Initial commit

OpenCV/tutoriel17/detecteur.py

@@ -0,0 +1,55 @@
+import os
+import sys
+import time
+import numpy as np
+import cv2
+
+cap=cv2.VideoCapture(0)
+#cap=cv2.VideoCapture("chien.mp4")
+
+kernel_blur=5
+seuil=15
+surface=1000
+ret, originale=cap.read()
+originale=cv2.cvtColor(originale, cv2.COLOR_BGR2GRAY)
+originale=cv2.GaussianBlur(originale, (kernel_blur, kernel_blur), 0)
+kernel_dilate=np.ones((5, 5), np.uint8)
+while True:
+    ret, frame=cap.read()
+    gray=cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+    gray=cv2.GaussianBlur(gray, (kernel_blur, kernel_blur), 0)
+    mask=cv2.absdiff(originale, gray)
+    mask=cv2.threshold(mask, seuil, 255, cv2.THRESH_BINARY)[1]
+    mask=cv2.dilate(mask, kernel_dilate, iterations=3)
+    contours, nada=cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    frame_contour=frame.copy()
+    for c in contours:
+        cv2.drawContours(frame_contour, [c], 0, (0, 255, 0), 5)
+        if cv2.contourArea(c)<surface:
+            continue
+        x, y, w, h=cv2.boundingRect(c)
+        cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 0, 255), 2)
+    originale=gray
+    cv2.putText(frame, "[o|l]seuil: {:d}  [p|m]blur: {:d}  [i|k]surface: {:d}".format(seuil, kernel_blur, surface), (10, 30), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 255, 255), 2)
+    cv2.imshow("frame", frame)
+    cv2.imshow("contour", frame_contour)
+    cv2.imshow("mask", mask)
+    intrus=0
+    key=cv2.waitKey(30)&0xFF
+    if key==ord('q'):
+        break
+    if key==ord('p'):
+        kernel_blur=min(43, kernel_blur+2)
+    if key==ord('m'):
+        kernel_blur=max(1, kernel_blur-2)
+    if key==ord('i'):
+        surface+=1000
+    if key==ord('k'):
+        surface=max(1000, surface-1000)
+    if key==ord('o'):
+        seuil=min(255, seuil+1)
+    if key==ord('l'):
+        seuil=max(1, seuil-1)
+
+cap.release()
+cv2.destroyAllWindows()