From d35b0a2afc7e9faf89ede559cb1532bcd1bebaa0 Mon Sep 17 00:00:00 2001
From: Ivan Chekanov <78589729+ichekanov@users.noreply.github.com>
Date: Sat, 30 Nov 2024 15:35:22 +0300
Subject: [PATCH] :sparkles: Add webcam script
---
lab-2/result.ipynb | 11 ++++++-
lab-2/test.py | 75 ++++++++++++++++++++++++++++++++++++++++++++++
2 files changed, 85 insertions(+), 1 deletion(-)
create mode 100644 lab-2/test.py
diff --git a/lab-2/result.ipynb b/lab-2/result.ipynb
index fc2eeda..383738f 100644
--- a/lab-2/result.ipynb
+++ b/lab-2/result.ipynb
@@ -620,6 +620,15 @@
"model = YOLO(\"yolov8s.pt\")"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "WANDB_API_KEY = \"f2c04ec6cbd9f5e8f4c01a46514f80400716f594\""
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -709,7 +718,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.11"
+ "version": "3.12.7"
},
"widgets": {
"application/vnd.jupyter.widget-state+json": {
diff --git a/lab-2/test.py b/lab-2/test.py
new file mode 100644
index 0000000..2bb001b
--- /dev/null
+++ b/lab-2/test.py
@@ -0,0 +1,75 @@
+import cv2
+import numpy as np
+
+import time
+import sys
+
+from ultralytics import YOLO
+
+
+CONFIDENCE = 0.5
+font_scale = 1
+thickness = 1
+labels = ['kettle', 'teapot']
+colors = np.random.randint(0, 255, size=(len(labels), 3), dtype="uint8")
+
+model = YOLO("/Users/ischknv/Documents/GitHub/miem/aimm/lab-2/runs/detect/train/weights/best.pt")
+
+cap = cv2.VideoCapture(0)
+_, image = cap.read()
+h, w = image.shape[:2]
+fourcc = cv2.VideoWriter_fourcc(*"XVID")
+out = cv2.VideoWriter("output.avi", fourcc, 20.0, (w, h))
+while True:
+ _, image = cap.read()
+
+ start = time.perf_counter()
+ # run inference on the image
+ # see: https://docs.ultralytics.com/modes/predict/#arguments for full list of arguments
+ results = model.predict(image, conf=CONFIDENCE)[0]
+ time_took = time.perf_counter() - start
+ print("Time took:", time_took)
+
+ # loop over the detections
+ for data in results.boxes.data.tolist():
+ # get the bounding box coordinates, confidence, and class id
+ xmin, ymin, xmax, ymax, confidence, class_id = data
+ # converting the coordinates and the class id to integers
+ xmin = int(xmin)
+ ymin = int(ymin)
+ xmax = int(xmax)
+ ymax = int(ymax)
+ class_id = int(class_id)
+
+ # draw a bounding box rectangle and label on the image
+ color = [int(c) for c in colors[class_id]]
+ cv2.rectangle(image, (xmin, ymin), (xmax, ymax), color=color, thickness=thickness)
+ text = f"{labels[class_id]}: {confidence:.2f}"
+ # calculate text width & height to draw the transparent boxes as background of the text
+ (text_width, text_height) = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, fontScale=font_scale, thickness=thickness)[0]
+ text_offset_x = xmin
+ text_offset_y = ymin - 5
+ box_coords = ((text_offset_x, text_offset_y), (text_offset_x + text_width + 2, text_offset_y - text_height))
+ overlay = image.copy()
+ cv2.rectangle(overlay, box_coords[0], box_coords[1], color=color, thickness=cv2.FILLED)
+ # add opacity (transparency to the box)
+ image = cv2.addWeighted(overlay, 0.6, image, 0.4, 0)
+ # now put the text (label: confidence %)
+ cv2.putText(image, text, (xmin, ymin - 5), cv2.FONT_HERSHEY_SIMPLEX,
+ fontScale=font_scale, color=(0, 0, 0), thickness=thickness)
+
+ # end time to compute the fps
+ end = time.perf_counter()
+ # calculate the frame per second and draw it on the frame
+ fps = f"FPS: {1 / (end - start):.2f}"
+ cv2.putText(image, fps, (50, 50),
+ cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 255, 0), 6)
+ out.write(image)
+ cv2.imshow("image", image)
+
+ if ord("q") == cv2.waitKey(1):
+ break
+
+
+cap.release()
+cv2.destroyAllWindows()
\ No newline at end of file
--
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