Add histogram equalization fcn

Dockerfile

@@ -39,13 +39,13 @@ COPY . /usr/src/app
 # sudo docker kill $(sudo docker ps -q)
 
 # Kill all image-based
-# sudo docker kill $(sudo docker ps -a -q --filter ancestor=ultralytics/yolov5:latest)
+# sudo docker kill $(sudo docker ps -qa --filter ancestor=ultralytics/yolov5:latest)
 
 # Bash into running container
 # sudo docker exec -it 5a9b5863d93d bash
 
 # Bash into stopped container
-# id=5a9b5863d93d && sudo docker start $id && sudo docker exec -it $id bash
+# id=$(sudo docker ps -qa) && sudo docker start $id && sudo docker exec -it $id bash
 
 # Send weights to GCP
 # python -c "from utils.general import *; strip_optimizer('runs/train/exp0_*/weights/best.pt', 'tmp.pt')" && gsutil cp tmp.pt gs://*.pt

models/yolo.py

@@ -107,7 +107,7 @@ def forward(self, x, augment=False, profile=False):
             for si, fi in zip(s, f):
                 xi = scale_img(x.flip(fi) if fi else x, si, gs=int(self.stride.max()))
                 yi = self.forward_once(xi)[0]  # forward
-                # cv2.imwrite('img%g.jpg' % s, 255 * xi[0].numpy().transpose((1, 2, 0))[:, :, ::-1])  # save
+                # cv2.imwrite(f'img_{si}.jpg', 255 * xi[0].cpu().numpy().transpose((1, 2, 0))[:, :, ::-1])  # save
                 yi[..., :4] /= si  # de-scale
                 if fi == 2:
                     yi[..., 1] = img_size[0] - yi[..., 1]  # de-flip ud

utils/datasets.py

@@ -631,10 +631,16 @@ def augment_hsv(img, hgain=0.5, sgain=0.5, vgain=0.5):
     img_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))).astype(dtype)
     cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=img)  # no return needed
 
-    # Histogram equalization
-    # if random.random() < 0.2:
-    #     for i in range(3):
-    #         img[:, :, i] = cv2.equalizeHist(img[:, :, i])
+
+def hist_equalize(img, clahe=True, bgr=False):
+    # Equalize histogram on BGR image 'img' with img.shape(n,m,3) and range 0-255
+    yuv = cv2.cvtColor(img, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV)
+    if clahe:
+        c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
+        yuv[:, :, 0] = c.apply(yuv[:, :, 0])
+    else:
+        yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :, 0])  # equalize Y channel histogram
+    return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB)  # convert YUV image to RGB
 
 
 def load_mosaic(self, index):