add catboost params and EDA

notebooks/FastMTCNN.py

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+from facenet_pytorch import MTCNN
+import torch
+from imutils.video import FileVideoStream
+import cv2
+import time
+from tqdm.notebook import tqdm
+
+
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+
+class FastMTCNN(object):
+    """Fast MTCNN implementation."""
+
+    def __init__(self, stride, resize=1, *args, **kwargs):
+        """Constructor for FastMTCNN class.
+
+        Arguments:
+            stride (int): The detection stride. Faces will be detected every `stride` frames
+                and remembered for `stride-1` frames.
+
+        Keyword arguments:
+            resize (float): Fractional frame scaling. [default: {1}]
+            *args: Arguments to pass to the MTCNN constructor. See help(MTCNN).
+            **kwargs: Keyword arguments to pass to the MTCNN constructor. See help(MTCNN).
+        """
+        self.stride = stride
+        self.resize = resize
+        self.mtcnn = MTCNN(*args, **kwargs)
+
+    def __call__(self, frames):
+        """Detect faces in frames using strided MTCNN."""
+        if self.resize != 1:
+            frames = [
+                cv2.resize(f, (int(f.shape[1] * self.resize), int(f.shape[0] * self.resize)))
+                for f in frames
+            ]
+
+        boxes, probs = self.mtcnn.detect(frames[::self.stride])
+
+        faces = []
+        for i, frame in enumerate(frames):
+            box_ind = int(i / self.stride)
+            if boxes[box_ind] is None:
+                continue
+            for box in boxes[box_ind]:
+                box = [int(b) for b in box]
+                faces.append(frame[box[1]:box[3], box[0]:box[2]])
+                image = frame[box[1]:box[3], box[0]:box[2]]
+                cv2.imshow("test", cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
+                cv2.waitKey(10)
+
+        return faces
+
+
+fast_mtcnn = FastMTCNN(
+    stride=4,
+    resize=1,
+    margin=14,
+    factor=0.6,
+    keep_all=True,
+    device=device
+)
+def run_detection(fast_mtcnn, filenames):
+    frames = []
+    frames_processed = 0
+    faces_detected = 0
+    batch_size = 60
+    start = time.time()
+    if filenames == 0:
+        cap = cv2.VideoCapture(0)
+        while True:
+            _, frame = cap.read()
+            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+            frames.append(frame)
+            if len(frames) >= batch_size:
+                faces = fast_mtcnn(frames)
+                frames_processed += len(frames)
+                faces_detected += len(faces)
+                frames = []
+                print(
+                    f'Frames per second: {frames_processed / (time.time() - start):.3f},',
+                    f'faces detected: {faces_detected}\r',
+                    end=''
+                )
+
+    for filename in tqdm(filenames):
+
+        v_cap = FileVideoStream(filename).start()
+        v_len = int(v_cap.stream.get(cv2.CAP_PROP_FRAME_COUNT))
+
+        for j in range(v_len):
+
+            frame = v_cap.read()
+            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+            frames.append(frame)
+
+            if len(frames) >= batch_size or j == v_len - 1:
+
+                faces = fast_mtcnn(frames)
+
+                frames_processed += len(frames)
+                faces_detected += len(faces)
+                frames = []
+
+                print(
+                    f'Frames per second: {frames_processed / (time.time() - start):.3f},',
+                    f'faces detected: {faces_detected}\r',
+                    end=''
+                )
+
+        v_cap.stop()
+
+
+run_detection(fast_mtcnn, 0)
+
+