:%S")
 print(currtime)
 
 
 def main():
+    # Load the model checkpoint if it exists
     if model_path is not None:
         if os.path.isfile(model_path):
             print("=> loading checkpoint '{}'".format(model_path))
@@ -35,68 +51,80 @@ def main():
             )
         else:
             print("=> no checkpoint found at '{}'".format(model_path))
+        # Start webcam capture and prediction
         imagecapture(model)
         return
 
 
 def imagecapture(model):
-    currtimeimg = time.strftime("%H:%M:%S")
+    # Initialize webcam capture
     cap = cv2.VideoCapture(0)
-    if not cap.isOpened():
-        print("Error: Could not open webcam.")
-        exit()
+    time.sleep(5)  # Wait for 5 seconds to allow the camera to initialize
 
-    face_cascade = cv2.CascadeClassifier(
-        cv2.data.haarcascades + "haarcascade_frontalface_default.xml"
-    )
+    # Keep trying to open the webcam until successful
+    while not cap.isOpened():
+        time.sleep(2)  # Wait for 2 seconds before retrying
 
-    start_time = None
-    capturing = False
-
-    while True:
+    # Flag to control webcam capture
+    capturing = True
+    while capturing:
+        # Import the predict function from the prediction module
         from prediction import predict
 
+        # Read a frame from the webcam
         ret, frame = cap.read()
 
+        # Handle potential error reading the frame
         if not ret:
             print("Error: Could not read frame.")
             break
 
+        # Convert the frame to grayscale
         gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
 
-        faces = face_cascade.detectMultiScale(
-            gray, scaleFactor=1.3, minNeighbors=5, minSize=(30, 30)
-        )
+        # Detect faces using Haar Cascades
+        faces = cv2.CascadeClassifier(
+            cv2.data.haarcascades + "haarcascade_frontalface_default.xml"
+        ).detectMultiScale(gray, scaleFactor=1.3, minNeighbors=5, minSize=(30, 30))
 
-        # Display the frame
+        # Display the current frame
         cv2.imshow("Webcam", frame)
 
-        # If faces are detected, start the timer
+        # If faces are detected, proceed with prediction
         if len(faces) > 0:
+            currtimeimg = time.strftime("%H:%M:%S:%.*f")
             print(f"[!]Face detected at {currtimeimg}")
+            # Crop the face region
             face_region = frame[
                 faces[0][1] : faces[0][1] + faces[0][3],
                 faces[0][0] : faces[0][0] + faces[0][2],
-            ]  # Crop the face region
+            ]
+            # Convert the face region to a PIL image
             face_pil_image = Image.fromarray(
                 cv2.cvtColor(face_region, cv2.COLOR_BGR2RGB)
-            )  # Convert to PIL image
+            )
             print("[!]Start Expressions")
-            print(f"-->Prediction starting at {currtimeimg}")
-            predictions = predict(model, image_path=face_pil_image)
-            print(f"-->Done prediction at {currtimeimg}")
-
-            # Reset capturing
+            # Record the prediction start time
+            starttime = time.strftime("%H:%M:%S:%.*f")
+            print(f"-->Prediction starting at {starttime}")
+            # Perform emotion prediction
+            predict(model, image_path=face_pil_image)
+            # Record the prediction end time
+            endtime = time.strftime("%H:%M:%S:%.*f")
+            print(f"-->Done prediction at {endtime}")
+
+            # Stop capturing once prediction is complete
             capturing = False
 
-        # Break the loop if the 'q' key is pressed
+        # Exit the loop if the 'q' key is pressed
         if cv2.waitKey(1) & 0xFF == ord("q"):
             break
 
-    # Release the webcam and close the OpenCV window
+    # Release webcam resources and close OpenCV windows
     cap.release()
     cv2.destroyAllWindows()
 
 
+# Execute the main function if the script is run directly
 if __name__ == "__main__":
     main()

FER/face_detection.py

@@ -1,13 +1,13 @@
 from deepface import DeepFace
-import matplotlib.pyplot as plt
-from PIL import Image
 import numpy as np
 import time
 
 
 def face_detection(img_path):
-    currtime = time.strftime("%H:%M:%S")
-    face_objs = DeepFace.extract_faces(np.array(img_path), detector_backend="mtcnn", enforce_detection=False)
+    currtime = time.strftime("%H%M%S")
+    face_objs = DeepFace.extract_faces(
+        np.array(img_path), detector_backend="opencv", enforce_detection=False
+    )
 
     coordinates = face_objs[0]["facial_area"]
     image = img_path
@@ -19,5 +19,5 @@ def face_detection(img_path):
             coordinates["y"] + coordinates["h"],
         )
     )
-    cropped_image.save(f"Images/test_{currtime}.jpg")
+    cropped_image.save(f"FER/Images/test_{currtime}.jpg")
     return cropped_image

FER/models/PosterV2_7cls.py

@@ -302,10 +302,14 @@ class pyramid_trans_expr2(nn.Module):
         self.N = [win * win for win in window_size]
         self.face_landback = MobileFaceNet([112, 112], 136)
 
+        # Get the directory of the current file (models/PosterV2_7cls.py)
+        script_dir = os.path.dirname(os.path.abspath(__file__))
+
+        # Construct the full path to the model file
         mobilefacenet_path = os.path.join(
-            os.getcwd(), "models/pretrain/mobilefacenet_model_best.pth.tar"
+            script_dir, "pretrain", "mobilefacenet_model_best.pth.tar"
         )
-        ir50_path = os.path.join(os.getcwd(), "models/pretrain/ir50.pth")
+        ir50_path = os.path.join(script_dir, r"pretrain\ir50.pth")
 
         print(mobilefacenet_path)
         face_landback_checkpoint = torch.load(

FER/prediction.py

@@ -1,7 +1,6 @@
 from models.PosterV2_7cls import pyramid_trans_expr2
 import torch
 import os
-from deepface import DeepFace
 from torchvision import transforms
 import numpy as np
 
@@ -21,11 +20,13 @@ model = pyramid_trans_expr2(img_size=224, num_classes=7)
 model = torch.nn.DataParallel(model)
 model = model.to(device)
 
-model_path = "raf-db-model_best.pth"
+script_dir = os.path.dirname(os.path.abspath(__file__))
+
+# Construct the full path to the model file
+model_path = os.path.join(script_dir, r"models\checkpoints\raf-db-model_best.pth")
+
 image_arr = []
-for foldername, subfolders, filenames in os.walk(
-    "/Users/futuregadgetlab/Downloads/Testing/"
-):
+for foldername, subfolders, filenames in os.walk("../FER/Images/"):
     for filename in filenames:
         # Construct the full path to the file
         file_path = os.path.join(foldername, filename)

pyproject.toml

@@ -41,6 +41,8 @@ dependencies = [
     "h5py==3.12.1",
     "huggingface-hub==0.26.2",
     "idna==3.10",
+    "ipykernel>=6.29.5",
+    "ipywidgets>=8.1.5",
     "itsdangerous==2.2.0",
     "jinja2==3.1.4",
     "joblib==1.4.2",
@@ -109,6 +111,7 @@ dependencies = [
     "tensorflow-io-gcs-filesystem==0.31.0 ; python_full_version < '3.12'",
     "tensorflow==2.18.0",
     "termcolor==2.5.0",
+    "tf-keras>=2.18.0",
     "threadpoolctl==3.5.0",
     "tokenizers==0.20.3",
     "torch==2.5.1+cu124",

uv.lock

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 version = "1.6.3"
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